Discriminating Systems - Gender, Race, and Power in AI
ai machine learning bias fairness ml fairness algorithmic bias algorithmic discrimination ai and society ainow google microsoft race gender stem pipeline gender gap diversity inclusion equity power
TL;DR:
- There exists both an unequal representation of people in the AI workforce as well as examples of societal bias in AI systems.
- The authors claim that the former causally leads to the latter and vice versa.
- To me, the report does not manage to make a strong enough argument for that claim.
- I find the statements made quite dishonest at times.

https://ainowinstitute.org/discriminatingsystems.pdf

Authors:
Sarah Myers West, Meredith Whittaker, Kate Crawford
 Hi there, today we're looking at discriminating systems, gender, race and power in AI by Sarah Myers-West, Meredith Whitaker and Kate Crawford of the AI Now Institute, which is a part of New York University or associated with it. This is not as much a paper as it is a report, kind of summarizing current literature and also kind of an opinion piece slash recommendation giving document. Yes, so we'll dive into it. As you can see from the index, it's quite a long report and we don't have time to go into all of it. Actually, we don't have time to go into most of it. I just hope to kind of point out what the main arguments and themes are in the report, kind of what it's trying to say, pick out some interesting things and summarize it to the best of my ability. Also give a little critique. So let me actually go ahead and try to state the kind of core argument that the report is trying to make, because it's not really clear from reading it and you have to kind of read the whole thing and then kind of becomes clear what the argument is, I feel, though they somehow stated in the introduction numerous times in various ways. So I might just be not as attentive reader at first time. But all right, so here's the argument and I really hope I'm representing this correctly. We have a problem currently that sometimes AI systems can exhibit what we usually call bias. And we don't mean mathematical bias, like bias variance tradeoff. We mean bias in a societal sense, let's say bias against certain types of people where they shouldn't exist. So for example, let me draw an AI system and I'll just draw a little computer screen with a little light bulb. All right. So this is because it's smart, this is an AI system and the AI system and they give numerous examples. One example they give us for is like face recognition algorithm that is much more accurate on faces of white males, as opposed to darker skinned females. So let me draw like two curves to represent these distributions are unequal. And so the AI system exhibits some bias with respect to some kinds of people with an especially protected attributes. And in this report, they focus mainly on gender and race. So that's what we're going to talk about. The second thing they observe, so this observation one, the second thing they observe is, I'm going to draw some generic people here that represent the workforce of AI. So the AI workforce is classified as all the people that work on AI, be that university researchers or within companies building AI products or deploying them. So this is the workforce and they observe that there is an unequal distribution among the AI workforce. So this distribution, I'm also going to do this for unequal distribution. There's an unequal distribution in the AI workforce, most notably, it's predominantly males who work on AI. And also white people are overrepresented compared to the world population at large. So that's kind of the two observations they make. And now what they claim is that the unequal representation in the workforce is causing the bias in the AI systems. So they're basically saying these AI systems are biased because that the workforce is unequally distributed. And also they claim in a less powerful sense, I feel, but they claim there is a loop that this then leads back that because there is bias in the AI system, that again leads to an unequal, more unequal distribution of the workforce. So the core argument really is, as they set out to do, like in the introduction, and also claim that they have done in the conclusion, is to demonstrate these two directions here in a causal way. So the systems are biased because there is an unequal representation in the workforce and that feeds back. So the argument is that if you want to fix the bias here, if you want to fix that, then you will have to fix it via making the workforce more what they call diverse, so less unilaterally distributed towards white males. That's kind of the final conclusion. If you read their report and the recommendations, that's mainly what they're going for. Yeah, so my opinion, or in my opinion, having read the report a couple of times, is that as I see it, they really don't demonstrate these links. So they give examples of this and they give examples of this. They show that the workforce is unequally distributed. They show that AI systems can exhibit such bias, but they never actually show these links in my opinion. They don't show this. So if you make the claim that in order to fix the bias in AI systems, you must fix the unequal representation in the workforce, I would need an argument that says because there is unequal representation, therefore A, therefore B, therefore C, therefore bias, like an actual argument to follow that says because of this, that, because of that, that, and so on. It's just not there. They simply show parallels. They simply show that these two things exist and they just list example after example of that. I don't think they make this argument. But I think, also the other direction, they don't really make this argument. Except in one case, where if you give them benefit of the doubt. What I also think is that it appears like the article, if you read it, and I encourage you to read it if you have some time, it makes a lot of sense if you have already accepted this conclusion. Like if you've already accepted this, then it's like, oh yeah, because I feel this is just a text where the confirmation bias is so high, just the way it's written, that it must make a lot of sense to someone who's already kind of in on this conclusion. But to someone who isn't sold yet, like myself, I am just not finding this convincing at all. The second thing is that it very much feels like this isn't like a discovery or something. But someone actually set out with the goal to address this here with the goal of I want companies to hire more of these people or certain kinds of people or to become more diverse or to promote more of a certain type of people. And now I'm going to find reasons for this. And the reason is like, oh, look at look at this bias here. This is caused. This is caused by this other thing. And therefore we must fix this other thing. It very much feels like someone setting out with already the conclusion in mind rather than this being an honest investigation. But yeah, I mean, read it for yourself. I can't prove the absence of an argument by not reading every single line. And I can't read every single line because it'll just get very long and boring. But read it yourself. And I think I'm pretty I'm pretty I've read it numerous times with really an open mind to be convinced that there is an argument in there. But I don't think there is or I don't think there is a very strong argument for this. All right. Let this first part here is more or less a summary. So research findings is more or less a summary. And we'll get to these things as they are important. Then they state recommendations right at the beginning. So actually, you'd have to read the article first. This is kind of more of an abstract section. But since it's right here, we'll kind of jump right into it. So these are recommendations and I've claimed they don't really show a connection. But they actually just show examples, examples of this and examples of this and parallel them. And this is reflected in like every single section, including here in the recommendations. They have recommendations for improving workplace diversity. And they have recommendations for addressing bias and discrimination in AI systems. Right. So all right, in my case, if you make this argument, I would I would feel you also make recommendations for breaking these links. But or argue why they can't be broken. But all right, let's jump into some of them. And it is really a mixed bag here, really. So some recommendations I'm really in favor of just from from the go not even you don't even need the article for those here. Discrimination, harassment and discrimination, transparency reports, including number of claims over time, the types of claims submitted and actions taken. So it's known that especially in these larger companies, sexual harassment claims often go down in either bureaucracy or are kind of hushed under the table or something like this. What you have to recognize is that a human resource department of a large company isn't there to serve the human resources. It's there to serve the company providing human resources. That's why a sexual harassment claim to an HR department is just a potential lawsuit. And that's why they don't want to take it seriously except for it must go away really quickly. So I think to kind of force companies or to ask companies to be more transparent, to take more seriously these the accusations of sexual harassment and assault and also discrimination is a very valuable goal. And I fully, fully support this. Also the here commit to transparency around hiring practices, especially hiring regarding how candidates are leveled, compensated and promoted. But also the larger the company gets, the less transparent this process usually becomes or the more bureaucratic, the more people are able to game it and so on and distort it. So I feel it's always good to be transparent around, okay, this person provides this much value to the company, therefore they should be compensated according to that or at least be transparent about it. So these are kind of recommendations I like. Then recommendations that really go into a different direction is something like this here, change hiring practices to maximize diversity. And this is kind of reflect, I'm not going to go on this reflected in other points, increase the number of people of color, women and other underrepresented groups at senior leadership levels of AI companies across all departments. So these things, they are usually within like company diversity goals and so on, doesn't really say how to do it. But then the I mean, as such, they're not really recommendations yet. They're more like goals. But here recommendation seven, I think is the the crucial one, ensure executive incentive structures are tied to increases in hiring and retention of underrepresented groups. So this is it's a bit of coded language. But here they talk about executive incentive structure tied to hiring and retention of underrepresented groups. This basically means if you are a manager or someone in charge of hiring or promoting, and you hire or promote a underrepresented person, and since they're talking about gender and race here, if you that means if you hire or promote a person of color or a woman, in this case, you will be compensated more. So at the end of the year, you'll somehow have more money, like more bonuses or more base comp or more equity or something like you'll get more money. So this, this recommendation is a direct call to hire based on race and gender. So this, this is a direct call to racist and sexist hiring basically to discriminate people according to their skin color and according to their gender, which I mean, how, how is this okay with anyone? Like how can anyone how are people even able to state this and in like a high profile report like this and get away with it and not have people criticize them, this directly calls for people to be treated according to their gender and race. And probably as directly as you can go without getting into actual legal trouble. But yeah, I'm really, really against such such practices. I mean, yeah, that's I just I just don't know how this how this can ever how this can ever be thought of as a good thing by anyone. All right, so, well, yeah, in my mind, this recommendation, and this recommendation kind of are counter to each other. Because if if I commit to transparency, how people are okay now I can, I can transparently commit to to be racist, I guess. But if I say, okay, I'm going to come and promote people based on how much value they provide to the company, then yeah, I'd much rather have that than saying I'm going to come and promote people based on their skin color. Alright, so let's actually jump into the report. I'm not gonna these recommendations for addressing bias and discrimination in systems this these are fairly general and common. So as well, as I said, we'll jump most of the things in the report. So introduction. So they start out with there is a diversity crisis in the AI industry. This they give like some numbers like 15% of AI research staff and 10% at Google, so 15% of Facebook are women. So these are some kind of fairly known statistics about how the AI field is kind of gender and race skewed. Currently, so they say they claim in bold the diversity problem is not just about women. It's about gender, race, and most fundamentally about power. It affects how companies work, what products get built, who they're designed to serve, and who benefits from their development. So this, I find this, this, this word power and this notion of power, a lot in this report, it appears again and again and again in in like power dynamics and power dynamics among groups. It's like a worldview, it paints like a worldview, where these different gender and race groups kind of struggle against each other to gain power over another. And whoever's in power will try to remain in power in alliance with their gender and race group and try to keep the other groups down. I'm not sure that's the correct view of the world. In my mind, the world is comprised of individual people that want to achieve something for themselves and they would like to prop themselves up. Whereas in this worldview, it's like, I'm going to use the power of my group to keep other groups down. I don't know which worldview you subscribe to, but I find the world is comprised of individuals. Yeah, and this is not discrediting that some people have it harder because of their gender or race. But to see the entire world as a power struggle between these groups, to me, it's, it's, yeah, and I'm not going to point out everywhere it appears, this power wording, but it appears a lot and it's really shapes how the report reads. You have to, you have to kind of remember, if you're a white male, and currently, the field is comprised of 90% white males, you, if you have like 10, like 10 hours, let's say you have to have 10 hours to do something, right, you can either choose to put down some other groups, like put down groups that you're not part of, or you can choose to invest these 10 hours in putting up yourself, you, right. So if, if I, like I profit, if I'm a white male, I profit minimally from keeping the other groups down because guess what, I still have to compete with the like 1 billion other white males there are. It's not going to help me to keep down anyone else, and especially, like it's, it's moronic, like who does that, who like has alliance, except most fringe people, like to their race or gender, rather than to the people they admire and respect and like to work with. So I'm going to, if I have like 10 hours today, I'm going to rather spend this in propping up myself compared to everyone else, and I don't care what gender or race they are. And so that to me, that's a much more accurate or, I don't know, plausible worldview. But just be aware that this report really takes on the language of kind of groups and power between groups and groups trying to, you know, kind of gain power and keep in, keep power and keep others from having power. All right, so say, to date, the diversity problems of the industry and the issues of bias in the systems it builds have tended to be considered separately. We suggest that these are two versions of the same problem. Issues of discrimination in the workforce and in system buildings are deeply intertwined. Challenge, and moreover, tackling the challenges of bias within technical systems requires addressing workforce diversity and vice versa. So the, I think this, this here actually is like how I described the argument and they kind of restated multiple times in a bit different way. But I think this is the core. And I really think I'm not misrepresenting the article here in that this is what they are setting out to do. They're setting out to say, okay, the diversity, the kind of unequal representation in the workforce and the bias in some AI systems are causally linked to each other and tackling one requires tackling the other. So yeah, if I'm misrepresenting them, let me know, but I really think I'm accurately representing their argument. So what they, what they do, as I said, is they give examples of one and of the other and also they really, they're really on kind of discrediting the kind of issues to solve problems of bias in a different way. So they point a little bit to this here in the introduction. They say in the face of growing evidence, the AI research community and the industry producing our products have begun addressing the problem of bias by building on a body of work of fairness, accountability and transparency. So fairness, accountability and transparency research concerns these issues. For one is research showing that some products are unfair or untransparent and so on. On the other hand, it's trying to devise algorithms that are more fair according to some notions or more accountable and transparent, which means that the algorithm can kind of say why it made a certain decision rather than it being a deep learning system that you don't really have an insight. These fields are active fields of research, definitely very interesting to look into. So but they, they kind of, it is not already here, but they say, yeah, we have adjusting AI systems that produce a result deemed fair by one of various mathematical definitions. You can already see in the language here, they don't really like this research and they are trying in this report to kind of discredit it or at least claim that it doesn't solve the whole problem because their point is, of course, you have to address this diversity issue in the workforce in order to fix the problems. So to this, I just want to say no, like if you can, I mean, you can criticize the fairness and accountability and transparency research field in that they haven't solved the problem fully yet. But in principle, if I have an algorithm, if I'm being delivered an algorithm, right, and the fairness literature has been applied to that algorithm and someone tells me, I guarantee you here is a proof, the algorithm is fair, right, then I really don't care who made that algorithm. As long as it's fair, the problem is fixed. If the bias is gone, the problem is fixed. And I don't care who fix it. I don't care if the person who fixed it is black or white or purple. Then the problem is fixed. And they, they really have to, they really try to just make the counter argument here is that no, that's it's not enough. But I claim yes, it, if you can actually solve the fairness problem, technically, then you have solved the fairness problem. Yeah, the only thing you can do is claim that it is not good enough yet, but not that it's fun to they kind of have to make the argument that it's fundamentally flawed approach. And I don't think they succeed in doing that here. Um, yeah, so they go on to say, we should expand to consider not only how I tools can be biased technically, but how they're shaped by the environments in which you're built in and the people that built them. Again, this this focus like who builds the AI system, I don't care, I care what it does, right? As much as if, if I hear an argument for or against something, I don't care who makes the argument, right? I care what the argument says. This is, it's like an ad hominem attack for an entire community. That's kind of how this this article, this report shows, or is appears to me. So they say, currently, large scale AI systems are developed almost exclusively in a handful of technology companies and a small set of elite university laboratories spaces that in the West tend to be extremely white, affluent, technically oriented and male. So yeah, their their problem, that's their fundamental problem here that these these spaces are skewed in one direction. Interestingly enough, their problem is not so much that it's that they're all in the same place, right? That they all live like 20 miles from each other in around San Francisco. That's that seems to be not a problem at all, as long as we get to like enough people of color and women into these 20 miles. But yeah, so that that's pointing out the the problem here or the yeah, kind of issue they have. All right, so they go on. Just kind of want to highlight again, they say both within the spaces where AI is being created and the logic of how AI systems are being designed. So paralleling the two things, the cost of bias, harassment and discrimination are born by the same people, gender minorities, people of color, other underrepresented groups. And they also say similarly, the benefits of such systems from profit to efficiency, accrue primarily to those are already in positions of power tend to be white, educated and male. So they again, they say the this points to a systematic relationship between patterns of exclusion within the field of AI and the industry driving its production on the one hand and the biases that manifest in the logics and applications of the technologies on the other. And they try to make this connection because they say the cost and the benefit of these two things are overlap in the people that where it costs and it benefits. And I really, again, it's just a parallel, but I really even don't think that's true because they kind of, they kind of argue against themselves later. So they always say, we have to look at again, they shoot against the take much more than the technically driven problem solving. They point to this. So our research requires looking at gender and racist categories within which humans think in short, sorry, studies of discriminatory systems, we need to ask who is harmed, who benefits, who gets to decide. So it's kind of who bears the cost, who bears the benefits and who has the power. So that's the, and again, it's we seek to understand how AI disadvantages some, we also consider how it works to the advantage of others. So keep that in mind. That's kind of the lens through how they analyze the this thing again, one that acknowledges power relationships and centers equity and justice. That's the, they want to see this bigger picture. So that's yeah, keep, again, keep that in mind. So they go into a section called which humans are in the loop, how workforces and AI systems interact. So this kind of from the title of this section, you think, okay, here's where we get in. Here's where we make the argument. And they start by listing examples of how AI systems can be discriminatory. And first, they go into an example of Amazon had developed an experimental hiring tool to help rank job candidates. By learning from its past reference preferences, Amazon hoped that the resume scanning tool will be able to efficiently identify qualified applicants, comparing their applications to previous hires. The system quickly began to downgrade resumes from candidates who attended all women's colleges along with any resumes that included the word women's. After uncovering this bias, Amazon engineers tried to fix the problem by directing the system to treat these terms in a neutral manner. The company eventually abandoned the tool when they were unable to ensure that the algorithm would not be biased against women. Gender based discrimination was built too deeply within the system and in Amazon's past hiring practices to be uprooted using a purely technical approach. So this just the way is written, I find to be quite dishonest. But let's analyze what happened here. So their final claim is that gender based discrimination was built too deeply within the system to be uprooted using a purely technical approach. So this is one of their arguments. They say technical approaches, they don't help because the Amazon engineers tried to fix the problem. But when they were unable to ensure that the algorithm would not be biased against women. So if you read this, you really I mean, I really get the impression that's not what happened here. What happened here most probably is Amazon built this tool, okay, and it fed in its past hires and we know of issues of like data set bias bias inherent in data set. So if your data set is skewed, the AI tends to pick up on the skewed data set and become skewed itself. Okay, so I actually would argue that most or all of the examples they stayed in here are examples of such biased data sets and not. So the the cause of the bias is the data set that they are strained on and not the person that ran the code or built the algorithm to train it on or built the deployment. And so but it doesn't matter you're a you're Amazon, you built this tool and you realize, oh, it discriminates against people having women's on their CV. So this is a pretty bad PR wise. So you tell your engineers engineers fix the problem. So the engineers go fix the problem, they come back and say, okay, we fixed the problem. And then what you do is you say, okay, engineers, can you ensure me that the algorithm would not be biased against women? Because if only the slightest bias exists, if only it doesn't even have to be if one journalist finds one example, where there is a down rank, because I add the word women's, then we are screwed, right? And the engineers will say, No, we can't guarantee that it's a deep learning system or something, right? We, we can't like give you a proof that it's not biased. If you're a smart executive, at that point, you'll scrap the tool, because the potential PR downside are just huge. And probably they've also realized it's not that handy to have this, this tool compared to their recruiters doing their job, because their recruiters might actually be good and have been doing this for a while. So to the to the fact that this tool was scrapped is probably much more a result of a PR disaster. But also independent of that to say gender based discrimination, sorry, gender based discrimination was built too deeply within the system to be uprooted using a purely technical approach. It's just I mean, what is what is this? This is just trying to discredit this kind of technical, technical going about solving this problem. I'm pretty sure if someone comes to me and says here, I have this tool, and I can mathematically prove to you that it's not biased, then it's not then the problem is solved. And also, I really don't see how the person training the algorithm, or the person researching such an algorithm has any influence over how the algorithm works, because they're not the ones making the data set, or if they are, yeah, then they can make a better data set. Also, if a person comes and makes a better data set, that will fix the problem. And it doesn't matter what skin color the person has that makes the better data set. So all of this, this link is just not demonstrated here, or anywhere here at all. But this this here is the closest Amazon that this report actually comes to making this point. And I said before, I drew that drew this thing workforce AI bias, right? So this this link since it here the AI system is used for hiring the workforce. So at least one could make a claim that this link is somewhat demonstrated. But I this it's a weak case, I would agree, but this is the closest they come. So that and but then to go this direction, you have to somehow argue, well, the workforce somehow makes the AI system bias, no, the workforce influences the data set. If the AI is trained, so if a hiring AI, how do you train a hiring AI, you optimally train it on the performance. So this this employee here is going to have a performance over time, right? And the AI system will look at that performance over time. So if the AI system even if it's initially biased, because it learns from the risk recruiters, it will learn that, okay, actually, if I always forgo these women, then I don't get as much performance of a workforce, so I should correct for that. So if you train the AI system on a good metric, then then then this problem will leave even out itself. But again, this Yeah, this this is this could be considered like one point in the argument, but I think it's a very weak point. And only because the AI system is actually used for hiring, where I think the point they're making is a much larger one is the general bias in the AI systems contributes to the workforce imbalances. And there you somehow have to say that, okay, the AI system somehow influences society at large and society at large then go leads to the workforce being skewed. I don't Yeah, that it's just not strong enough, in my opinion. And the other direction also isn't isn't strong here. But again, the examples only get weaker from here on. They go on to say, this is just one of many examples that show how the functional logics of a given technology echo the gender and racial dynamics of the industry that produced it here. Yeah, this, that's the claim they're making to echo the gender and racial dynamics. And they're actually making a stronger claim, namely a causal claim. They give the other example of the Amazon's recognition facial analysis service previously demonstrated gender and racial biases worse than those of comparable tools. So it failed to see dark skinned women while being most proficient at detecting likes light skinned men. And they later go into this example again, where they basically also state yes, this is an issue of the data set, the data set being much more comprised of white men. And they say, but then they have to kind of make the turnaround argument and say, well, the data set is a reflection of society and society, you know, part of society is the workforce. And it's just not, I mean, it's again, this argument only works if you already believe the conclusion. Otherwise, there's actually no argument there or no solid one. But what they do here is they say Amazon's initial response to such criticism has been to try and discredit the research behind it. This reaction, or let's let's first discuss this. So the Amazon, yeah, Amazon, of course, being the accused here and a multi billion dollar company and the criticism is something that is PR wise very bad for them. They discredit the research tried to discredit the research behind it. It's understandable that this could be dishonest from Amazon side, right? I mean, they're getting attacked. It's like, you know, the tobacco companies trying to discredit the smoking research, but still, I mean, that doesn't mean it's wrong. It could actually be bad research, right? You have to actually go and look at what's Amazon saying, what is the research really doing? Is Amazon right or wrong? Completely open that Amazon is wrong here, but you still have to go look. And this citation here, I've tried this citation here. This one isn't to a to Amazon's response. It's to like a medium article and the medium article doesn't even include Amazon's response. I've looked, maybe I haven't seen it. It doesn't also doesn't link Amazon's response. Maybe it links something that links something or that includes it in some way. But basically this medium article only states, yeah, Amazon has been denying this or Amazon has been critical of this. And if you state such a sentence, Amazon's initial response to such criticism has been to try and discredit the research behind it. I at least expect the citation to lead me to Amazon's response so that I can verify what they're saying. Right. So this, I mean, I don't know, willing to chalk it up to incompetence rather than malice. Right, but then they go on and they say this reaction is evidence of the wider problem. The research was conducted by two well-regarded AI researchers who are women of color. By attempting to publicly discredit their expertise and research methods, Amazon is reinforcing the same kinds of prejudice and derasers that the research critiques. Yeah, here you go straight to the identity of the researchers. Like play the race card straight out. I mean, this is maximum dishonesty, right? Except if Amazon said something like, well, these women of color, clearly because they're women of color, they have no idea what they're doing or something like this. This is basically it's coded language for saying either saying you're not allowed to criticize people of color because they're a minority or you're basically saying Amazon is racist and that's why they criticize them. They just don't take them seriously because they're women of color. I mean, both are both are abhorrent. This is just dishonesty really stated here too. I mean, again, I'm perfectly willing to accept that Amazon's critique of this research is wrong and is not well intended because they're the ones attacked, but you still have to examine it rather than say, well, they shoot against women of color and therefore somehow that makes their counter argument irrelevant or even racist or something. That's I don't know. I find this dishonest. Yeah, I don't know about you. Moving on. So they go on and state a number of examples of bias and discrimination in the workforce and they a lot of times they make a mixture of the gender and race imbalance in workforce and things like sexual harassment not being taken seriously by the companies and also the things like gender or race pay gaps, which I'm open to accept that these things exist and are even intertwined. But just to tell you what's happening because we're kind of skipping but it's kind of a mixture of these things. So they say these issues are systemic. There's a close relationship between these workplaces with discriminatory practices and discriminatory tools, a feedback loop that is shaping the industry and its tools. So again here to state, I think I've stated it enough now that or demonstrated enough that I'm really representing their arguments as they intended it to namely that there is this kind of causal links and loop between these two things. And they shoot against the fairness literature by saying from this perspective, locating individual biases within given technical systems and attempting to fix them by tweaking the system becomes an exercise in futility. Only by examining discrimination through the lens of social logics, who it benefits, who it harms and how can we see the workings of these systems in the context of existing power relationships. So they say these issues aren't technically fixing these systems won't help. If that's the problem. And I agree, if that causal link actually exists, then technically fixing the system might not solve the problem. Not even sure. I mean, if you technically fix a system like this, then you technically break the causal link and thereby fix the problem. I would not sure, but again, this is based on the hypothesis that they've already reached, like demonstrated their, their conclusion, which they haven't and which they are not in the entire article. Yeah, so the next section goes into who makes AI so I don't know about you, but this section was titled how workforces and AI systems interact. And apart from one, the AI system being used for hiring the workforce, which is said this one instance where actually there could be one causal direction from bias to different misrepresentation the workforce. Other than that, there isn't really anything in there that really shows how these two interact, especially in a in a causal way. Alright, the next section is called who makes AI is broadly about the about the gender and race imbalances or miss not unequal representation in the workforce. And we're going to skip this diversity statistics that kind of that discuss that diversity statistics of companies aren't really accurate, or can be, you know, massaged kind of by the companies, which you know, is true. Definitely companies will always try to maximize their profits. And even if they give out such a report, so that definitely critical thinking is in order. Alright, so the next section is called the discrimination feedback loop. Right, if so if in the earlier section, you felt like here we go into the meat, then you must feel with this title, like, okay, we're actually going to see how this loop works and how the two things are really linked, like how one causes the other and vice versa. So let's jump in. They say AI systems increasingly play a role in our social and political institutions, including education, healthcare, hiring, criminal justice. Yes, therefore, we need to consider the relationship between the workplace diversity crisis and the problems with bias and discrimination in AI systems. No, why I don't see how therefore, but yeah, so I don't see how therefore we need to consider the relationship. Okay, if there is a relationship, we need to consider whether there's a relationship. Okay, granted. So they say fairness, accountability and transparency research is playing an emerging role. Now what they mean here is the aspect of fairness, accountability and transparency research that shows that there is a problem. So I told you there's two sides, one side is showing there is a problem in current systems and the other side is trying to fix them. So they're very much fans of the side that shows that there is a problem and they use show some of these problems here, we've already seen some but they show some more like Facebook's ad delivery systems let users to be shown as for housing and employment in a discriminatory manner. So giving 2019 study found significant racial bias in a widely used commercial algorithm used to determine whether patients will be enrolled in care management programs. So these are these are just examples of these AI systems being biased. So they go into this say taking a contextualized view may enable more extensive account and the contextualized view they when they say this they mean anything more than just a technical approach at solving these problems. More extensive account of bias to emerge future work could examine the politics of system design study how AI systems in situated reality and study AI systems in situated realities ask why a system was designed in a particular way, how it was constructed, whose interest it shaped shaped by the metrics in which its success or failure is assessed, rather than solely focusing on improving existing data sets or individual algorithms. Yeah, I agree. I mean, we always have to we always have to pay attention to these things, especially like looking at the metrics by which its success or failure is assessed. But a lot of times this is this is rather straightforward in kind of if you look at the metric, the metric most often, especially in commercial applications is money, right? So the metric of like an ad showing system, like if I have a system to recommend ads to people, show people ads and personalize them and so on, I simply want to maximize my revenue. So I want to sell someone something. And everything I want to know is how likely is it that person is going to buy that thing? Right? I that's basically Yeah. So in essence, sometimes it's really valuable to consider what capitalism is. So in capitalism in so capitalism, these kind of this system we're working on is kind of a form of limited capitalism, but mostly mostly capitalism. And capitalism is very greedy. So capitalism, all corporations want to do basically is make money. And that is and on the other side, you have discrimination. So discrimination meaning these unequal represent like unequal distribution actively. So and often sometimes these go hand in hand, sometimes you can make more money by discriminating against a certain type of people. And that's, that's a really bad scenario. Like that's a very, like, this is really something where we need to take action. But a lot of times, a lot of times, these two things stand in opposition to each other. So little arrow here, non compatible. That means if I want to sell someone something, then I maximize my profit by not caring by accurately assessing how likely is it that person buys that thing. If I want to discriminate here, if I want to discriminate, start discriminating, according to skin color saying like, No, I don't like that this person with the skin color is able to buy this product, I want to kind of keep them down, and so on, then I forgo profit, right, then I actually, even though this person could buy this thing, I forego that. So often these things are in direct opposition to each other. Also, if I am in charge of hiring, and I don't like people of a certain gender, but they would actually be really, really good, whatever, good employees. So I forgo that, that means I'm getting a pay more for less qualified people just because I'm biased and I'm down ranking unjustifiably, these people of the gender I don't like. So oftentimes, you have to ask yourself, are people fundamentally greedy, or discriminatory? Which are they more? If push comes to shove, would they rather have more money? Or would they rather keep their own race and gender group in power? And with just, yeah, so the and you have to ask this of corporations, you have to ask this of people. And in my experience and view, like people are much, much more greedy than they are willing to discriminate and give up money for discrimination. And so if we look at metrics by which success or failure of AI systems are designed, then I would argue a lot of the times metrics are actually profit incentives. And especially if we look at data set construction, if there is a skewed data set that makes my AI system be biased, that actually loses me money and the company would profit a lot from building a better data set. So looking at kind of metrics actually makes a lot of sense to me and very much in favor of that. And I think by designing accurate metrics and then getting the best possible information, the best possible data sets to maximize these metrics will oftentimes actually eliminate such forms of discrimination. Again, there are situations where they don't, we have to be very cognizant of these. They go into this and they say, also examine more thoroughly how societal discrimination surfaces in data provenance, examining the history and process of data set construction and considering how cultural norms and stereotypes were enumerated and represented at the time of data creation. This is a big issue. Yes. The data set construction kind of at the time of data creation and so on, this is a big issue in these systems and a lot of bias. And I would argue most of the bias we've seen here arises from corrupt data sets and from data sets that were constructed in an already biased way. And the AI system trained on these data sets simply replicates this bias. So I think that's very correct here. They go into this example, they say the labeled faces in the wild data set contains over 15,000 images. Only 7% of images are of black people. This is because these, the media landscape of the early 2000s, these images were gathered from the news media at the time, predominantly featured white men in positions of celebrity and power. This exactly. So if you train a system on this data set, the system will inherit this bias. Yeah, so this is a classic example of a corrupt data set. Also this isn't only with race and gender. This is also if you like take pictures from IMDB, yes, a lot of this currently Celeb A data set that is used in all the GAN research is collected from IMDB. You probably have overly beautiful, like pretty face people on there. So that your AI system, your generative model is only going to produce mostly pretty face people, since movie stars tend to be a lot prettier than the average humans. So that the kind of data set construction process, I think is currently the biggest source of bias in AI. But that also, it's interesting that they go into this here and they kind of want to make the point that this is because society and power in society, the data set reflects that. But I would argue if someone makes a data set that doesn't have this bias, then the problem is solved. And I don't care who makes the data set. So the link between the workforce and the bias is really broken by an argument like this, because as soon as we have a correct data set, an unbiased data set, we can mitigate the bias. And they even go, they go into this here. They say, sorry. Yeah, they say down here. They say these people, these researchers have looked at these facial recognition systems and they assessed this what we saw earlier, higher error rates for darker skinned women than for any other group, lowest error rates for light skinned men. To measure this disparity, these researchers developed a new data set that is more balanced, both in terms of gender and skin color. Good. Problem, like make a larger data set to actually train on and then problem solved. And I don't care at all what race and what gender these people are. Well done. Good people make a good data set like this. And then we've solved the problem. What's the problem here? Why would you ever care what these people look like if they do good work? That's to me, this actually breaks their own argument. I don't know why they included here. To me that to then suggest that there is a link to the workforces, if here is obvious that if you fix the data set, you can fix the recognition system. All right, so we'll go on here, jump a couple more paragraphs. Except when they say they shoot again against this kind of say to this point, a focus on fixing technical systems in isolation without examining their broader context of use and power and dynamics that attends issues is not limited in its intervention, it can actively cause harm. So if you fix the problem in a technical manner, they argue here it can actively cause harm. And the example they give is that facial and image recognition systems, they are often applied in service of police surveillance, which disproportionately harms poor people and communities of color. So there's a quote from this person that says, is this not social progress to make black people equally visible to software that will inevitably be further weaponized against us? We are considered criminal and more surveillable by orders of magnitude. Whatever claim to a right of privacy that we may have is diminished by a state that believes we must always be watched and seen. So this is an example where by improving the facial recognition for black people, it makes the police better at surveilling them, which is true. And then it is an ethical problem that the police is able to use these facial recognition systems to surveil people. That's a massive privacy problem. That's a massive problem in how much the state is allowed to overreach and so on. So I think it's a discussion in itself, but here they argue because at the very beginning I asked you to remember this whole notion of we always have to look at who benefits from the way the AI system is constructed, who is harmed from that, who benefits from how the metrics are shaped and so on. In this case, we actually have a perfect example where if the face recognition system is very inaccurate for black people's faces, that actually helps them in the societal context. So by logic of this report here, that must mean that somehow the bias works for them and thereby the system is good or something like this. And by fixing it, you actually make it worse. Yeah, they say it can actively cause harm. So I think this is pretty much arguing against themselves earlier where they say, oh, we always have to look at who benefits from the system. Yeah, here, if the face recognition system can't recognize you, you actually benefit. So I don't think that argument works in any case except if you only look at it when you want to look at it. All right, so we're going to jump a couple of sections here. But the core thing here was the feedback loop. And again, the feedback loop isn't demonstrated at all here. Just examples of systems that are biased and of data sets that are biased, because of data sets that are biased. But there's no demonstration of how the workforce, I mean, yeah, just take this previous argument. So the workforce is supposedly supremely white. And it makes a face recognition system that makes that is performing poorly for darker skinned people. And that actually in this context of police surveillance helps the darker skinned people compared to the lighter skinned people. So that kind of is an exact counterexample to the argument that this misrepresentation in the workforce leads to the biases in the system. If we interpret it through the lens, who it costs and who it benefits. All right. So the next section is corporate diversity beyond the pipeline problem. And this is kind of an odd inclusion when I read it first to interpret to go against the pipeline problem here. But it kind of makes sense if you know what these people set out to do. So what these people set out to do is to argue we must fix the workforce, right? We must fix the, we must hire more people of color, more women and so on, promote them more. And they have a very much have a problem with this pipeline argument. What the pipeline argument is, is the following. So at the beginning, if you consider like the educational or career paths of people, then you have like 100% of people that's represented at this at the beginning, and then most of these people go through school. So most of these go on. This is kind of the area in here is the population. And then some of them pursue higher education like some drop out. So this gets a smaller amount. So this is here, this is time and this is kind of volume of people. And then very few go into computer science, right? And then even fewer go into AI. So what you end up is just a tiny sliver of people that actually go into AI. So this is called a pipeline, and we have various junctions here like where you would go into higher education, where you would choose your major in university, where you would go into a subfield of computer science, where the kind of volume of people drops significantly from one point to the other. And now if you compare this, if you compare this and use it say, we're not considered all of society, but here over here we'll call consider all just men and over here we'll consider all women again, they all go to high school and then university and then maybe very few go to CS, even fewer go to AI. What you'll find is, and I've drawn it maybe wrong here, is that this is smaller than this. So if you comparatively look at how many males end up in the AI field, you will find that fewer end up in more and will end up in our field than women. If you comparatively look at it. So at and this is over time, like at the beginning, you have 5050 main women distribution in society, almost I guess, I think slightly more boys are born, but I could be wrong about this. And then as you go through time here, excuse that I believe. So you go through high school and let's just assume like high school is still kind of equal, it depends on the country. Then you go to university, where there's actually more women at university slightly. And then you go into computer science and in computer science, and this is just relative here, that's why I kind of norm it at 100%. Otherwise these things would go down all of them at the same time. But comparatively, you have then much more men than women in computer science. And then if you see who chooses AI, I don't know if there's any statistics of specifically choosing AI from computer science. I'm just going to assume that remains the same. So if you look into the AI field, kind of this, this will stay the same. So in the AI field, you have much more men than women. And presumably, because you already have much more men than women choosing computer science as their major or choosing any technical field as their major. This is kind of the so called pipeline argument. So where do AI companies hiring come in? AI companies come in here, they hire at this point, after your university degree, presumably. There's exceptions, but just say they hire after your university degree. And therefore, they basically have to choose from this distribution. And if they just say, okay, we'll just take the top, I don't know, 10% people will hire the good people of this, we don't care what gender they are. Right, so the top 10% here, the top 10% here, then this will end up being the same distribution as you have graduates. Right, so this is kind of the company, company hiring from an let's say an 80 20 distribution without looking at gender will end up with an 80 20 distribution. That's the pipeline argument of companies. And they don't like the pipeline argument, because the pipeline argument basically says that the problem is somewhere here, right? The problem isn't the company's hiring wrongly. The problem isn't that the company's here, deselected, the problem is somewhere here. And because they want to make the argument that the company should hire in a different way, they can't have that. So they argue against it. Now to argue against this would actually be very easy. If this argument were wrong, like they claim the argument is is is not good, the pipeline argument isn't good. If the pipeline argument were wrong, what you'd have to do is you would have to say, you would have to say, hey, companies, look at that. In your company, you have an 80 20 distribution men to women, right? That's pretty unequal. And you know, in university graduates, the pool you choose from is actually 5050. So obviously, you're engaged in discriminatory hiring, because you know, the pool is 5050. There's no reason why it why your hiring practices should cause this inequality. And therefore, we can clearly show you do discriminatory hiring, you should stop it, you should definitely hire more women and people of color, more of these more of the minorities, because your hiring practices are the problem. But that's not the case. How do I know? Because if it were the case, they would simply state this. Definitely in this report, if that were the case, that you could actually show with numbers that the pipeline argument is wrong, then they would absolutely do this. That they have to like, go back and they have to like, ramble around it for several pages, which will mostly skip but mainly because this is the case, it is the case that these companies hire from a pool of of unequally represented people. And the only argument that you can make is that, well, if if you were to equalize this here, then maybe here where the problem is that would fix like, so the argument is often made if young girls choosing their majors have no one to look up to, like no strong women in in corporation CEO roles, they will think that it's not a climate for women and they will elect not to go into these fields, which is a valid argument, like I'm completely open to that to that argument. But it's the only argument you can make. And still then, even if you determine this as the cause, I would still not support racist and sexist hiring practices like do something else like make them clear that the environment can be changed or change the environment, like change the if if it really is the case that it's kind of a non anti woman environment, change that. If it's just the case that they perceive it as such change the perception, but do not engage in discriminatory hiring practices, because there's always someone losing out unfairly on these practices. And that's, that's something I'm not willing to, to go into, like that's something I'm not willing to engage in. And I don't think people should engage be engaging in that. Actually, that's why it's illegal. So let's, let's actually look at very few points. This is just why the so they claim they go kind of go over these pipeline studies. And they yeah, they say term used in industry to reference the absence of diverse candidates in the hiring pool of to justify the inability of large firms to achieve diversity due to scarcity. Right? So that's, they basically agree the of that on the definition that I stated here. So the companies that are challenged on their lack of diversity frequently site pipeline studies as proof of the persistent challenge of finding enough women and people of color to hire. Yes, and, and the yeah, but they say but the evidence suggests otherwise. For example, in 2016, Facebook chief diversity officer wrote that it has become clear that at the most fundamental level, appropriate representation, technology or any other industry will depend upon more people having the opportunity to gain necessary skills through the public education system. Well, yes, that's something I would agree. And that's something clearly that addresses this region here. Then and where the actual problem is happening. So I would say that's a very, very good statement from the Facebook's chief diversity officer. They say but as the Center for Investigative Reporting study of tech company diversity data found 91 large tech companies headquartered in Silicon Valley managed to hire higher percent of black, Latino and multiracial employees than Facebook that year. Well, just if other just just because other companies employ racist and sexist hiring to improve their diversity numbers doesn't mean that Facebook has to do this. Right? It it like just because other companies do this doesn't mean that it's a it's a it's a good thing to do or that's how you should go about it. Facebook simply says like, if we want to hire without being racist or sexist, if we want to just hire the best people, then more of the best people have to be in the pipeline, like more people have to gain access to educational opportunities so we can then hire them. Whereas these other companies probably make a big effort to say, well, even if you are not as educated, even if you're not as qualified as this other person will hire you because of your skin color. I don't think that's that's an argument in that in the favor of what the report is claiming. Like I don't think that that is evidence that the pipeline argument is invalid. All right, so they go into core themes in pipeline research, and they do some they do some overview of the kind of pipeline research that often so sometimes the pipeline research examines why, why, for example, why women don't choose to go into computer science as much and sometimes they focus on what is their perception of the field, what was it, what is their perceptions of the stereotypes of the field, what is their perceptions of the kind of culture in the field, is it suited to them, what is their perception of how qualified they are for the field, and is that true, is that false, and so on. So this research examines a whole variety of things. And it's very interesting, actually, to read through this research. I want to point out this here. Other studies suggest that gender is correlated with a person's motivations for pursuing a career in the field. Women and particularly women from low socioeconomic status or minority backgrounds are more likely to see computing as a versatile profession that provides an opportunity for secure employment, higher pay, and better social standing. Moreover, their interests go beyond technical aspects of computing, focusing instead on the purpose and application of software. However, such interests are often de-emphasized in computer science curricula, a price technical skill and its applicability to industrial settings above all else. So I find this really interesting because it's basically saying that women have different interests than men on average. That's basically saying that, which is almost heresy. To say this in this context, people will come after you if you suggest something like this, and yet they're just stating it here. Remember this for later. This is really funny that they're like, yeah, the interests could be different for women than for men. And we might have to adjust our curriculum to be more suited to these different interests. I mean, yeah. I'm sure that's... Yeah, as I said, you're like, usually this is forbidden to say. All right. So they go on. They say limitations of pipeline research, right? These are fairly like common limitations, let's say, of studies in general, social science studies, which I won't go into much. Again, they state we have to examine... We don't only have to examine this, but the problem... They basically say the problem is actually the culture and the problem is actually the perpetrators, where do I say? I don't remember where this is stated, but they again say we have to examine who benefits from its present construction, who is underserved within the current tech ecology, who benefits from its present construction, how these dynamics might be untangled, and so on. So again, stating these kind of power relationships for the different groups, which I don't agree is in large part what's happening. They say it's worth considering the scope of these studies and by and large, the recommendations they issue are limited, targeted at the administrators of university computer science programs seeking to broaden the diversity of their student body. Yes, that's exactly where we saw the problem appears to be, right? So the reason they have a problem with these studies is that they actually focus on the point where this discrepancy appears to happen, because they want to claim that no, no, no, you should focus on a different point, namely hiring in these companies, hiring and promotion. They say though important, so at least they acknowledge that that's an important problem. This is a narrow frame through which potential solutions to barriers to inclusion. It does not address the companies that hire computer science students, the peers responsible for promulgating stereotype views or engaging in hostile behavior or the broader social conditions that may influence students' success in computer science programs. Actually the research and even some of the examples they've included of this research addresses all of this. But the research often addresses the kind of stereotypes and how the peers act and how the companies act and also how the companies hire and how people have something to look forward to or nothing to look forward to and how that influences their decisions. Yeah, again, they say the studies are frequently cited by those within corporate environments to justify their own lack of diversity as they situate the locus of change outside of the corporation itself. As such pipeline studies are disproportionately emphasized as a part of the broader research agenda on diversity and technology. Again, they state companies use this to get out and of course, like companies, of course they're going to use this to get out. I mean, I agree at least with that. I agree that companies are going to try to use this to get out of responsibility. Certainly. All right. So the last section here is the pipeline dreams after years of research. Again this is on this pipeline studies. Basically they say the pipeline research hasn't shown, like hasn't borne fruit. It hasn't led to meaningful change in the field even though we've researched this. The reason they say the number of reasons they tend to place the owners to solve issues of discrimination, Silicon Valley on those who are discriminated against rather than the perpetrators. I find this word choice really interesting. Perpetrators, right? Like again, the group of white men is trying to put down everyone else. That's the perspective that the article takes. And it's not even true. This research, a lot of times it actually says the reason why, for example, women don't choose to go into computer science is the male dominated culture within these corporations, is the perception of this not being a woman friendly environment, is the people here of sexual harassment and so on. So it's not even true. But moreover, I just wanted to point out the choice of word here, perpetrators. I don't know how you get to this word. It really shows kind of a worldview of the authors in my opinion. All right. So they go on and say, okay, this pipeline studies haven't been beneficial and companies haven't done much or hasn't been successful. They're going to worker led initiatives, which I'm going to skip here. It's just a kind of a reporting of what happened at companies where the workers themselves organized. And then the last section here is the pushback against diversity. So in this section, they're kind of documenting and arguing against people who have basically stated counter arguments to their recommendations mainly. So their recommendations being, let's change the hiring, let's change the promotion, and so on to be based on race and gender. And the pushback here characterized in different ways. So we'll go through this. This is the last section. I know it's a long video already. If you're still here, like the one person who's still here, hi, I hope you're doing well. Good. Keep hydrated. Yeah. So they say, it's a critical time. We now see diversity itself being weaponized. So they say this growing awareness accompanied by demands for inclusion and equity has led to some change, but there has also been resistance, especially among those implicitly privileged by the status quo. So again, jumping straight to attack on the person. Like I don't care if who makes an argument against me. I want to go on the argument and I'm going to go on the content of the argument. But these people straight, first thing they stayed is that's just by the people who are benefiting. That's just by the white men, basically. Straight to the identity of the person. That's dishonesty right there. So those questioning and even rejecting the idea that racism, misogyny, and harassment are problems within the AI field and the tech industry have appropriated the language of diversity to argue that efforts to improve inclusion are in fact exclusionary and addressing the deeper structural challenges posed by racism, sex and inequity is misguided. And yes, yes, definitely efforts to improve inclusion can be exclusionary. Like just because, so this is a thing, just because you're fixing a problem doesn't mean the method you're using to fixing it is justified and is itself good. Methods to improve inclusion can be exclusionary and some that have been proposed are exclusionary. Definitely it depends on the method. It doesn't mean these people are against these efforts. It means that the measures, for example, implementing racist hiring policy, I can definitely see that this is going to lead to more equal representation within the workforce. But the tool itself is really bad and exclusionary and discriminating. So yeah, I would say that it's accurate that it can be exclusionary. I say, for example, some AI researchers greeted the announcement of Black in AI Workshop at NRIPS leading machine learning conference by questioning whether the event was necessary, arguing that it would be discriminatory. But can't they? Can't they question whether the event was necessary? Like that would, I would, here I would need a discussion. What is it for? Right? Why is this event happening? And what is it doing? And is it discriminatory? It could be. Any event can be discriminatory. Does it discriminate based on race or gender or anything? Is it, you know, does it do so unjustly and all? So I don't, I don't just don't see why. Could still be wrong. Like you could question and then you could be wrong. But you should be taken on your argument. But the argument here is just already questioning this is already on the wrong side of the argument. And I don't agree with this. I don't agree with these people that question this workshop. Don't have a particular opinion on these things. But I have the opinion that you have to take arguments at their argument value and not just at who makes them or whether or not they're against a particular viewpoint. All right. They say such pushback often centers calls for cognitive diversity or viewpoint diversity. The idea that individual differences in the ways people think and understand the world are distinctions that should be counted alongside or instead of other identity categories such as race and gender. Well, yes, that's I mean, isn't that isn't that a very reasonable thing to say? Isn't it very reasonable to say that differences in the ways people think and understand the world, their distinctions that should be counted alongside other identity categories such as race and gender, they say a dozen white men so long as they were not raised in the same household and don't think identical thoughts could be considered diverse. That's I don't know if this is a sarcastic statement or not, but clearly it's it's kind of the counterpoint they're trying to make here that but yes, I would I would totally agree with this statement in a way a white man growing up in San Francisco, a white man growing up in rural Idaho, a white man growing up in Florida, a white man growing up in Western Europe, one in Russia, and one growing up on the road with its circus, his circus parents in Mongolia would definitely be that plenty diverse, right? I mean, they criticize this here, but this is is actually how can you how can you not see this that? Yes, these are valid differences, and people are going to think differently, independent of how they look, people are going to have different thoughts. And it's important to recognize other people think differently. And therefore, you should, you know, include them if it's relevant. And the counter argument to this is, of course, what the authors here are saying basically is that 12, a dozen people, as long as they are don't look the same, could be considered diverse, even if they all were raised in the same place, and basically all live in San Francisco, and think the exact same thing. Yeah, that's, I mean, it sounds to me, it sounds as absurd as the other way around. To me. So here's, here's my, here's my thoughts on this. I am not going to pretend that I know what life is like as a woman. Right? I'm absolutely sure that for areas of life, it is it is definitely valuable to listen to the experience of a woman or multiple women, an aggregate of women, because the life is just different as a woman. Life is also different. As a black person, I absolutely concede that there are things that I might not be able to draw from my life experience, because I am not of that skin color that different problems that people face. And that's why it's important to have an opinion of that at the table. But I'm also absolutely certain that I have no relation to someone who grew up as a child pop star from the age of 12, and then had that life. I have no relation to someone growing up under a communist regime. I have no relation to someone growing up in in kind of a Buddhist religious tradition. I just don't. And I don't care how they look. They have different experiences. They have different bodies of knowledge to draw on. And I don't think why we should make the difference along the exact lines of race and gender. Yeah, but that's what they that's of course what they argue here. Those arguments work by centering identity while flattening or ignoring power relationships. Here the VP, the Facebook VP of engineering said that the ultimate goal is cognitive diversity and cognitive diversity is correlated with identity diversity. That means it's not just about getting women in tech, it's about broad voices, broad representation. Right? So the the this is exactly what I would say the reason why we want different the reason why we want a woman or a black person at the table is because they have a different knowledge is because they have different thoughts because of their different life experience. They have different thoughts that they can bring in. So actually, by including these what they call bodies, it is about cognitive diversity, even in itself. But the authors here really see this from a different angle. They really see this in terms of power relationships between race and gender groups. And I yeah, the arguments of the authors don't make sense if you don't view it through that lens. That lens to me is just such a it's such a I don't know, it's just sad look on the world. And also, I think it's a very, very inaccurate look on the world. And it's, I think, a very dangerous look on the world. Um, yeah, again, they say instead of looking at historical patterns of marginalization, calls for cognitive diversity argued that all differences are equal. No, we're not. Like, no calls for cognitive diversity or don't argue that all differences are equal. Well aware that some people have it harder, well aware that some differences are bigger, worse or better. That's absolutely well aware all they're saying is that race and gender shouldn't be the like, only things to consider and shouldn't be in itself be considered diverse. Just because someone is of a certain skin color, it doesn't mean anything, right? It doesn't actually tell you anything about that person. So why not consider people as individuals and look at what was their life like until this point and what could they contribute to the discussion we're having rather than looking at the color of their skin. I mean, if the color of their skin played a role in their life, then obviously that would manifest in my suggestion as well. But to just look at people through this kind of group lens is is so foreign to me. And yeah, I feel it's it's quite dangerous. Yeah, so again, and this this could argue that all differences are equal. I mean, the point where you have to start misrepresenting what the counter argument is saying, that's really how you know you're dealing with a with not a well intentioned person on the other side of the of the discussion. This is really politics now. This isn't a well intended argumentation. It's really someone to trying to achieve some goal, because they have to misrepresent the other side. And this only gets worse from here. They say recently was exemplified in the controversy over Google's appointment of Heritage Foundation CEO K calls James to its Advanced Technology External Advisory Council. Google's reasoning for the appointment of James was ostensibly to ensure diversity of thought by including a conservative viewpoint on the council. Alright, so Google has a technology advisory board, or council, sorry, of external people, and they've included a conservative. And she is by all by all metrics, let's say, a standard conservative. So this is not a far right neo Nazi type. I don't know. But this is this is someone who has similar opinions than half the US country and in generally in at least in the Western world, generally half of the of the country's population tends to be conservative. More or less, I mean, there's differences. But yeah, so this this is a this is an opinion that a large portion of the population shares. So it would be I don't know, it would be suitable to include at least someone of that opinion in an external advisory council to to have that on board. You don't have to listen to her like she's not like she's made king. It's simply that she will have the opportunity to input her voice representative of kind of that large, very large percentage of people. They go on to say, James is also a black woman, thus adding racial and gender diversity to the panel. So even further, right, this is it's a conservative black woman. All right, but the pushback following James's inclusion focused on her policy position, citing specifically her vocal anti LGBTQ and anti immigrant views and highlighted why cognitive diversity is a particularly limited lens. And the pushback here was very much spearheaded by one of the authors of this article. So I am this isn't just reporting. I will also I'll also criticize the the this pushback here since it's, you know, it's kind of argued for in this article. It's not just reported and also because the authors are the same. So here they say they have vocal anti LGBTQ and anti immigrant views. And I haven't actually gone specifically and looked at what this person particularly has said, but given that she's a standard conservative and has been in public office, I believe under George W. Bush, she can't like I have trouble believing that she has like extremely hateful opinions like these people shouldn't exist or like something like that nature. Like often people like conservative people have have issues with forcing people to adopt certain pronouns for people or issues with which bathrooms do people go in and, you know, generally are tougher on immigration, especially illegal immigration and so on. I mean, these are these are views that people hold. It's a large part of people and these are discussions to be had. So including this this person would be very sensible move. But they say in a letter opposing the appointment, a group of Google workers calling themselves Googlers against transphobia and hate, transphobia and hate responded to the idea that diversity of thought justified James's addition to the council. This is a weaponization of the language of diversity by appointing James to the ATAC. Google elevates and endorses her view, implying that hers is a valid perspective worthy of inclusions in its decision making. This is unacceptable. Here it says again, the author was one of the organizers of that. And that's what they're saying here. The views, if you don't have our views, these are unacceptable views, right? It's valid perspective worthy of inclusion. It's what they're saying basically is you don't even talk to these to this person, like talking to this person, considering their opinion. You can still evaluate the opinion, but even considering their opinion is already wrong. And that given that the person is a black woman. So basically, they are called the author's idea of diversity is people that look different that are from race and gender groups that have don't have much power or perceived what they call power right now. As long as they all think exactly as we think, right, then that's fine. As long as they they share our thoughts, as long as they don't have dissenting opinions, we want the we want the different looking people. But don't dare talk to anyone of a different opinion. Yeah, this, I don't I don't see how I mean, these these authors, in my opinion, they really live in in a bubble, they really live in the in a tiny Silicon Valley or Silicon Valley influenced spaces, because this is this is half the people they basically saying half the people in their greater community in their country aren't even worthy listening to their opinions aren't even worthy of inclusion in of consideration. So yeah, well, well done might as well discredit them at once. I'm sure I'm sure I'm sure that's gonna fly well with these people. All right. Yeah, might might start calling them deplorables and see what they do. Maybe they'll return the favor and elect a moron just to stick it in your face. I mean, that's what happened. So the idea of cognitive diversity is mobilized by some support in support that the AI field and the tech industry are already diverse. Including as far as to support claims that not including identities like white and male constitutes discrimination. Yes, it can. Like if, if you include every single identity except white and male, that constitutes discrimination. That's I mean, yes, even if they're in the majority is still constitutes discrimination, like no one can help being born white and male, no one white and male chose to be born like that. Don't mostly don't choose the melanin content of your skin, you can modulate it a bit by going to the sun, which computer science people statistically don't do very often. So there's not much leeway there. So yeah, to not include identities like that, if you include every other one, can constitute discrimination. True. A July 2017 memo written by James Damore, a software engineer at Google is illustrative of such pushback titled Google's ideological echo chamber. And published in an internal mailing list, the memo critiqued the company's diversity policies arguing that biological differences between men and women rather than bias and discrimination help explain gender disparities at the company. I feel the you can leave out the rather than here. I think the memo simply stated that biological differences can help explain the gender disparities. The most objective writing the memo was to make the case that policies designed to achieve equal representation are unfair, divisive and bad for business. Well some are. Yes, especially the recommendations that you've given at the beginning, number seven, is unfair, divisive and I would also argue bad for business. So supporters for Damore's point of view at times even drew on the rhetoric of the pipeline to make the case that diversity initiatives are in fact discriminatory. They argue incorrectly that if there aren't qualified candidates in the pipeline, then hiring those who are unqualified on the basis of identity discriminates against those who are qualified. No, I would say hiring anyone on the basis of identity discriminates. I mean inherently. So again I think that's the larger argument that these people are making, which is not incorrect, is very correct. So in an update to the memo Damore himself asserted that he values diversity and inclusion, but his primary concern was cognitive diversity. He says diversity inclusion is not denying that sexism exists, doesn't endorse using stereotypes. And in specific I've read the memo and it directly says these are population level kind of statistics and there is more overlap than difference and you absolutely can't say anything about an individual by looking at these statistics. That's almost a quote from this memo. So he was very much concerned with considering people as individuals, but also if you like he was basically making the same argument as earlier. I told you to remember, hey look this one study that found that women's interests might be different and we might shape the curriculum. That's basically what Damore said. He said women's interests might be different and we'd have to maybe shape the way we do work, like change the way we do software engineering to attract more of them. That was one of his points. So he's exactly the same thing, but of course he's a misogynist because he suggested that this could be due partly because of biological differences. And the way he was dragged through the mud is just crazy. And they shoot here very much against this kind of biological, what they call biological determinism. We'll see this very briefly. I'd say diversity becomes an empty signifier, stripped of the histories and experiences of systemic discrimination, repurposed around ideology rather than bodies. I'd say diversity has nothing inherently to do with bodies as such. I think that's only the case if you are already convinced of this. Within hours of the memo's publication, harassment targeting minority advocates who pushed back against the claims in the memo began, with a particular focus on queer and trans workers. That's bad, but also I think the pushback against people who voiced support was also pretty bad because one of them was fired, as you already stated. Google's vice president of diversity even locked down her Twitter account shortly after Demours firing, responding to the barrage of threats describing her as a police Nazi. Well yeah, if you fire something. I mean undoubtedly Google fired this guy because they thought it was less of a PR disaster if they also fired him now. This probably wasn't an ideological decision, much more a PR decision. If you fire someone after stating something like this, it very much looks like you're firing them because you don't like their ideas and you don't like what they're saying, which people generally are not in favor of censoring freedom of speech. But yeah, that being said, harassment is bad, don't harass people. Also that being said, criticism isn't always harassment and don't conflate the two. Demours' memo also stated that the distribution of preference abilities of men and women differ in part due to biological causes and that these differences may explain why we don't see equal representation of women in tech and leadership. This assertion hinges on a flawed assumption that identities like gender and race are essential and fixed biological attributes and that inequalities are at least in part the product of such irreducible differences. Well, I mean, if they're not fixed biological attributes, certainly gender and race have a 0.99 correlation with biology. Since your biology is first and it's determined when you're conceived, that demonstrates a causal direction. Even if they're not exactly fixed, they are overwhelmingly fixed. And to suggest that this is a flawed assumption, that these inequalities are at least part the product of such differences, what you'd have to do, they simply state it's a flawed assumption. What you have to do in order to show this is a flawed assumption, you have to show that gender and race, as far as they're biologically determined, have no influence whatsoever on these differences. That's what you have to show, right? That's the counterclaim because the claim is they have at least in part something to do with it. And that's also, I believe, what the more stated and what the predominant opinion like is very like all the research points to, for example, there is a large difference in interest between genders as far as, for example, career selection goes and so on. Now, we can talk about why that is, but there's also a large consensus, I believe, that this is at least partly determined to however degree, but it is at least partly determined by biology. In order to show that this is flawed, you need to show that it does not have, it can't have any influence, right? You have to basically prove them the impossibility of this having an influence, which no one has done so far, much to the contrary. So simply state this is a flawed assumption kind of shows to me that they've already, they are there, they're in a bubble and they're expecting to speak to people in the same bubble. Yeah, so they go on and kind of discredit this as called a biological determinism, which I don't think that's a correct use of the term biological determinism, but you can judge for yourself. All I think these people are saying that biology might have some influence and we could adjust for that. It's not even right, it's not even. Yeah, this comes up here. So conclusion, conclusion, finally, I think it's been two hours. Sorry. Conclusion. Throughout this report, we've outlined the scope and scale of the problem, tracing how the diversity crisis in the industry and the problems of bias and AI systems are interrelated aspect of the same issue. No. In the past, these topics are commonly examined in isolation, but increasing evidence shows that they are closely intertwined. No, you've shown that they're parallel. You have absolutely not shown that they're interrelated aspects of the same issue and you have not shown that one, any one of these causally influences the other, that there is any feedback loop. You have not shown that fixing one leads to fixing the other. I mean, you could also take a company that extremely is focused on, or for some reason has a different workforce and then show how their products with the same data sets as the previous companies don't end up being biased. Probably not so easy. But again, none of that is in the report. There are many things you could actually do to show what you wanted to show, but it's just not the case in this article. Our analysis surfaced two prominent responses to the diversity crisis. On one hand, a worker driven movement, which we've skipped. On the other hand, we observe a small but vocal counter movement that actively resists diversity in the industry. What dishonesty actively resists diversity? I mean, the thought that these people stray around like, no, I don't like the other looking people. It's just so absurd. All they're saying is that either we don't understand the problem in the correct way or our tools aren't appropriate to solve the problem. I think everyone has the same goal of the workplace and the AI systems being as fair and as non discriminatory as possible. Misrepresentation of the other side is something that really bugs me. And it's something that these authors do a lot. So yeah, I lose my polite side maybe. And uses arguments from biological determinism to assert that women are inherently less suited to computer science and AI. What a load of crap. Sorry, but uses to assert that women are inherently less suited to computer science. No one. Okay, not no one, but no one that I know. Asserts that absolutely no one that makes these arguments. Sorry, not no one. You can always find a sexist douchebag that makes that argument. But this is not a serious argument made. And this is not this counter movement. Most people in the argument that most people in this counter movement make. Not at all. And to represent them as such is just so dishonest that yeah, this this this basically this is the it's nice that it's in the conclusion because it finally like at the end it completely destroys the credibility of me taking seriously these authors. I thought they had so that the parts we skipped over I mostly would say I'm mostly okay with they mostly show parallels between the that AI systems are biased and they also show that there is unequal representation. They also show examples of discrimination, harassment and so on. Problems in AI companies and universities that all you can read the report for this that's it's pretty interesting to read. But the points I've addressed, I'm not happy with. Yeah, so that was it for now. Sorry this was took so long, but I felt that a thorough take was necessary. Have a nice rest of the day.
 Hi there, today we're looking at discriminating systems, gender, race and power in AI by Sarah
 Myers-West, Meredith Whitaker and Kate Crawford of the AI Now Institute, which is a part of
 New York University or associated with it.
 This is not as much a paper as it is a report, kind of summarizing current literature and
 also kind of an opinion piece slash recommendation giving document.
 Yes, so we'll dive into it.
 As you can see from the index, it's quite a long report and we don't have time to go
 into all of it.
 Actually, we don't have time to go into most of it.
 I just hope to kind of point out what the main arguments and themes are in the report,
 kind of what it's trying to say, pick out some interesting things and summarize it to
 the best of my ability.
 Also give a little critique.
 So let me actually go ahead and try to state the kind of core argument that the report
 is trying to make, because it's not really clear from reading it and you have to kind
 of read the whole thing and then kind of becomes clear what the argument is, I feel, though
 they somehow stated in the introduction numerous times in various ways.
 So I might just be not as attentive reader at first time.
 But all right, so here's the argument and I really hope I'm representing this correctly.
 We have a problem currently that sometimes AI systems can exhibit what we usually call
 bias.
 And we don't mean mathematical bias, like bias variance tradeoff.
 We mean bias in a societal sense, let's say bias against certain types of people where
 they shouldn't exist.
 So for example, let me draw an AI system and I'll just draw a little computer screen with
 a little light bulb.
 All right.
 So this is because it's smart, this is an AI system and the AI system and they give
 numerous examples.
 One example they give us for is like face recognition algorithm that is much more accurate
 on faces of white males, as opposed to darker skinned females.
 So let me draw like two curves to represent these distributions are unequal.
 And so the AI system exhibits some bias with respect to some kinds of people with an especially
 protected attributes.
 And in this report, they focus mainly on gender and race.
 So that's what we're going to talk about.
 The second thing they observe, so this observation one, the second thing they observe is, I'm
 going to draw some generic people here that represent the workforce of AI.
 So the AI workforce is classified as all the people that work on AI, be that university
 researchers or within companies building AI products or deploying them.
 So this is the workforce and they observe that there is an unequal distribution among
 the AI workforce.
 So this distribution, I'm also going to do this for unequal distribution.
 There's an unequal distribution in the AI workforce, most notably, it's predominantly
 males who work on AI.
 And also white people are overrepresented compared to the world population at large.
 So that's kind of the two observations they make.
 And now what they claim is that the unequal representation in the workforce is causing
 the bias in the AI systems.
 So they're basically saying these AI systems are biased because that the workforce is unequally
 distributed.
 And also they claim in a less powerful sense, I feel, but they claim there is a loop that
 this then leads back that because there is bias in the AI system, that again leads to
 an unequal, more unequal distribution of the workforce.
 So the core argument really is, as they set out to do, like in the introduction, and also
 claim that they have done in the conclusion, is to demonstrate these two directions here
 in a causal way.
 So the systems are biased because there is an unequal representation in the workforce
 and that feeds back.
 So the argument is that if you want to fix the bias here, if you want to fix that, then
 you will have to fix it via making the workforce more what they call diverse, so less unilaterally
 distributed towards white males.
 That's kind of the final conclusion.
 If you read their report and the recommendations, that's mainly what they're going for.
 Yeah, so my opinion, or in my opinion, having read the report a couple of times, is that
 as I see it, they really don't demonstrate these links.
 So they give examples of this and they give examples of this.
 They show that the workforce is unequally distributed.
 They show that AI systems can exhibit such bias, but they never actually show these links
 in my opinion.
 They don't show this.
 So if you make the claim that in order to fix the bias in AI systems, you must fix the
 unequal representation in the workforce, I would need an argument that says because there
 is unequal representation, therefore A, therefore B, therefore C, therefore bias, like an actual
 argument to follow that says because of this, that, because of that, that, and so on.
 It's just not there.
 They simply show parallels.
 They simply show that these two things exist and they just list example after example of
 that.
 I don't think they make this argument.
 But I think, also the other direction, they don't really make this argument.
 Except in one case, where if you give them benefit of the doubt.
 What I also think is that it appears like the article, if you read it, and I encourage
 you to read it if you have some time, it makes a lot of sense if you have already accepted
 this conclusion.
 Like if you've already accepted this, then it's like, oh yeah, because I feel this is
 just a text where the confirmation bias is so high, just the way it's written, that it
 must make a lot of sense to someone who's already kind of in on this conclusion.
 But to someone who isn't sold yet, like myself, I am just not finding this convincing at all.
 The second thing is that it very much feels like this isn't like a discovery or something.
 But someone actually set out with the goal to address this here with the goal of I want
 companies to hire more of these people or certain kinds of people or to become more
 diverse or to promote more of a certain type of people.
 And now I'm going to find reasons for this.
 And the reason is like, oh, look at look at this bias here.
 This is caused.
 This is caused by this other thing.
 And therefore we must fix this other thing.
 It very much feels like someone setting out with already the conclusion in mind rather
 than this being an honest investigation.
 But yeah, I mean, read it for yourself.
 I can't prove the absence of an argument by not reading every single line.
 And I can't read every single line because it'll just get very long and boring.
 But read it yourself.
 And I think I'm pretty I'm pretty I've read it numerous times with really an open mind
 to be convinced that there is an argument in there.
 But I don't think there is or I don't think there is a very strong argument for this.
 All right.
 Let this first part here is more or less a summary.
 So research findings is more or less a summary.
 And we'll get to these things as they are important.
 Then they state recommendations right at the beginning.
 So actually, you'd have to read the article first.
 This is kind of more of an abstract section.
 But since it's right here, we'll kind of jump right into it.
 So these are recommendations and I've claimed they don't really show a connection.
 But they actually just show examples, examples of this and examples of this and parallel
 them.
 And this is reflected in like every single section, including here in the recommendations.
 They have recommendations for improving workplace diversity.
 And they have recommendations for addressing bias and discrimination in AI systems.
 Right.
 So all right, in my case, if you make this argument, I would I would feel you also make
 recommendations for breaking these links.
 But or argue why they can't be broken.
 But all right, let's jump into some of them.
 And it is really a mixed bag here, really.
 So some recommendations I'm really in favor of just from from the go not even you don't
 even need the article for those here.
 Discrimination, harassment and discrimination, transparency reports, including number of
 claims over time, the types of claims submitted and actions taken.
 So it's known that especially in these larger companies, sexual harassment claims often
 go down in either bureaucracy or are kind of hushed under the table or something like
 this.
 What you have to recognize is that a human resource department of a large company isn't
 there to serve the human resources.
 It's there to serve the company providing human resources.
 That's why a sexual harassment claim to an HR department is just a potential lawsuit.
 And that's why they don't want to take it seriously except for it must go away really
 quickly.
 So I think to kind of force companies or to ask companies to be more transparent, to take
 more seriously these the accusations of sexual harassment and assault and also discrimination
 is a very valuable goal.
 And I fully, fully support this.
 Also the here commit to transparency around hiring practices, especially hiring regarding
 how candidates are leveled, compensated and promoted.
 But also the larger the company gets, the less transparent this process usually becomes
 or the more bureaucratic, the more people are able to game it and so on and distort
 it.
 So I feel it's always good to be transparent around, okay, this person provides this much
 value to the company, therefore they should be compensated according to that or at least
 be transparent about it.
 So these are kind of recommendations I like.
 Then recommendations that really go into a different direction is something like this
 here, change hiring practices to maximize diversity.
 And this is kind of reflect, I'm not going to go on this reflected in other points, increase
 the number of people of color, women and other underrepresented groups at senior leadership
 levels of AI companies across all departments.
 So these things, they are usually within like company diversity goals and so on, doesn't
 really say how to do it.
 But then the I mean, as such, they're not really recommendations yet.
 They're more like goals.
 But here recommendation seven, I think is the the crucial one, ensure executive incentive
 structures are tied to increases in hiring and retention of underrepresented groups.
 So this is it's a bit of coded language.
 But here they talk about executive incentive structure tied to hiring and retention of
 underrepresented groups.
 This basically means if you are a manager or someone in charge of hiring or promoting,
 and you hire or promote a underrepresented person, and since they're talking about gender
 and race here, if you that means if you hire or promote a person of color or a woman, in
 this case, you will be compensated more.
 So at the end of the year, you'll somehow have more money, like more bonuses or more
 base comp or more equity or something like you'll get more money.
 So this, this recommendation is a direct call to hire based on race and gender.
 So this, this is a direct call to racist and sexist hiring basically to discriminate people
 according to their skin color and according to their gender, which I mean, how, how is
 this okay with anyone?
 Like how can anyone how are people even able to state this and in like a high profile report
 like this and get away with it and not have people criticize them, this directly calls
 for people to be treated according to their gender and race.
 And probably as directly as you can go without getting into actual legal trouble.
 But yeah, I'm really, really against such such practices.
 I mean, yeah, that's I just I just don't know how this how this can ever how this can ever
 be thought of as a good thing by anyone.
 All right, so, well, yeah, in my mind, this recommendation, and this recommendation kind
 of are counter to each other.
 Because if if I commit to transparency, how people are okay now I can, I can transparently
 commit to to be racist, I guess.
 But if I say, okay, I'm going to come and promote people based on how much value they
 provide to the company, then yeah, I'd much rather have that than saying I'm going to
 come and promote people based on their skin color.
 Alright, so let's actually jump into the report.
 I'm not gonna these recommendations for addressing bias and discrimination in systems this these
 are fairly general and common.
 So as well, as I said, we'll jump most of the things in the report.
 So introduction.
 So they start out with there is a diversity crisis in the AI industry.
 This they give like some numbers like 15% of AI research staff and 10% at Google, so
 15% of Facebook are women.
 So these are some kind of fairly known statistics about how the AI field is kind of gender and
 race skewed.
 Currently, so they say they claim in bold the diversity problem is not just about women.
 It's about gender, race, and most fundamentally about power.
 It affects how companies work, what products get built, who they're designed to serve,
 and who benefits from their development.
 So this, I find this, this, this word power and this notion of power, a lot in this report,
 it appears again and again and again in in like power dynamics and power dynamics among
 groups.
 It's like a worldview, it paints like a worldview, where these different gender and race groups
 kind of struggle against each other to gain power over another.
 And whoever's in power will try to remain in power in alliance with their gender and
 race group and try to keep the other groups down.
 I'm not sure that's the correct view of the world.
 In my mind, the world is comprised of individual people that want to achieve something for
 themselves and they would like to prop themselves up.
 Whereas in this worldview, it's like, I'm going to use the power of my group to keep
 other groups down.
 I don't know which worldview you subscribe to, but I find the world is comprised of individuals.
 Yeah, and this is not discrediting that some people have it harder because of their gender
 or race.
 But to see the entire world as a power struggle between these groups, to me, it's, it's,
 yeah, and I'm not going to point out everywhere it appears, this power wording, but it appears
 a lot and it's really shapes how the report reads.
 You have to, you have to kind of remember, if you're a white male, and currently, the
 field is comprised of 90% white males, you, if you have like 10, like 10 hours, let's
 say you have to have 10 hours to do something, right, you can either choose to put down some
 other groups, like put down groups that you're not part of, or you can choose to invest these
 10 hours in putting up yourself, you, right.
 So if, if I, like I profit, if I'm a white male, I profit minimally from keeping the
 other groups down because guess what, I still have to compete with the like 1 billion other
 white males there are.
 It's not going to help me to keep down anyone else, and especially, like it's, it's moronic,
 like who does that, who like has alliance, except most fringe people, like to their race
 or gender, rather than to the people they admire and respect and like to work with.
 So I'm going to, if I have like 10 hours today, I'm going to rather spend this in propping
 up myself compared to everyone else, and I don't care what gender or race they are.
 And so that to me, that's a much more accurate or, I don't know, plausible worldview.
 But just be aware that this report really takes on the language of kind of groups and
 power between groups and groups trying to, you know, kind of gain power and keep in,
 keep power and keep others from having power.
 All right, so say, to date, the diversity problems of the industry and the issues of
 bias in the systems it builds have tended to be considered separately.
 We suggest that these are two versions of the same problem.
 Issues of discrimination in the workforce and in system buildings are deeply intertwined.
 Challenge, and moreover, tackling the challenges of bias within technical systems requires
 addressing workforce diversity and vice versa.
 So the, I think this, this here actually is like how I described the argument and they
 kind of restated multiple times in a bit different way.
 But I think this is the core.
 And I really think I'm not misrepresenting the article here in that this is what they
 are setting out to do.
 They're setting out to say, okay, the diversity, the kind of unequal representation in the
 workforce and the bias in some AI systems are causally linked to each other and tackling
 one requires tackling the other.
 So yeah, if I'm misrepresenting them, let me know, but I really think I'm accurately
 representing their argument.
 So what they, what they do, as I said, is they give examples of one and of the other
 and also they really, they're really on kind of discrediting the kind of issues to solve
 problems of bias in a different way.
 So they point a little bit to this here in the introduction.
 They say in the face of growing evidence, the AI research community and the industry
 producing our products have begun addressing the problem of bias by building on a body
 of work of fairness, accountability and transparency.
 So fairness, accountability and transparency research concerns these issues.
 For one is research showing that some products are unfair or untransparent and so on.
 On the other hand, it's trying to devise algorithms that are more fair according to some notions
 or more accountable and transparent, which means that the algorithm can kind of say why
 it made a certain decision rather than it being a deep learning system that you don't
 really have an insight.
 These fields are active fields of research, definitely very interesting to look into.
 So but they, they kind of, it is not already here, but they say, yeah, we have adjusting
 AI systems that produce a result deemed fair by one of various mathematical definitions.
 You can already see in the language here, they don't really like this research and they
 are trying in this report to kind of discredit it or at least claim that it doesn't solve
 the whole problem because their point is, of course, you have to address this diversity
 issue in the workforce in order to fix the problems.
 So to this, I just want to say no, like if you can, I mean, you can criticize the fairness
 and accountability and transparency research field in that they haven't solved the problem
 fully yet.
 But in principle, if I have an algorithm, if I'm being delivered an algorithm, right,
 and the fairness literature has been applied to that algorithm and someone tells me, I
 guarantee you here is a proof, the algorithm is fair, right, then I really don't care who
 made that algorithm.
 As long as it's fair, the problem is fixed.
 If the bias is gone, the problem is fixed.
 And I don't care who fix it.
 I don't care if the person who fixed it is black or white or purple.
 Then the problem is fixed.
 And they, they really have to, they really try to just make the counter argument here
 is that no, that's it's not enough.
 But I claim yes, it, if you can actually solve the fairness problem, technically, then you
 have solved the fairness problem.
 Yeah, the only thing you can do is claim that it is not good enough yet, but not that it's
 fun to they kind of have to make the argument that it's fundamentally flawed approach.
 And I don't think they succeed in doing that here.
 Um, yeah, so they go on to say, we should expand to consider not only how I tools can
 be biased technically, but how they're shaped by the environments in which you're built
 in and the people that built them.
 Again, this this focus like who builds the AI system, I don't care, I care what it does,
 right?
 As much as if, if I hear an argument for or against something, I don't care who makes
 the argument, right?
 I care what the argument says.
 This is, it's like an ad hominem attack for an entire community.
 That's kind of how this this article, this report shows, or is appears to me.
 So they say, currently, large scale AI systems are developed almost exclusively in a handful
 of technology companies and a small set of elite university laboratories spaces that
 in the West tend to be extremely white, affluent, technically oriented and male.
 So yeah, their their problem, that's their fundamental problem here that these these
 spaces are skewed in one direction.
 Interestingly enough, their problem is not so much that it's that they're all in the
 same place, right?
 That they all live like 20 miles from each other in around San Francisco.
 That's that seems to be not a problem at all, as long as we get to like enough people of
 color and women into these 20 miles.
 But yeah, so that that's pointing out the the problem here or the yeah, kind of issue
 they have.
 All right, so they go on.
 Just kind of want to highlight again, they say both within the spaces where AI is being
 created and the logic of how AI systems are being designed.
 So paralleling the two things, the cost of bias, harassment and discrimination are born
 by the same people, gender minorities, people of color, other underrepresented groups.
 And they also say similarly, the benefits of such systems from profit to efficiency,
 accrue primarily to those are already in positions of power tend to be white, educated and male.
 So they again, they say the this points to a systematic relationship between patterns
 of exclusion within the field of AI and the industry driving its production on the one
 hand and the biases that manifest in the logics and applications of the technologies on the
 other.
 And they try to make this connection because they say the cost and the benefit of these
 two things are overlap in the people that where it costs and it benefits.
 And I really, again, it's just a parallel, but I really even don't think that's true
 because they kind of, they kind of argue against themselves later.
 So they always say, we have to look at again, they shoot against the take much more than
 the technically driven problem solving.
 They point to this.
 So our research requires looking at gender and racist categories within which humans
 think in short, sorry, studies of discriminatory systems, we need to ask who is harmed, who
 benefits, who gets to decide.
 So it's kind of who bears the cost, who bears the benefits and who has the power.
 So that's the, and again, it's we seek to understand how AI disadvantages some, we also
 consider how it works to the advantage of others.
 So keep that in mind.
 That's kind of the lens through how they analyze the this thing again, one that acknowledges
 power relationships and centers equity and justice.
 That's the, they want to see this bigger picture.
 So that's yeah, keep, again, keep that in mind.
 So they go into a section called which humans are in the loop, how workforces and AI systems
 interact.
 So this kind of from the title of this section, you think, okay, here's where we get in.
 Here's where we make the argument.
 And they start by listing examples of how AI systems can be discriminatory.
 And first, they go into an example of Amazon had developed an experimental hiring tool
 to help rank job candidates.
 By learning from its past reference preferences, Amazon hoped that the resume scanning tool
 will be able to efficiently identify qualified applicants, comparing their applications
 to previous hires.
 The system quickly began to downgrade resumes from candidates who attended all women's
 colleges along with any resumes that included the word women's.
 After uncovering this bias, Amazon engineers tried to fix the problem by directing the
 system to treat these terms in a neutral manner.
 The company eventually abandoned the tool when they were unable to ensure that the algorithm
 would not be biased against women.
 Gender based discrimination was built too deeply within the system and in Amazon's past
 hiring practices to be uprooted using a purely technical approach.
 So this just the way is written, I find to be quite dishonest.
 But let's analyze what happened here.
 So their final claim is that gender based discrimination was built too deeply within
 the system to be uprooted using a purely technical approach.
 So this is one of their arguments.
 They say technical approaches, they don't help because the Amazon engineers tried to
 fix the problem.
 But when they were unable to ensure that the algorithm would not be biased against women.
 So if you read this, you really I mean, I really get the impression that's not what
 happened here.
 What happened here most probably is Amazon built this tool, okay, and it fed in its past
 hires and we know of issues of like data set bias bias inherent in data set.
 So if your data set is skewed, the AI tends to pick up on the skewed data set and become
 skewed itself.
 Okay, so I actually would argue that most or all of the examples they stayed in here
 are examples of such biased data sets and not.
 So the the cause of the bias is the data set that they are strained on and not the person
 that ran the code or built the algorithm to train it on or built the deployment.
 And so but it doesn't matter you're a you're Amazon, you built this tool and you realize,
 oh, it discriminates against people having women's on their CV.
 So this is a pretty bad PR wise.
 So you tell your engineers engineers fix the problem.
 So the engineers go fix the problem, they come back and say, okay, we fixed the problem.
 And then what you do is you say, okay, engineers, can you ensure me that the algorithm would
 not be biased against women?
 Because if only the slightest bias exists, if only it doesn't even have to be if one
 journalist finds one example, where there is a down rank, because I add the word women's,
 then we are screwed, right?
 And the engineers will say, No, we can't guarantee that it's a deep learning system or something,
 right?
 We, we can't like give you a proof that it's not biased.
 If you're a smart executive, at that point, you'll scrap the tool, because the potential
 PR downside are just huge.
 And probably they've also realized it's not that handy to have this, this tool compared
 to their recruiters doing their job, because their recruiters might actually be good and
 have been doing this for a while.
 So to the to the fact that this tool was scrapped is probably much more a result of a PR disaster.
 But also independent of that to say gender based discrimination, sorry, gender based
 discrimination was built too deeply within the system to be uprooted using a purely technical
 approach.
 It's just I mean, what is what is this?
 This is just trying to discredit this kind of technical, technical going about solving
 this problem.
 I'm pretty sure if someone comes to me and says here, I have this tool, and I can mathematically
 prove to you that it's not biased, then it's not then the problem is solved.
 And also, I really don't see how the person training the algorithm, or the person researching
 such an algorithm has any influence over how the algorithm works, because they're not the
 ones making the data set, or if they are, yeah, then they can make a better data set.
 Also, if a person comes and makes a better data set, that will fix the problem.
 And it doesn't matter what skin color the person has that makes the better data set.
 So all of this, this link is just not demonstrated here, or anywhere here at all.
 But this this here is the closest Amazon that this report actually comes to making this
 point.
 And I said before, I drew that drew this thing workforce AI bias, right?
 So this this link since it here the AI system is used for hiring the workforce.
 So at least one could make a claim that this link is somewhat demonstrated.
 But I this it's a weak case, I would agree, but this is the closest they come.
 So that and but then to go this direction, you have to somehow argue, well, the workforce
 somehow makes the AI system bias, no, the workforce influences the data set.
 If the AI is trained, so if a hiring AI, how do you train a hiring AI, you optimally train
 it on the performance.
 So this this employee here is going to have a performance over time, right?
 And the AI system will look at that performance over time.
 So if the AI system even if it's initially biased, because it learns from the risk recruiters,
 it will learn that, okay, actually, if I always forgo these women, then I don't get as much
 performance of a workforce, so I should correct for that.
 So if you train the AI system on a good metric, then then then this problem will leave even
 out itself.
 But again, this Yeah, this this is this could be considered like one point in the argument,
 but I think it's a very weak point.
 And only because the AI system is actually used for hiring, where I think the point they're
 making is a much larger one is the general bias in the AI systems contributes to the
 workforce imbalances.
 And there you somehow have to say that, okay, the AI system somehow influences society at
 large and society at large then go leads to the workforce being skewed.
 I don't Yeah, that it's just not strong enough, in my opinion.
 And the other direction also isn't isn't strong here.
 But again, the examples only get weaker from here on.
 They go on to say, this is just one of many examples that show how the functional logics
 of a given technology echo the gender and racial dynamics of the industry that produced
 it here.
 Yeah, this, that's the claim they're making to echo the gender and racial dynamics.
 And they're actually making a stronger claim, namely a causal claim.
 They give the other example of the Amazon's recognition facial analysis service previously
 demonstrated gender and racial biases worse than those of comparable tools.
 So it failed to see dark skinned women while being most proficient at detecting likes light
 skinned men.
 And they later go into this example again, where they basically also state yes, this
 is an issue of the data set, the data set being much more comprised of white men.
 And they say, but then they have to kind of make the turnaround argument and say, well,
 the data set is a reflection of society and society, you know, part of society is the
 workforce.
 And it's just not, I mean, it's again, this argument only works if you already believe
 the conclusion.
 Otherwise, there's actually no argument there or no solid one.
 But what they do here is they say Amazon's initial response to such criticism has been
 to try and discredit the research behind it.
 This reaction, or let's let's first discuss this.
 So the Amazon, yeah, Amazon, of course, being the accused here and a multi billion dollar
 company and the criticism is something that is PR wise very bad for them.
 They discredit the research tried to discredit the research behind it.
 It's understandable that this could be dishonest from Amazon side, right?
 I mean, they're getting attacked.
 It's like, you know, the tobacco companies trying to discredit the smoking research,
 but still, I mean, that doesn't mean it's wrong.
 It could actually be bad research, right?
 You have to actually go and look at what's Amazon saying, what is the research really
 doing?
 Is Amazon right or wrong?
 Completely open that Amazon is wrong here, but you still have to go look.
 And this citation here, I've tried this citation here.
 This one isn't to a to Amazon's response.
 It's to like a medium article and the medium article doesn't even include Amazon's response.
 I've looked, maybe I haven't seen it.
 It doesn't also doesn't link Amazon's response.
 Maybe it links something that links something or that includes it in some way.
 But basically this medium article only states, yeah, Amazon has been denying this or Amazon
 has been critical of this.
 And if you state such a sentence, Amazon's initial response to such criticism has been
 to try and discredit the research behind it.
 I at least expect the citation to lead me to Amazon's response so that I can verify what
 they're saying.
 Right.
 So this, I mean, I don't know, willing to chalk it up to incompetence rather than malice.
 Right, but then they go on and they say this reaction is evidence of the wider problem.
 The research was conducted by two well-regarded AI researchers who are women of color.
 By attempting to publicly discredit their expertise and research methods, Amazon is
 reinforcing the same kinds of prejudice and derasers that the research critiques.
 Yeah, here you go straight to the identity of the researchers.
 Like play the race card straight out.
 I mean, this is maximum dishonesty, right?
 Except if Amazon said something like, well, these women of color, clearly because they're
 women of color, they have no idea what they're doing or something like this.
 This is basically it's coded language for saying either saying you're not allowed to
 criticize people of color because they're a minority or you're basically saying Amazon
 is racist and that's why they criticize them.
 They just don't take them seriously because they're women of color.
 I mean, both are both are abhorrent.
 This is just dishonesty really stated here too.
 I mean, again, I'm perfectly willing to accept that Amazon's critique of this research is
 wrong and is not well intended because they're the ones attacked, but you still have to examine
 it rather than say, well, they shoot against women of color and therefore somehow that
 makes their counter argument irrelevant or even racist or something.
 That's I don't know.
 I find this dishonest.
 Yeah, I don't know about you.
 Moving on.
 So they go on and state a number of examples of bias and discrimination in the workforce
 and they a lot of times they make a mixture of the gender and race imbalance in workforce
 and things like sexual harassment not being taken seriously by the companies and also
 the things like gender or race pay gaps, which I'm open to accept that these things exist
 and are even intertwined.
 But just to tell you what's happening because we're kind of skipping but it's kind of a
 mixture of these things.
 So they say these issues are systemic.
 There's a close relationship between these workplaces with discriminatory practices and
 discriminatory tools, a feedback loop that is shaping the industry and its tools.
 So again here to state, I think I've stated it enough now that or demonstrated enough
 that I'm really representing their arguments as they intended it to namely that there is
 this kind of causal links and loop between these two things.
 And they shoot against the fairness literature by saying from this perspective, locating
 individual biases within given technical systems and attempting to fix them by tweaking the
 system becomes an exercise in futility.
 Only by examining discrimination through the lens of social logics, who it benefits, who
 it harms and how can we see the workings of these systems in the context of existing power
 relationships.
 So they say these issues aren't technically fixing these systems won't help.
 If that's the problem.
 And I agree, if that causal link actually exists, then technically fixing the system
 might not solve the problem.
 Not even sure.
 I mean, if you technically fix a system like this, then you technically break the causal
 link and thereby fix the problem.
 I would not sure, but again, this is based on the hypothesis that they've already reached,
 like demonstrated their, their conclusion, which they haven't and which they are not
 in the entire article.
 Yeah, so the next section goes into who makes AI so I don't know about you, but this section
 was titled how workforces and AI systems interact.
 And apart from one, the AI system being used for hiring the workforce, which is said this
 one instance where actually there could be one causal direction from bias to different
 misrepresentation the workforce.
 Other than that, there isn't really anything in there that really shows how these two interact,
 especially in a in a causal way.
 Alright, the next section is called who makes AI is broadly about the about the gender and
 race imbalances or miss not unequal representation in the workforce.
 And we're going to skip this diversity statistics that kind of that discuss that diversity statistics
 of companies aren't really accurate, or can be, you know, massaged kind of by the companies,
 which you know, is true.
 Definitely companies will always try to maximize their profits.
 And even if they give out such a report, so that definitely critical thinking is in order.
 Alright, so the next section is called the discrimination feedback loop.
 Right, if so if in the earlier section, you felt like here we go into the meat, then you
 must feel with this title, like, okay, we're actually going to see how this loop works
 and how the two things are really linked, like how one causes the other and vice versa.
 So let's jump in.
 They say AI systems increasingly play a role in our social and political institutions,
 including education, healthcare, hiring, criminal justice.
 Yes, therefore, we need to consider the relationship between the workplace diversity crisis and
 the problems with bias and discrimination in AI systems.
 No, why I don't see how therefore, but yeah, so I don't see how therefore we need to consider
 the relationship.
 Okay, if there is a relationship, we need to consider whether there's a relationship.
 Okay, granted.
 So they say fairness, accountability and transparency research is playing an emerging role.
 Now what they mean here is the aspect of fairness, accountability and transparency research that
 shows that there is a problem.
 So I told you there's two sides, one side is showing there is a problem in current systems
 and the other side is trying to fix them.
 So they're very much fans of the side that shows that there is a problem and they use
 show some of these problems here, we've already seen some but they show some more like Facebook's
 ad delivery systems let users to be shown as for housing and employment in a discriminatory
 manner.
 So giving 2019 study found significant racial bias in a widely used commercial algorithm
 used to determine whether patients will be enrolled in care management programs.
 So these are these are just examples of these AI systems being biased.
 So they go into this say taking a contextualized view may enable more extensive account and
 the contextualized view they when they say this they mean anything more than just a technical
 approach at solving these problems.
 More extensive account of bias to emerge future work could examine the politics of system
 design study how AI systems in situated reality and study AI systems in situated realities
 ask why a system was designed in a particular way, how it was constructed, whose interest
 it shaped shaped by the metrics in which its success or failure is assessed, rather than
 solely focusing on improving existing data sets or individual algorithms.
 Yeah, I agree.
 I mean, we always have to we always have to pay attention to these things, especially
 like looking at the metrics by which its success or failure is assessed.
 But a lot of times this is this is rather straightforward in kind of if you look at
 the metric, the metric most often, especially in commercial applications is money, right?
 So the metric of like an ad showing system, like if I have a system to recommend ads to
 people, show people ads and personalize them and so on, I simply want to maximize my revenue.
 So I want to sell someone something.
 And everything I want to know is how likely is it that person is going to buy that thing?
 Right?
 I that's basically Yeah.
 So in essence, sometimes it's really valuable to consider what capitalism is.
 So in capitalism in so capitalism, these kind of this system we're working on is kind of
 a form of limited capitalism, but mostly mostly capitalism.
 And capitalism is very greedy.
 So capitalism, all corporations want to do basically is make money.
 And that is and on the other side, you have discrimination.
 So discrimination meaning these unequal represent like unequal distribution actively.
 So and often sometimes these go hand in hand, sometimes you can make more money by discriminating
 against a certain type of people.
 And that's, that's a really bad scenario.
 Like that's a very, like, this is really something where we need to take action.
 But a lot of times, a lot of times, these two things stand in opposition to each other.
 So little arrow here, non compatible.
 That means if I want to sell someone something, then I maximize my profit by not caring by
 accurately assessing how likely is it that person buys that thing.
 If I want to discriminate here, if I want to discriminate, start discriminating, according
 to skin color saying like, No, I don't like that this person with the skin color is able
 to buy this product, I want to kind of keep them down, and so on, then I forgo profit,
 right, then I actually, even though this person could buy this thing, I forego that.
 So often these things are in direct opposition to each other.
 Also, if I am in charge of hiring, and I don't like people of a certain gender, but they
 would actually be really, really good, whatever, good employees.
 So I forgo that, that means I'm getting a pay more for less qualified people just because
 I'm biased and I'm down ranking unjustifiably, these people of the gender I don't like.
 So oftentimes, you have to ask yourself, are people fundamentally greedy, or discriminatory?
 Which are they more?
 If push comes to shove, would they rather have more money?
 Or would they rather keep their own race and gender group in power?
 And with just, yeah, so the and you have to ask this of corporations, you have to ask
 this of people.
 And in my experience and view, like people are much, much more greedy than they are willing
 to discriminate and give up money for discrimination.
 And so if we look at metrics by which success or failure of AI systems are designed, then
 I would argue a lot of the times metrics are actually profit incentives.
 And especially if we look at data set construction, if there is a skewed data set that makes my
 AI system be biased, that actually loses me money and the company would profit a lot from
 building a better data set.
 So looking at kind of metrics actually makes a lot of sense to me and very much in favor
 of that.
 And I think by designing accurate metrics and then getting the best possible information,
 the best possible data sets to maximize these metrics will oftentimes actually eliminate
 such forms of discrimination.
 Again, there are situations where they don't, we have to be very cognizant of these.
 They go into this and they say, also examine more thoroughly how societal discrimination
 surfaces in data provenance, examining the history and process of data set construction
 and considering how cultural norms and stereotypes were enumerated and represented at the time
 of data creation.
 This is a big issue.
 Yes.
 The data set construction kind of at the time of data creation and so on, this is a big
 issue in these systems and a lot of bias.
 And I would argue most of the bias we've seen here arises from corrupt data sets and from
 data sets that were constructed in an already biased way.
 And the AI system trained on these data sets simply replicates this bias.
 So I think that's very correct here.
 They go into this example, they say the labeled faces in the wild data set contains over 15,000
 images.
 Only 7% of images are of black people.
 This is because these, the media landscape of the early 2000s, these images were gathered
 from the news media at the time, predominantly featured white men in positions of celebrity
 and power.
 This exactly.
 So if you train a system on this data set, the system will inherit this bias.
 Yeah, so this is a classic example of a corrupt data set.
 Also this isn't only with race and gender.
 This is also if you like take pictures from IMDB, yes, a lot of this currently Celeb A
 data set that is used in all the GAN research is collected from IMDB.
 You probably have overly beautiful, like pretty face people on there.
 So that your AI system, your generative model is only going to produce mostly pretty face
 people, since movie stars tend to be a lot prettier than the average humans.
 So that the kind of data set construction process, I think is currently the biggest
 source of bias in AI.
 But that also, it's interesting that they go into this here and they kind of want to
 make the point that this is because society and power in society, the data set reflects
 that.
 But I would argue if someone makes a data set that doesn't have this bias, then the
 problem is solved.
 And I don't care who makes the data set.
 So the link between the workforce and the bias is really broken by an argument like
 this, because as soon as we have a correct data set, an unbiased data set, we can mitigate
 the bias.
 And they even go, they go into this here.
 They say, sorry.
 Yeah, they say down here.
 They say these people, these researchers have looked at these facial recognition systems
 and they assessed this what we saw earlier, higher error rates for darker skinned women
 than for any other group, lowest error rates for light skinned men.
 To measure this disparity, these researchers developed a new data set that is more balanced,
 both in terms of gender and skin color.
 Good.
 Problem, like make a larger data set to actually train on and then problem solved.
 And I don't care at all what race and what gender these people are.
 Well done.
 Good people make a good data set like this.
 And then we've solved the problem.
 What's the problem here?
 Why would you ever care what these people look like if they do good work?
 That's to me, this actually breaks their own argument.
 I don't know why they included here.
 To me that to then suggest that there is a link to the workforces, if here is obvious
 that if you fix the data set, you can fix the recognition system.
 All right, so we'll go on here, jump a couple more paragraphs.
 Except when they say they shoot again against this kind of say to this point, a focus on
 fixing technical systems in isolation without examining their broader context of use and
 power and dynamics that attends issues is not limited in its intervention, it can actively
 cause harm.
 So if you fix the problem in a technical manner, they argue here it can actively cause harm.
 And the example they give is that facial and image recognition systems, they are often
 applied in service of police surveillance, which disproportionately harms poor people
 and communities of color.
 So there's a quote from this person that says, is this not social progress to make black
 people equally visible to software that will inevitably be further weaponized against us?
 We are considered criminal and more surveillable by orders of magnitude.
 Whatever claim to a right of privacy that we may have is diminished by a state that
 believes we must always be watched and seen.
 So this is an example where by improving the facial recognition for black people, it makes
 the police better at surveilling them, which is true.
 And then it is an ethical problem that the police is able to use these facial recognition
 systems to surveil people.
 That's a massive privacy problem.
 That's a massive problem in how much the state is allowed to overreach and so on.
 So I think it's a discussion in itself, but here they argue because at the very beginning
 I asked you to remember this whole notion of we always have to look at who benefits
 from the way the AI system is constructed, who is harmed from that, who benefits from
 how the metrics are shaped and so on.
 In this case, we actually have a perfect example where if the face recognition system is very
 inaccurate for black people's faces, that actually helps them in the societal context.
 So by logic of this report here, that must mean that somehow the bias works for them
 and thereby the system is good or something like this.
 And by fixing it, you actually make it worse.
 Yeah, they say it can actively cause harm.
 So I think this is pretty much arguing against themselves earlier where they say, oh, we
 always have to look at who benefits from the system.
 Yeah, here, if the face recognition system can't recognize you, you actually benefit.
 So I don't think that argument works in any case except if you only look at it when you
 want to look at it.
 All right, so we're going to jump a couple of sections here.
 But the core thing here was the feedback loop.
 And again, the feedback loop isn't demonstrated at all here.
 Just examples of systems that are biased and of data sets that are biased, because of data
 sets that are biased.
 But there's no demonstration of how the workforce, I mean, yeah, just take this previous argument.
 So the workforce is supposedly supremely white.
 And it makes a face recognition system that makes that is performing poorly for darker
 skinned people.
 And that actually in this context of police surveillance helps the darker skinned people
 compared to the lighter skinned people.
 So that kind of is an exact counterexample to the argument that this misrepresentation
 in the workforce leads to the biases in the system.
 If we interpret it through the lens, who it costs and who it benefits.
 All right.
 So the next section is corporate diversity beyond the pipeline problem.
 And this is kind of an odd inclusion when I read it first to interpret to go against
 the pipeline problem here.
 But it kind of makes sense if you know what these people set out to do.
 So what these people set out to do is to argue we must fix the workforce, right?
 We must fix the, we must hire more people of color, more women and so on, promote them
 more.
 And they have a very much have a problem with this pipeline argument.
 What the pipeline argument is, is the following.
 So at the beginning, if you consider like the educational or career paths of people,
 then you have like 100% of people that's represented at this at the beginning, and then most of
 these people go through school.
 So most of these go on.
 This is kind of the area in here is the population.
 And then some of them pursue higher education like some drop out.
 So this gets a smaller amount.
 So this is here, this is time and this is kind of volume of people.
 And then very few go into computer science, right?
 And then even fewer go into AI.
 So what you end up is just a tiny sliver of people that actually go into AI.
 So this is called a pipeline, and we have various junctions here like where you would
 go into higher education, where you would choose your major in university, where you
 would go into a subfield of computer science, where the kind of volume of people drops significantly
 from one point to the other.
 And now if you compare this, if you compare this and use it say, we're not considered
 all of society, but here over here we'll call consider all just men and over here we'll
 consider all women again, they all go to high school and then university and then maybe
 very few go to CS, even fewer go to AI.
 What you'll find is, and I've drawn it maybe wrong here, is that this is smaller than this.
 So if you comparatively look at how many males end up in the AI field, you will find that
 fewer end up in more and will end up in our field than women.
 If you comparatively look at it.
 So at and this is over time, like at the beginning, you have 5050 main women distribution in society,
 almost I guess, I think slightly more boys are born, but I could be wrong about this.
 And then as you go through time here, excuse that I believe.
 So you go through high school and let's just assume like high school is still kind of equal,
 it depends on the country.
 Then you go to university, where there's actually more women at university slightly.
 And then you go into computer science and in computer science, and this is just relative
 here, that's why I kind of norm it at 100%.
 Otherwise these things would go down all of them at the same time.
 But comparatively, you have then much more men than women in computer science.
 And then if you see who chooses AI, I don't know if there's any statistics of specifically
 choosing AI from computer science.
 I'm just going to assume that remains the same.
 So if you look into the AI field, kind of this, this will stay the same.
 So in the AI field, you have much more men than women.
 And presumably, because you already have much more men than women choosing computer science
 as their major or choosing any technical field as their major.
 This is kind of the so called pipeline argument.
 So where do AI companies hiring come in?
 AI companies come in here, they hire at this point, after your university degree, presumably.
 There's exceptions, but just say they hire after your university degree.
 And therefore, they basically have to choose from this distribution.
 And if they just say, okay, we'll just take the top, I don't know, 10% people will hire
 the good people of this, we don't care what gender they are.
 Right, so the top 10% here, the top 10% here, then this will end up being the same distribution
 as you have graduates.
 Right, so this is kind of the company, company hiring from an let's say an 80 20 distribution
 without looking at gender will end up with an 80 20 distribution.
 That's the pipeline argument of companies.
 And they don't like the pipeline argument, because the pipeline argument basically says
 that the problem is somewhere here, right?
 The problem isn't the company's hiring wrongly.
 The problem isn't that the company's here, deselected, the problem is somewhere here.
 And because they want to make the argument that the company should hire in a different
 way, they can't have that.
 So they argue against it.
 Now to argue against this would actually be very easy.
 If this argument were wrong, like they claim the argument is is is not good, the pipeline
 argument isn't good.
 If the pipeline argument were wrong, what you'd have to do is you would have to say,
 you would have to say, hey, companies, look at that.
 In your company, you have an 80 20 distribution men to women, right?
 That's pretty unequal.
 And you know, in university graduates, the pool you choose from is actually 5050.
 So obviously, you're engaged in discriminatory hiring, because you know, the pool is 5050.
 There's no reason why it why your hiring practices should cause this inequality.
 And therefore, we can clearly show you do discriminatory hiring, you should stop it,
 you should definitely hire more women and people of color, more of these more of the
 minorities, because your hiring practices are the problem.
 But that's not the case.
 How do I know?
 Because if it were the case, they would simply state this.
 Definitely in this report, if that were the case, that you could actually show with numbers
 that the pipeline argument is wrong, then they would absolutely do this.
 That they have to like, go back and they have to like, ramble around it for several pages,
 which will mostly skip but mainly because this is the case, it is the case that these
 companies hire from a pool of of unequally represented people.
 And the only argument that you can make is that, well, if if you were to equalize this
 here, then maybe here where the problem is that would fix like, so the argument is often
 made if young girls choosing their majors have no one to look up to, like no strong
 women in in corporation CEO roles, they will think that it's not a climate for women and
 they will elect not to go into these fields, which is a valid argument, like I'm completely
 open to that to that argument.
 But it's the only argument you can make.
 And still then, even if you determine this as the cause, I would still not support racist
 and sexist hiring practices like do something else like make them clear that the environment
 can be changed or change the environment, like change the if if it really is the case
 that it's kind of a non anti woman environment, change that.
 If it's just the case that they perceive it as such change the perception, but do not
 engage in discriminatory hiring practices, because there's always someone losing out
 unfairly on these practices.
 And that's, that's something I'm not willing to, to go into, like that's something I'm
 not willing to engage in.
 And I don't think people should engage be engaging in that.
 Actually, that's why it's illegal.
 So let's, let's actually look at very few points.
 This is just why the so they claim they go kind of go over these pipeline studies.
 And they yeah, they say term used in industry to reference the absence of diverse candidates
 in the hiring pool of to justify the inability of large firms to achieve diversity due to
 scarcity.
 Right?
 So that's, they basically agree the of that on the definition that I stated here.
 So the companies that are challenged on their lack of diversity frequently site pipeline
 studies as proof of the persistent challenge of finding enough women and people of color
 to hire.
 Yes, and, and the yeah, but they say but the evidence suggests otherwise.
 For example, in 2016, Facebook chief diversity officer wrote that it has become clear that
 at the most fundamental level, appropriate representation, technology or any other industry
 will depend upon more people having the opportunity to gain necessary skills through the public
 education system.
 Well, yes, that's something I would agree.
 And that's something clearly that addresses this region here.
 Then and where the actual problem is happening.
 So I would say that's a very, very good statement from the Facebook's chief diversity officer.
 They say but as the Center for Investigative Reporting study of tech company diversity
 data found 91 large tech companies headquartered in Silicon Valley managed to hire higher percent
 of black, Latino and multiracial employees than Facebook that year.
 Well, just if other just just because other companies employ racist and sexist hiring
 to improve their diversity numbers doesn't mean that Facebook has to do this.
 Right?
 It it like just because other companies do this doesn't mean that it's a it's a it's
 a good thing to do or that's how you should go about it.
 Facebook simply says like, if we want to hire without being racist or sexist, if we want
 to just hire the best people, then more of the best people have to be in the pipeline,
 like more people have to gain access to educational opportunities so we can then hire them.
 Whereas these other companies probably make a big effort to say, well, even if you are
 not as educated, even if you're not as qualified as this other person will hire you because
 of your skin color.
 I don't think that's that's an argument in that in the favor of what the report is claiming.
 Like I don't think that that is evidence that the pipeline argument is invalid.
 All right, so they go into core themes in pipeline research, and they do some they do
 some overview of the kind of pipeline research that often so sometimes the pipeline research
 examines why, why, for example, why women don't choose to go into computer science as
 much and sometimes they focus on what is their perception of the field, what was it, what
 is their perceptions of the stereotypes of the field, what is their perceptions of the
 kind of culture in the field, is it suited to them, what is their perception of how qualified
 they are for the field, and is that true, is that false, and so on.
 So this research examines a whole variety of things.
 And it's very interesting, actually, to read through this research.
 I want to point out this here.
 Other studies suggest that gender is correlated with a person's motivations for pursuing a
 career in the field.
 Women and particularly women from low socioeconomic status or minority backgrounds are more likely
 to see computing as a versatile profession that provides an opportunity for secure employment,
 higher pay, and better social standing.
 Moreover, their interests go beyond technical aspects of computing, focusing instead on
 the purpose and application of software.
 However, such interests are often de-emphasized in computer science curricula, a price technical
 skill and its applicability to industrial settings above all else.
 So I find this really interesting because it's basically saying that women have different
 interests than men on average.
 That's basically saying that, which is almost heresy.
 To say this in this context, people will come after you if you suggest something like this,
 and yet they're just stating it here.
 Remember this for later.
 This is really funny that they're like, yeah, the interests could be different for women
 than for men.
 And we might have to adjust our curriculum to be more suited to these different interests.
 I mean, yeah.
 I'm sure that's...
 Yeah, as I said, you're like, usually this is forbidden to say.
 All right.
 So they go on.
 They say limitations of pipeline research, right?
 These are fairly like common limitations, let's say, of studies in general, social science
 studies, which I won't go into much.
 Again, they state we have to examine...
 We don't only have to examine this, but the problem...
 They basically say the problem is actually the culture and the problem is actually the
 perpetrators, where do I say?
 I don't remember where this is stated, but they again say we have to examine who benefits
 from its present construction, who is underserved within the current tech ecology, who benefits
 from its present construction, how these dynamics might be untangled, and so on.
 So again, stating these kind of power relationships for the different groups, which I don't agree
 is in large part what's happening.
 They say it's worth considering the scope of these studies and by and large, the recommendations
 they issue are limited, targeted at the administrators of university computer science programs seeking
 to broaden the diversity of their student body.
 Yes, that's exactly where we saw the problem appears to be, right?
 So the reason they have a problem with these studies is that they actually focus on the
 point where this discrepancy appears to happen, because they want to claim that no, no, no,
 you should focus on a different point, namely hiring in these companies, hiring and promotion.
 They say though important, so at least they acknowledge that that's an important problem.
 This is a narrow frame through which potential solutions to barriers to inclusion.
 It does not address the companies that hire computer science students, the peers responsible
 for promulgating stereotype views or engaging in hostile behavior or the broader social
 conditions that may influence students' success in computer science programs.
 Actually the research and even some of the examples they've included of this research
 addresses all of this.
 But the research often addresses the kind of stereotypes and how the peers act and how
 the companies act and also how the companies hire and how people have something to look
 forward to or nothing to look forward to and how that influences their decisions.
 Yeah, again, they say the studies are frequently cited by those within corporate environments
 to justify their own lack of diversity as they situate the locus of change outside of
 the corporation itself.
 As such pipeline studies are disproportionately emphasized as a part of the broader research
 agenda on diversity and technology.
 Again, they state companies use this to get out and of course, like companies, of course
 they're going to use this to get out.
 I mean, I agree at least with that.
 I agree that companies are going to try to use this to get out of responsibility.
 Certainly.
 All right.
 So the last section here is the pipeline dreams after years of research.
 Again this is on this pipeline studies.
 Basically they say the pipeline research hasn't shown, like hasn't borne fruit.
 It hasn't led to meaningful change in the field even though we've researched this.
 The reason they say the number of reasons they tend to place the owners to solve issues
 of discrimination, Silicon Valley on those who are discriminated against rather than
 the perpetrators.
 I find this word choice really interesting.
 Perpetrators, right?
 Like again, the group of white men is trying to put down everyone else.
 That's the perspective that the article takes.
 And it's not even true.
 This research, a lot of times it actually says the reason why, for example, women don't
 choose to go into computer science is the male dominated culture within these corporations,
 is the perception of this not being a woman friendly environment, is the people here of
 sexual harassment and so on.
 So it's not even true.
 But moreover, I just wanted to point out the choice of word here, perpetrators.
 I don't know how you get to this word.
 It really shows kind of a worldview of the authors in my opinion.
 All right.
 So they go on and say, okay, this pipeline studies haven't been beneficial and companies
 haven't done much or hasn't been successful.
 They're going to worker led initiatives, which I'm going to skip here.
 It's just a kind of a reporting of what happened at companies where the workers themselves
 organized.
 And then the last section here is the pushback against diversity.
 So in this section, they're kind of documenting and arguing against people who have basically
 stated counter arguments to their recommendations mainly.
 So their recommendations being, let's change the hiring, let's change the promotion, and
 so on to be based on race and gender.
 And the pushback here characterized in different ways.
 So we'll go through this.
 This is the last section.
 I know it's a long video already.
 If you're still here, like the one person who's still here, hi, I hope you're doing
 well.
 Good.
 Keep hydrated.
 Yeah.
 So they say, it's a critical time.
 We now see diversity itself being weaponized.
 So they say this growing awareness accompanied by demands for inclusion and equity has led
 to some change, but there has also been resistance, especially among those implicitly privileged
 by the status quo.
 So again, jumping straight to attack on the person.
 Like I don't care if who makes an argument against me.
 I want to go on the argument and I'm going to go on the content of the argument.
 But these people straight, first thing they stayed is that's just by the people who are
 benefiting.
 That's just by the white men, basically.
 Straight to the identity of the person.
 That's dishonesty right there.
 So those questioning and even rejecting the idea that racism, misogyny, and harassment
 are problems within the AI field and the tech industry have appropriated the language of
 diversity to argue that efforts to improve inclusion are in fact exclusionary and addressing
 the deeper structural challenges posed by racism, sex and inequity is misguided.
 And yes, yes, definitely efforts to improve inclusion can be exclusionary.
 Like just because, so this is a thing, just because you're fixing a problem doesn't mean
 the method you're using to fixing it is justified and is itself good.
 Methods to improve inclusion can be exclusionary and some that have been proposed are exclusionary.
 Definitely it depends on the method.
 It doesn't mean these people are against these efforts.
 It means that the measures, for example, implementing racist hiring policy, I can definitely see
 that this is going to lead to more equal representation within the workforce.
 But the tool itself is really bad and exclusionary and discriminating.
 So yeah, I would say that it's accurate that it can be exclusionary.
 I say, for example, some AI researchers greeted the announcement of Black in AI Workshop at
 NRIPS leading machine learning conference by questioning whether the event was necessary,
 arguing that it would be discriminatory.
 But can't they?
 Can't they question whether the event was necessary?
 Like that would, I would, here I would need a discussion.
 What is it for?
 Right?
 Why is this event happening?
 And what is it doing?
 And is it discriminatory?
 It could be.
 Any event can be discriminatory.
 Does it discriminate based on race or gender or anything?
 Is it, you know, does it do so unjustly and all?
 So I don't, I don't just don't see why.
 Could still be wrong.
 Like you could question and then you could be wrong.
 But you should be taken on your argument.
 But the argument here is just already questioning this is already on the wrong side of the argument.
 And I don't agree with this.
 I don't agree with these people that question this workshop.
 Don't have a particular opinion on these things.
 But I have the opinion that you have to take arguments at their argument value and not
 just at who makes them or whether or not they're against a particular viewpoint.
 All right.
 They say such pushback often centers calls for cognitive diversity or viewpoint diversity.
 The idea that individual differences in the ways people think and understand the world
 are distinctions that should be counted alongside or instead of other identity categories such
 as race and gender.
 Well, yes, that's I mean, isn't that isn't that a very reasonable thing to say?
 Isn't it very reasonable to say that differences in the ways people think and understand the
 world, their distinctions that should be counted alongside other identity categories such as
 race and gender, they say a dozen white men so long as they were not raised in the same
 household and don't think identical thoughts could be considered diverse.
 That's I don't know if this is a sarcastic statement or not, but clearly it's it's kind
 of the counterpoint they're trying to make here that but yes, I would I would totally
 agree with this statement in a way a white man growing up in San Francisco, a white man
 growing up in rural Idaho, a white man growing up in Florida, a white man growing up in Western
 Europe, one in Russia, and one growing up on the road with its circus, his circus parents
 in Mongolia would definitely be that plenty diverse, right?
 I mean, they criticize this here, but this is is actually how can you how can you not
 see this that?
 Yes, these are valid differences, and people are going to think differently, independent
 of how they look, people are going to have different thoughts.
 And it's important to recognize other people think differently.
 And therefore, you should, you know, include them if it's relevant.
 And the counter argument to this is, of course, what the authors here are saying basically
 is that 12, a dozen people, as long as they are don't look the same, could be considered
 diverse, even if they all were raised in the same place, and basically all live in San
 Francisco, and think the exact same thing.
 Yeah, that's, I mean, it sounds to me, it sounds as absurd as the other way around.
 To me.
 So here's, here's my, here's my thoughts on this.
 I am not going to pretend that I know what life is like as a woman.
 Right?
 I'm absolutely sure that for areas of life, it is it is definitely valuable to listen
 to the experience of a woman or multiple women, an aggregate of women, because the life is
 just different as a woman.
 Life is also different.
 As a black person, I absolutely concede that there are things that I might not be able
 to draw from my life experience, because I am not of that skin color that different problems
 that people face.
 And that's why it's important to have an opinion of that at the table.
 But I'm also absolutely certain that I have no relation to someone who grew up as a child
 pop star from the age of 12, and then had that life.
 I have no relation to someone growing up under a communist regime.
 I have no relation to someone growing up in in kind of a Buddhist religious tradition.
 I just don't.
 And I don't care how they look.
 They have different experiences.
 They have different bodies of knowledge to draw on.
 And I don't think why we should make the difference along the exact lines of race and gender.
 Yeah, but that's what they that's of course what they argue here.
 Those arguments work by centering identity while flattening or ignoring power relationships.
 Here the VP, the Facebook VP of engineering said that the ultimate goal is cognitive diversity
 and cognitive diversity is correlated with identity diversity.
 That means it's not just about getting women in tech, it's about broad voices, broad representation.
 Right?
 So the the this is exactly what I would say the reason why we want different the reason
 why we want a woman or a black person at the table is because they have a different knowledge
 is because they have different thoughts because of their different life experience.
 They have different thoughts that they can bring in.
 So actually, by including these what they call bodies, it is about cognitive diversity,
 even in itself.
 But the authors here really see this from a different angle.
 They really see this in terms of power relationships between race and gender groups.
 And I yeah, the arguments of the authors don't make sense if you don't view it through that
 lens.
 That lens to me is just such a it's such a I don't know, it's just sad look on the world.
 And also, I think it's a very, very inaccurate look on the world.
 And it's, I think, a very dangerous look on the world.
 Um, yeah, again, they say instead of looking at historical patterns of marginalization,
 calls for cognitive diversity argued that all differences are equal.
 No, we're not.
 Like, no calls for cognitive diversity or don't argue that all differences are equal.
 Well aware that some people have it harder, well aware that some differences are bigger,
 worse or better.
 That's absolutely well aware all they're saying is that race and gender shouldn't be the like,
 only things to consider and shouldn't be in itself be considered diverse.
 Just because someone is of a certain skin color, it doesn't mean anything, right?
 It doesn't actually tell you anything about that person.
 So why not consider people as individuals and look at what was their life like until
 this point and what could they contribute to the discussion we're having rather than
 looking at the color of their skin.
 I mean, if the color of their skin played a role in their life, then obviously that
 would manifest in my suggestion as well.
 But to just look at people through this kind of group lens is is so foreign to me.
 And yeah, I feel it's it's quite dangerous.
 Yeah, so again, and this this could argue that all differences are equal.
 I mean, the point where you have to start misrepresenting what the counter argument
 is saying, that's really how you know you're dealing with a with not a well intentioned
 person on the other side of the of the discussion.
 This is really politics now.
 This isn't a well intended argumentation.
 It's really someone to trying to achieve some goal, because they have to misrepresent the
 other side.
 And this only gets worse from here.
 They say recently was exemplified in the controversy over Google's appointment of Heritage Foundation
 CEO K calls James to its Advanced Technology External Advisory Council.
 Google's reasoning for the appointment of James was ostensibly to ensure diversity of
 thought by including a conservative viewpoint on the council.
 Alright, so Google has a technology advisory board, or council, sorry, of external people,
 and they've included a conservative.
 And she is by all by all metrics, let's say, a standard conservative.
 So this is not a far right neo Nazi type.
 I don't know.
 But this is this is someone who has similar opinions than half the US country and in generally
 in at least in the Western world, generally half of the of the country's population tends
 to be conservative.
 More or less, I mean, there's differences.
 But yeah, so this this is a this is an opinion that a large portion of the population shares.
 So it would be I don't know, it would be suitable to include at least someone of that opinion
 in an external advisory council to to have that on board.
 You don't have to listen to her like she's not like she's made king.
 It's simply that she will have the opportunity to input her voice representative of kind
 of that large, very large percentage of people.
 They go on to say, James is also a black woman, thus adding racial and gender diversity to
 the panel.
 So even further, right, this is it's a conservative black woman.
 All right, but the pushback following James's inclusion focused on her policy position,
 citing specifically her vocal anti LGBTQ and anti immigrant views and highlighted why cognitive
 diversity is a particularly limited lens.
 And the pushback here was very much spearheaded by one of the authors of this article.
 So I am this isn't just reporting.
 I will also I'll also criticize the the this pushback here since it's, you know, it's kind
 of argued for in this article.
 It's not just reported and also because the authors are the same.
 So here they say they have vocal anti LGBTQ and anti immigrant views.
 And I haven't actually gone specifically and looked at what this person particularly has
 said, but given that she's a standard conservative and has been in public office, I believe under
 George W. Bush, she can't like I have trouble believing that she has like extremely hateful
 opinions like these people shouldn't exist or like something like that nature.
 Like often people like conservative people have have issues with forcing people to adopt
 certain pronouns for people or issues with which bathrooms do people go in and, you know,
 generally are tougher on immigration, especially illegal immigration and so on.
 I mean, these are these are views that people hold.
 It's a large part of people and these are discussions to be had.
 So including this this person would be very sensible move.
 But they say in a letter opposing the appointment, a group of Google workers calling themselves
 Googlers against transphobia and hate, transphobia and hate responded to the idea that diversity
 of thought justified James's addition to the council.
 This is a weaponization of the language of diversity by appointing James to the ATAC.
 Google elevates and endorses her view, implying that hers is a valid perspective worthy of
 inclusions in its decision making.
 This is unacceptable.
 Here it says again, the author was one of the organizers of that.
 And that's what they're saying here.
 The views, if you don't have our views, these are unacceptable views, right?
 It's valid perspective worthy of inclusion.
 It's what they're saying basically is you don't even talk to these to this person, like
 talking to this person, considering their opinion.
 You can still evaluate the opinion, but even considering their opinion is already wrong.
 And that given that the person is a black woman.
 So basically, they are called the author's idea of diversity is people that look different
 that are from race and gender groups that have don't have much power or perceived what
 they call power right now.
 As long as they all think exactly as we think, right, then that's fine.
 As long as they they share our thoughts, as long as they don't have dissenting opinions,
 we want the we want the different looking people.
 But don't dare talk to anyone of a different opinion.
 Yeah, this, I don't I don't see how I mean, these these authors, in my opinion, they really
 live in in a bubble, they really live in the in a tiny Silicon Valley or Silicon Valley
 influenced spaces, because this is this is half the people they basically saying half
 the people in their greater community in their country aren't even worthy listening to their
 opinions aren't even worthy of inclusion in of consideration.
 So yeah, well, well done might as well discredit them at once.
 I'm sure I'm sure I'm sure that's gonna fly well with these people.
 All right.
 Yeah, might might start calling them deplorables and see what they do.
 Maybe they'll return the favor and elect a moron just to stick it in your face.
 I mean, that's what happened.
 So the idea of cognitive diversity is mobilized by some support in support that the AI field
 and the tech industry are already diverse.
 Including as far as to support claims that not including identities like white and male
 constitutes discrimination.
 Yes, it can.
 Like if, if you include every single identity except white and male, that constitutes discrimination.
 That's I mean, yes, even if they're in the majority is still constitutes discrimination,
 like no one can help being born white and male, no one white and male chose to be born
 like that.
 Don't mostly don't choose the melanin content of your skin, you can modulate it a bit by
 going to the sun, which computer science people statistically don't do very often.
 So there's not much leeway there.
 So yeah, to not include identities like that, if you include every other one, can constitute
 discrimination.
 True.
 A July 2017 memo written by James Damore, a software engineer at Google is illustrative
 of such pushback titled Google's ideological echo chamber.
 And published in an internal mailing list, the memo critiqued the company's diversity
 policies arguing that biological differences between men and women rather than bias and
 discrimination help explain gender disparities at the company.
 I feel the you can leave out the rather than here.
 I think the memo simply stated that biological differences can help explain the gender disparities.
 The most objective writing the memo was to make the case that policies designed to achieve
 equal representation are unfair, divisive and bad for business.
 Well some are.
 Yes, especially the recommendations that you've given at the beginning, number seven, is unfair,
 divisive and I would also argue bad for business.
 So supporters for Damore's point of view at times even drew on the rhetoric of the pipeline
 to make the case that diversity initiatives are in fact discriminatory.
 They argue incorrectly that if there aren't qualified candidates in the pipeline, then
 hiring those who are unqualified on the basis of identity discriminates against those who
 are qualified.
 No, I would say hiring anyone on the basis of identity discriminates.
 I mean inherently.
 So again I think that's the larger argument that these people are making, which is not
 incorrect, is very correct.
 So in an update to the memo Damore himself asserted that he values diversity and inclusion,
 but his primary concern was cognitive diversity.
 He says diversity inclusion is not denying that sexism exists, doesn't endorse using
 stereotypes.
 And in specific I've read the memo and it directly says these are population level kind
 of statistics and there is more overlap than difference and you absolutely can't say anything
 about an individual by looking at these statistics.
 That's almost a quote from this memo.
 So he was very much concerned with considering people as individuals, but also if you like
 he was basically making the same argument as earlier.
 I told you to remember, hey look this one study that found that women's interests might
 be different and we might shape the curriculum.
 That's basically what Damore said.
 He said women's interests might be different and we'd have to maybe shape the way we do
 work, like change the way we do software engineering to attract more of them.
 That was one of his points.
 So he's exactly the same thing, but of course he's a misogynist because he suggested that
 this could be due partly because of biological differences.
 And the way he was dragged through the mud is just crazy.
 And they shoot here very much against this kind of biological, what they call biological
 determinism.
 We'll see this very briefly.
 I'd say diversity becomes an empty signifier, stripped of the histories and experiences
 of systemic discrimination, repurposed around ideology rather than bodies.
 I'd say diversity has nothing inherently to do with bodies as such.
 I think that's only the case if you are already convinced of this.
 Within hours of the memo's publication, harassment targeting minority advocates who pushed back
 against the claims in the memo began, with a particular focus on queer and trans workers.
 That's bad, but also I think the pushback against people who voiced support was also
 pretty bad because one of them was fired, as you already stated.
 Google's vice president of diversity even locked down her Twitter account shortly after
 Demours firing, responding to the barrage of threats describing her as a police Nazi.
 Well yeah, if you fire something.
 I mean undoubtedly Google fired this guy because they thought it was less of a PR disaster
 if they also fired him now.
 This probably wasn't an ideological decision, much more a PR decision.
 If you fire someone after stating something like this, it very much looks like you're
 firing them because you don't like their ideas and you don't like what they're saying,
 which people generally are not in favor of censoring freedom of speech.
 But yeah, that being said, harassment is bad, don't harass people.
 Also that being said, criticism isn't always harassment and don't conflate the two.
 Demours' memo also stated that the distribution of preference abilities of men and women differ
 in part due to biological causes and that these differences may explain why we don't
 see equal representation of women in tech and leadership.
 This assertion hinges on a flawed assumption that identities like gender and race are essential
 and fixed biological attributes and that inequalities are at least in part the product of such irreducible
 differences.
 Well, I mean, if they're not fixed biological attributes, certainly gender and race have
 a 0.99 correlation with biology.
 Since your biology is first and it's determined when you're conceived, that demonstrates a
 causal direction.
 Even if they're not exactly fixed, they are overwhelmingly fixed.
 And to suggest that this is a flawed assumption, that these inequalities are at least part
 the product of such differences, what you'd have to do, they simply state it's a flawed
 assumption.
 What you have to do in order to show this is a flawed assumption, you have to show that
 gender and race, as far as they're biologically determined, have no influence whatsoever on
 these differences.
 That's what you have to show, right?
 That's the counterclaim because the claim is they have at least in part something to
 do with it.
 And that's also, I believe, what the more stated and what the predominant opinion like
 is very like all the research points to, for example, there is a large difference in interest
 between genders as far as, for example, career selection goes and so on.
 Now, we can talk about why that is, but there's also a large consensus, I believe, that this
 is at least partly determined to however degree, but it is at least partly determined by biology.
 In order to show that this is flawed, you need to show that it does not have, it can't
 have any influence, right?
 You have to basically prove them the impossibility of this having an influence, which no one
 has done so far, much to the contrary.
 So simply state this is a flawed assumption kind of shows to me that they've already,
 they are there, they're in a bubble and they're expecting to speak to people in the same bubble.
 Yeah, so they go on and kind of discredit this as called a biological determinism, which
 I don't think that's a correct use of the term biological determinism, but you can judge
 for yourself.
 All I think these people are saying that biology might have some influence and we could adjust
 for that.
 It's not even right, it's not even.
 Yeah, this comes up here.
 So conclusion, conclusion, finally, I think it's been two hours.
 Sorry.
 Conclusion.
 Throughout this report, we've outlined the scope and scale of the problem, tracing how
 the diversity crisis in the industry and the problems of bias and AI systems are interrelated
 aspect of the same issue.
 No.
 In the past, these topics are commonly examined in isolation, but increasing evidence shows
 that they are closely intertwined.
 No, you've shown that they're parallel.
 You have absolutely not shown that they're interrelated aspects of the same issue and
 you have not shown that one, any one of these causally influences the other, that there
 is any feedback loop.
 You have not shown that fixing one leads to fixing the other.
 I mean, you could also take a company that extremely is focused on, or for some reason
 has a different workforce and then show how their products with the same data sets as
 the previous companies don't end up being biased.
 Probably not so easy.
 But again, none of that is in the report.
 There are many things you could actually do to show what you wanted to show, but it's
 just not the case in this article.
 Our analysis surfaced two prominent responses to the diversity crisis.
 On one hand, a worker driven movement, which we've skipped.
 On the other hand, we observe a small but vocal counter movement that actively resists
 diversity in the industry.
 What dishonesty actively resists diversity?
 I mean, the thought that these people stray around like, no, I don't like the other looking
 people.
 It's just so absurd.
 All they're saying is that either we don't understand the problem in the correct way
 or our tools aren't appropriate to solve the problem.
 I think everyone has the same goal of the workplace and the AI systems being as fair
 and as non discriminatory as possible.
 Misrepresentation of the other side is something that really bugs me.
 And it's something that these authors do a lot.
 So yeah, I lose my polite side maybe.
 And uses arguments from biological determinism to assert that women are inherently less suited
 to computer science and AI.
 What a load of crap.
 Sorry, but uses to assert that women are inherently less suited to computer science.
 No one.
 Okay, not no one, but no one that I know.
 Asserts that absolutely no one that makes these arguments.
 Sorry, not no one.
 You can always find a sexist douchebag that makes that argument.
 But this is not a serious argument made.
 And this is not this counter movement.
 Most people in the argument that most people in this counter movement make.
 Not at all.
 And to represent them as such is just so dishonest that yeah, this this this basically this is
 the it's nice that it's in the conclusion because it finally like at the end it completely
 destroys the credibility of me taking seriously these authors.
 I thought they had so that the parts we skipped over I mostly would say I'm mostly okay with
 they mostly show parallels between the that AI systems are biased and they also show that
 there is unequal representation.
 They also show examples of discrimination, harassment and so on.
 Problems in AI companies and universities that all you can read the report for this
 that's it's pretty interesting to read.
 But the points I've addressed, I'm not happy with.
 Yeah, so that was it for now.
 Sorry this was took so long, but I felt that a thorough take was necessary.
 Have a nice rest of the day.