Draft Version – 31 August 2020 1 Mapping Value Sensitive Design onto AI for Social Good Principles Steven Umbrelloa and Ibo van de Poelb a Institute for Ethics and Emerging Technology; University of Turin, Italy steven.umbrello@unito.it b Delft University of Technology, Delft, Netherlands I.R.vandePoel@tudelft.nl Abstract Value Sensitive Design (VSD) is an established method for integrating values in technical design. It has been applied to different technologies and recently also to artificial intelligence (AI) applications. We argue that AI poses a number of specific challenges to VSD that require a somewhat adapted VSD approach. These challenges mainly derive from the use of machine learning (ML) in AI. ML poses two challenges to VSD. First, it may opaque (to humans) how an AI systems has learned certain things, which requires attention for such values as transparency, explainability and accountability. Second, ML may lead to AI systems adapting themselves in such ways that they 'disembody' the values that have been embodied in them by VSD designers. Dealing with this requires on-going monitoring and extension of VSD to the whole lifecycle. In order to address the challanges, we propose to integrate the AI4SG principles proposed by Floridi et al to a VSD for AI approach. We illustrate the new VSD for AI approach with an example use case of a particular SARS-CoV-2 contact-tracing app. Keywords: value sensitive design, VSD, artificial intelligence, AI4SG , Sustainable Development Goals, COVID-19 Acknowledgments: All remaining errors are the author's alone. The views expressed in this paper are not necessarily those of the Institute for Ethics and Emerging Technologies. Ibo van de Poel's contribution to this paper is part of the project ValueChange that has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme under grant agreement No 788321. The content of this publication has not been approved by the United Nations and does not reflect the views of the United Nations or its officials or Member States Draft Version – 31 August 2020 2 1. Introduction Past research has explored how Value-Sensitive Design (VSD )can be applied to specific technologies such as energy systems (Mouter et al. 2018; Oosterlaken 2015), mobile phone usage (Woelfer et al. 2011), architecture projects (van den Hoven 2013), and augmented reality systems just to name a few (Friedman and Kahn Jr. 2000). It has similarly been proposed as a suitable design framework for future technologies, both near and long term. Examples include its exploratory application to nanopharmaceuticals (Timmermans et al. 2011), molecular manufacturing (Umbrello 2019a), intelligent agent systems (Umbrello and De Bellis 2018), and, less futuristic autonomous vehicles (Calvert et al. 2018; Umbrello and Yampolskiy 2020). Although these studies provide a useful theoretical basis for how VSD can be applied to specific technologies, they do not account for the unique ethical and technical issues that various AI systems present. There is ample discussion about the risks, benefits and impacts of Artificial Intelligence (AI) . Although the exact effects of AI on society are neither clear nor certain, what is beyond doubt is that AI is and will continue to have a profound impact on human flourishing broadly construed. Several scholars have explored the ethical concerns and values necessary to construct AI towards socially beneficial ends. AI is here understood as the class of technologies that are autonomous, interactive and adaptive and which can carry out human-like tasks (c.f. Floridi and Sanders 2004). In particular, we are interested in AI technologies that are based on Machine Learning (ML), which allows such technologies to learn on basis of interaction with and feedback from its environment. These learning capabilities pose, we will argue, specific challanges for VSD; in particular AI technologies are more likely than other to acquire features that were neither foreseen nor intended by their designers, and – in addition the way they lean and evolve may be opaque to humans. In order to address these challenges, we suggest that a set of AI-specific design principles need to be added to VSD. Here we propose to build on the significant headway that has recently been made in the numerous AI for Social Good (AI4SG) projects that are becoming popular in various research circles (Hager et al. 2019). Practical, on the ground, applications of AI4SG principles are already enacted for various AI enabled technologies. This provides researchers with a solid groundwork of how ethics manifests itself in practice. However, AI4SG is difficult and its underlying principles are still fuzzy, given the multiplicity of research domains, practices and design programs (Taddeo and Floridi 2018). Yet, some work has already been done to narrow down the essential AI4SG factors (Floridi et al. 2018a, 2020). This paper argues that the design methodology called Value Sensitive Design (VSD) provides a principled approach that diverse design teams can adopt regardless of domain to formalize their approach to design AI4SG along these factors. Although other tools for achieving responsible research and innovation have been proposed (IEEE 2019; UNESCO 2017), VSD in particular is chosen as the design methodology because of its inherent self-reflexivity and its emphasis on engaging with both direct and indirect stakeholders as a fundamental part of the design process and the philosophical investigation of values (Friedman and Hendry 2019; Umbrello 2018). Draft Version – 31 August 2020 3 This paper is organized in the following way. §2 will lay out the VSD framework. §3 describes why it is challenging to apply VSD to AI §4 outlines the motivations and description of the AI4SG factors as a way to address the specific challenges raised by AI for VSD.. §5 outlines a design approach inspired by VSD and the AI4SG principles §6 uses the example of a specific SARS-CoV-2 contact-tracing app to provide a preliminary illustration of this approach. §7 concludes the paper. 2. Value Sensitive Design VSD is a principled approach to take values of ethical importance into account in the design of new technologies. The original approach was developed by Batya Friedman and colleagues from the University of Washington, but the approach is now more widely adopted and has been further developed by others, sometimes under somewhat different headings like Values at Play or Design for Values (Flanagan et al. 2008; van den Hoven et al. 2015). At the core of the VSD approach is what Friedman et al. (2006) call the tripartite methodology of empirical, conceptual and technical investigations (see Fig. 1). These investigations, that can be carried out consecutively, in parallel, or iteratively, involve 1) empirically investigating the relevant stakeholders, their values and their value understandings and priorities 2) conceptual investigations into values and possible value trade-off, 3) technical investigations into value issues raised by current technology and possible implementation of values into new designs. Friedman and Hendry (2019) propose 14 more specific methods that can be used in VSD: (1) stakeholder analysis; (2) designer/stakeholder explicitly supported values; (3) coevolution of technology and social structure; (4) value scenarios; (5) value sketches; (6) value-oriented semistructured interview; (7) granular assessments of magnitude, scale, and proximity; (8) valueoriented coding manual; (9) value-oriented mock-ups, prototypes, and field deployments; (10) ethnography focused on values and technology; (11) model for informed consent online; (12) value dams and flows; (13) value sensitive action-reflection model; and (14) envisioning cards. Draft Version – 31 August 2020 4 Fig. 1. The recursive VSD tripartite framework employed in this study. Source: (Umbrello 2020) One important issue in VSD is how to identify the values that should be taken into account in a concrete VSD process (Davis and Nathan 2015). Friedman et al (2016) propose a list of thirteen values that are important for the design of information systems (human welfare, ownership and property, privacy, freedom from bias, universal usability, trust, autonomy, informed consent, accountability, courtesy, identity, calmness and environmental sustainability). Others have opposed such an approach and argued that it is better to elicit values more bottom-up from stakeholders (Borning and Muller 2012; Le Dantec et al. 2009). Both approaches probably have their advantages and disadvantages. A value list may well miss out on values that are important in a specific situation, but are not on the list. Although bottom-up elicitation may help to discover such values, it is also not watertight as important values may not be articulated by the stakeholders (or crucial stakeholders may not have been identified). Moreover, not every value held by a stakeholder is also a value of ethical importance that should be included in VSD. For the case of AI, some considerations are important when it comes to identifying values in VSD design processes of AI technologies. First, there is now widespread consensus that AI raises specific ethical issues, which are not, or at least to a much lesser degree, raised by more conventional information and communication technologies. This has two implications for the issue of value identification. First, the original VSD list of values does not suffice for AI; instead, one may, for example, take the values identified by the EU High-level expert group on ethics of AI as starting point (Floridi 2019; High-Level Expert Group on AI 2019): respect for human autonomy, prevention of harm, fairness and explicability. Second, some value list would seem desirable for the case of AI to ensure that the typical ethical concerns arises by AI are not overlooked. This is not to say that no other values should be included in the design of AI applications. They should, and some form of bottom-up elicitation may be relevant here, but it should be supplemented by principles that ensure that typical AI ethical issues are properly addressed. Here we propose to have recourse to the AI4SG meanings and factors that we will discuss in more detail in section 4. Conceptual Investigations Values from both the relevant philosophical literature and those explicitly elicited from stakeholders are determined and investigated. Technical Investigations The technical limitations of the technology itself are evaluated for how they support or contstrain indentified values and design requirments Empirical Investigations Stakeholder values are empirically evaluated through socio-cultural norms and translated into potential design requirments Draft Version – 31 August 2020 5 3. Challenges posed by AI AI applications pose specific challenges when it comes to VSD. This is particularly due to the self-learning capabilities of AI. This complicates the reliable integration of values in the design of technologies that employ AI. We start with a short imaginary, but illustrative, example and then discuss in more general terms the complications raised by AI for value sensitive design. Suppose the tax department of a certain country wants to develop an algorithm that helps to detect potential cases of fraud. More specifically, the application should help civil servants to select those citizens whose tax declaration needs extra or special scrutiny. Now suppose they choose to build a self-learning artificial neural network for this task. An artificial neural network consist of a number of input units, hidden units and one or more output unit, as pictured in Figure 2. Fig. 2 an artificial neural network Let us suppose that the output unit or variable is simply a yes/no indicating whether a specific tax declaration needs additional scrutiny. The input variables (units) can be many, including, for example, the amount of tax to be paid by a certain citizen, the use of specific tax exemptions, prior history of the person (e.g. suspected fraud in the past) but also personal details (age, sex, place of living etc.) The units (variables) in the artificial neural network are connected as shown in Figure 2. The connections between the units can be weight factors that are learned by the algorithm. This Draft Version – 31 August 2020 6 learning can be supervised or not (Russell and Norvig 2010). If supervised learning is applied, the algorithm may learn to make calls on what tax declaration need to be scrutinized that are similar to those of experienced civil servants at the tax office. In the case of unsupervised learning, information on which scrutinized cases led to detection of actual fraud may be fed back into the algorithm and it may be programmed to learn to select those cases that have the highest probability of leading to the detection of actual fraud. Now one of the values that is obviously important in the design of such an algorithm is 'freedom from bias'. This value is in fact already included in the original list of VSD values proposed by Friedman and Kahn (2002). Friedman and Nissenbaum (1996, 332) define 'freedom from bias' in reference to "computer systems that systematically and unfairly discriminate against certain individuals or groups of individuals in favor of others." In traditional VSD, this value me be implemented in the design of the algorithm in a number of ways. First, and foremost, it may be translated into a design requirements that none of the variables in the artificial neural network (the nodes in Figure 2) uses variables that may lead to an unwanted bias. For example, ethnicity may be ruled out as a potential variable. However, this will not be enough to ensure the realisation of the value 'freedom from bias,' as bias may also be introduced through proxy variables. For example, postal codes may be a proxy variable for ethnicity and one may also want to rule out the use of such variables to ensure 'freedom from bias.' But even then, a self-learning algorithm may be biased due to the way it learns (Mehrabi et al. 2019). It may, for example, be biased because the training set for the algorithm is not representative or skewed. If a form of supervising learning is chosen, it is well conceivable that the algorithm learns the bias that was already in human judgements that are used for the supervisory learning. But even if also these potential sources of bias have been excluded, it cannot be guaranteed that the resulting algorithm is not biased, certainly not if a form of nonsupervised (reinforcement) learning is chosen. One issue is that that the resulting artificial neural network may be described as following a certain rule even if this rule was never encoded nor can be (easily) derived from the nodes (variables) in the artificial neural network (c.f. Walmsley 2012, 88-112). In other words, it is conceivable that the resulting algorithm can be described as following a rule that is somehow biased without this result being foreseeable or even clearly discernible. This means that bias in the algorithm in this imaginary case may be emergent and opaque. Emergent in the sense that it is an unintended and unforeseen consequence from the way the algorithm has learned, opaque in the sense that it may not be immediate clear, for humans, from inspection of the algorithm or artificial neural network that it is biased. This point is more general and does not just apply to this specific example or the value 'freedom from bias' (or 'fairness'). Due to their self-learning capabilities, AI systems, in particular ML, may develop features that were never intended nor foreseen (or foreseeable) by their designers. This is also means that the may have unintended value consequences and it can even imply that they unintendedly 'disembody' values that were embedded in their original design (van de Poel forthcoming; Vanderelst and Winfield 2018). Moreover, these unintended features may not always be discernible as they may be due specific ways the algorithm has developed itself that are hard or even impossible, for humans to fully understand. Draft Version – 31 August 2020 7 These issues are not necessarily insurmountable. For the imaginary case of the algorithm for the tax office, there may be technical solutions that make it at least much more unlikely that the system will develop in a biased direction, for example we may tell the algorithm to optimise itself not only in terms of effectiveness (expressed e.g. number or percentage of cases of fraud detected) but also in terms of fairness (e.g. presenting a non-biased selection of cases to be investigated) (Mehrabi et al. 2019). The important point is that addressing emergence and opaqueness requires a set of design principles (or design norms) that are not needed for traditional technologies. Some of these principles relate to technical or design requirements, others to the organisation of the design process and the further life-cycle of a products (like continued monitoring), and still other may have to do with what AI techniques to use, or not to use. In the next section, we will look at the proposed AI4SG principles as a way to address the specific challenges that AI poses to VSD. 4. AI4SG Meaning and Factors The most thorough work of harmonization of AI4SG values has been recently undertaken by Cowls, King, Taddeo, & Floridi (2019) who focus on factors that are 'particularly relevant" to AI (not exhausting the potential list of relevant factors). The seven factors that are particularly relevant for the design of AI towards the social good are: (1) falsifiability and incremental deployment; (2) safeguards against the manipulation of predictors; (3) receiver-contextualized intervention; (4) receiver-contextualized explanation and transparent purposes; (5) privacy protection and data subject consent; (6) situational fairness; and (7) human-friendly semanticisation (Floridi et al. 2020, 3). The seven factors although discussed separately naturally co-depend and co-vary with one another, thus they should not be understood as a rank-ordered hierarchy. Similarly, the seven factors each relate, in some way, to at least one of the four ethical principles that EU High-Level Expert Group on AI lays out: respect for human autonomy, prevention of harm, fairness and explicability. This mapping on to the more general values of ethical AI is not insignificant, any divergences from these more general values has potentially deleterious consequences. What the seven factors are meant to do then is to specify these higher-order values into more specific norms and design requirements (Fig. 4). Draft Version – 31 August 2020 8 Fig. 3. Relationship between higher-order values of the EU HLEG on AI and AI4SG norms. Rather than reiterate what has already been clearly evaluated and discussed in Floridi et al. (2020), here each of the seven factors will be laid out in summary, later to be discussed in §5 alongside ways that VSD practices can be levied to actualize these factors. (1) Falsifiability and incremental deployment To forward the development of AI towards the embodiments of values such as transparency and safety, the value of falsifiability is considered to be a critical factors towards the social acceptance and trust of technologies in general. Falsifiability is defined as "the specification, and the possibility of empirical testing, of one or more critical requirements, that is, an essential condition, resource, or means for a capability to be fully operational, such that something could or should not work without it," (Floridi et al. 2020, 5). This means that other values that are implicated in AI design are predicated on their ability to be falsifiable, that is, essential to the architectures of a technical system. This entails that continued empirical testing must be undertaken in different contexts (obviously this cannot be exhausted without full deployment of a system) to best ascertain possible failures of a system. This means the need for an incremental deployment cycle in which systems are introduced into real-world contexts only when a minimum level of safety makes such deployment warranted. In sum: "AI4SG designers should identify falsifiable requirements and test them in incremental steps from the lab to the "outside world"" (Floridi et al. 2020, 7). (2) Safeguards against the manipulations of predictors The manipulation of predictors can lead to a range of potentially deleterious outcomes for AI, moving away from the promises that AI4SG promises. This has been described as the outcome Human Autonomy Receivercontextualized intervention (AI4SG #3) Privacy protection and data subject consent (AI4SG #5) Human-friendly semanticisation (AI4SG #7) Prevention of Harm Falsifiability and incremental deployment (AI4SG #1) Privacy protection and data subject consent (AI4SG #5) Fairness Safeguards against the manipulations of predictors (AI4SG #2) Situational fairness (AI4SG #6) Explicability Receivercontextualized explanation and transparent purposes (AI4SG #4) Human-friendly semanticisation (AI4SG #7) Draft Version – 31 August 2020 9 of the manipulation of input data as well as the overreliance on non-causal indicators (Floridi et al. 2020, 7). The nature of overreliance on non-causal indicators as well as the often overespused, but underthought value of transparency can lead to the gamification of systems towards desired ends by those who understand what inputs lead to what outputs (Boscoe 2019; Ghani 2016). In order to avoid this, Floridi et al. (2020) argue that "AI4SG designers should adopt safeguards which (i) ensure that non-causal indicators do not inappropriately skew interventions, and (ii) limit, when appropriate, knowledge of how inputs affect outputs from AI4SG systems, to prevent manipulation" (Floridi et al. 2020, 8). (3) Receiver-contextualized intervention The co-construction and co-varying of technologies and users implicates a delicate balancing act between how artifacts effect user autonomy, a value of particular importance within the context of technological design and development (Umbrello 2019b). What this means that is in order to balance both the false-positives and false-negatives that can result in sub-optimal levels of usertechnology interventions, optionality given to users provides one possible route for balancing interventions on autonomy. This is contextualized based on "information about users' capacities, preferences and goals, and the circumstances in which the intervention will take effect"" (Floridi et al. 2020, 9). In sum: AI4SG designers should build-decision-making systems in consultation with users interacting with and impacted, by these systems; with understanding of users' characteristics, of the methods of coordination, and the purposes and effects of an intervention; and with respect for users' right to ignore or modify interventions. (Floridi et al. 2020, 9). (4) Receiver-contextualized explanation and transparent purposes The aims of any given system must be transparent, that is, the operations carried out by a system should be explainable so as to be understood. Design thus is inextricably linked to these values given that the intricacies of a system's operations and objectives are the consequence of design decisions. Seeing as the evermore ubiquitous deployment of AI systems is already underway, need for explainability and transparency in their operations and goals has similarly garnered a lot of attention given the potential harm that can come about as a consequence of opaque goals and operations (Allo et al. 2016; Boscoe 2019; Turilli and Floridi 2009). In relation to (3), the explained information of a systems operations and objectives should similarly be receivercontextualized (Floridi et al. 2020). Because the goals, design programs and tools used for differing AI4SG projects will vary greatly, the correct contextualization will similarly vary. The conceptual schema of what is being framed and to whom is called by Floridi (2017) as the Level of Abstraction and consists of five Draft Version – 31 August 2020 10 components which any theory of a given system consists of (Floridi 2017)1 Because the inner workings and overall goals of any AI system is the consequence of designers' choices and design flows, there must similarly be transparency in the design decisions that are made to determine if they map onto the motivation behind any given systems' design and deployment. Thus, early stage considerations of the type of transparency, the goals and intentions of the designers and the level of transparency that is needed for the successful Explainability of AI systems' operations and goals must necessarily be carried out early-on in the design program in question. In sum: AI4SG designers should choose a Level of Abstraction for AI explanation that fulfils the desired explanatory purpose and is appropriate to the system and the receivers; then deploy arguments that are rationally and suitably persuasive for the receivers to deliver the explanation and ensure that the goal (the system's purpose) for which an AI4SG system is developed and deployed is knowable to receivers of its outputs by default. (Floridi et al. 2020, 14). (5) Privacy protection and data subject consent The scholarship on privacy protection and subject consent is both rich and nuanced, encompassing decades of socio-ethical and legal perspectives among others informing the topics. Given that privacy forms the basis of both good policy and just democratic regimes (Peters 2018), it is natural that AI4SG programs should make this an essential factor (Solove 2008). Tensions and boundaries between different levels and understandings of user data processing and use has already been explored and the nuances of how to adequately address such tensions have been proposed (Floridi 2016; Price and Cohen 2019). Given that stakeholder data is foundational to the usability and efficacy of AI systems, AI4SG systems must then seek to provide a sufficient balance that respects the values of stakeholders with regards to data processing and storage. Thus: "AI4SG designers should respect the threshold of consent established for the processing of datasets of personal data," (Floridi et al. 2020, 16). (6) Situational fairness As mentioned in (5), data sets are critical to the function of AI systems. Given that datasets can be biased on account of multiple factors (dataset collection, selection, categorizations, etc.), the resulting function of any given system will similarly provide biased results (Boscoe 2019). The biased decision-making can be of ethical importance given that the relevant datasets may be data regarding to ethically relevant categories such as race, gender or age among others (Friedman and Nissenbaum 1996). The propagation of bias in datasets must then be avoided if we are at attain AI4SG given that recursively improving systems will only exacerbate bias if designed/trained with biased datasets. Thus, "AI4SG designers should remove from relevant datasets variables and proxies that are irrelevant to an outcome, except when their inclusion supports inclusivity, safety, or other ethical imperatives," (Floridi et al. 2020, 18). 1 For the sake of brevity and conciseness, we do not include the full description of the five level of Abstraction. For further on this we direct he reader to Floridi, L., 2017. The logic of design as a conceptual logic of information. Minds Mach. 27, 495–519. Draft Version – 31 August 2020 11 (7) Human-friendly semanticisation Managing and maximizing agents' "semantic capital" must be essential to the design of AI4SG systems. Floridi (2018) defines semantic capital as "any content that can enhance someone's power to give meaning to and make sense of (semanticise) something," (Floridi, 2018, 483). Because AI allows for the automation of semanticisation, i.e., making sense of things, if done so haphazardly it can lead to ethically problematic results. Arbitrary semanticisation can lead to results that gives meaning in ways that do not map on to our own (random meaning-making) or can similarly AI semanticisation can be to narrow given limited dataset exposure that allows for the propagation of similarly narrow meanings, thus limiting the redefinition or interpretation of things (Al-Abdulkarim et al. 2016). Likewise, because the agent engaging in semanticisation is essential to what and how meaning is made, semanticisation is thus subjective, making AI systems that aim at total semanticisation unworkable and quixotic (i.e., making sense of all possible objects). The way around this is delimiting the tasks set to be carried out by AI systems. Rather than total abdication of tasks, which ones are essential to be carried out by AI systems should be determined a priori to AI4SG systems' deployment (Floridi et al. 2020). Thus, "AI4SG designers should not hinder the ability for people to semanticise (that is, to give meaning to, and make sense of) something," (Floridi et al. 2020, 19). This section has condensed the seven essential factors necessary to the design of AI4SG systems as proposed by Floridi et al. (2020). Let us now see how these factors would help to overcome the challenges posed by AI for VSD as discussed in the previous section. We focus on the specific example given. First principle #6 would require to "remove from relevant datasets variables and proxies that are irrelevant to an outcome," This is in line with the traditional VSD approach but it is not enough, as AI bias may be emergent and/or hidden (opaque). To address the emergent character of bias, principle #1 is particularly important, and specifically the emphasis on incremental development. This is primarily a procedural requirement, and it requires monitoring, and extending VSD to the full-life cycle of design, as we will discuss in greater detail in the next section. In order to avoid opaqueness, AI4SG principles # 4 and #7 are important. It should be noted that they may sometimes imply that certain ML techniques should not be used. 5. Adapting the VSD approach In order to address the challenges posed for VSD by AI, we propose a somewhat adapted VSD approach. The adaptations we propose are threefold: 1) integrating the AI4SG principles in VSD as design norms from which more specific design requirements can be derived, 2) distinguishing between values to be promoted by the design and values to be respected by the design in order to ensure that the resulting design does not only do no harm but also contributes to doing good, and 3) extending the VSD process to encompass the whole life cycle of a AI technology in order to be able to monitor unintended value consequences and to redesign the technology if necessary. We first briefly explain these new features and then sketch the overall process. Integrating AI4SG principles as design norms: We propose to map the AI4SG principles Draft Version – 31 August 2020 12 onto the 'norms' used to translate values into technical design requirements, and vice versa, that van de Poel (2013) outlines (see Fig. 4). Likewise, an entire typology available of practices and methods for turning the principles of AI for good (i.e., beneficence, non-maleficence, autonomy, justice, explicability) as well as case studies based on the work of Morley et al. (2020) has been gathered by Digital Catapult into an 'Applied AI ethics typology' (Digital Catapult n.d.). However, these methods are all relatively high-level and not specifically operationalized for designing for AI4SG. For this reason VSD is proposed as an apt starting point given its theoretical overlap with AI4SG principles as norms for translating these values into design requirements. Fig. 4. Values hierarchy. Source: (Van de Poel, 2013). Distinguishing between values to be promoted and values to be respected: In order for a VSD approach to AI to be more than just avoiding harm and actually contributing to social good, an explicit orientation is required to socially desirable ends. Such an orientation is still missing in current proposals for AI4SG principles. Here we propose to address this by an explicit orientation to the Sustainable Development Goals (SDGs) as proposed by the UN, as a best approximation of what we collectively believe to be valuable societal ends. In 2015, all of the member states of the United Nations adopted the then proposed 2030 agenda for sustainable development, a proposal aimed at the design and implementation of goals towards a safe and sustainable future founded on the agreed desire for global peace (United Nations 2018). The general adoption of the resolution is towards making actionable the 17 SDGs that form its foundation (Fig. 5). It recognizes that the included SDGs must not be looked at as mutually exclusive of one another, rank valued or as trade-offs, but rather SDGs such as the ending of poverty and climate change remediation go hand-in-hand (United Nations 2019). Among ending poverty and climate change action, there are goals such as 'affordable and clean energy', 'industry, innovation and infrastructure', and 'sustainable cities and communities' just to name a few. Design Requirments Norms Values Value Norm Design Requirment Design Requirment Design Requirment Design Requirment Norm Norm Norm Draft Version – 31 August 2020 13 Fig. 5. United Nations Sustainable Development Goals. Source: (United Nations 2019) Extending VSD to the entire life cycle: in order to address the emergent, and possibly unintended, properties that AI systems acquire as they learn, we propose to extend VSD to the full life cycle of AI technologies, in order to keep monitoring the potential unintended value consequences and to redesign the technology if necessary (de Reuver et al. 2020; van de Poel forthcoming). A similar idea is indeed voiced in the AI4SG Principle (1): "AI4SG designers should identify falsifiable requirements and test them in incremental steps from the lab to the "outside world"" (Floridi et al. 2020, 7). The need for ongoing monitoring arises from he uncertainties that accompany new technologies that are introduced in society (van de Poel 2016). The resulting VSD process is illustrated in Fig. 6. Given that each AI systems design has different uses and thus different value implications, the illustration serves as a general model that engineers can use to guide them throughout their design program. Draft Version – 31 August 2020 14 Fig 6. VSD design process for AI technologies We suggest that VSD for AI proceeds in four iterative phases that we briefly describe below: 1. Context analysis: Motivations for design differ across different projects. For this reason, there is no normative starting point that designers must begin with. VSD acknowledges that technology design can begin with the discrete technology itself as a starting point, the context of use or a certain value. In all cases an analysis of the context is crucial. Various contextual variables come in to play that impact the way values are understood (in the second phase), both in conceptual terms as well as in practice on account of different sociocultural and political norms. Eliciting stakeholders in sociocultural contexts is imperative within the VSD approach to determine whether the explicated values of the project faithfully map on to those of the stakeholders, both direct and indirect. Thus, empirical investigations play a key role in determining the potential boons and downfalls to any given context. In engaging with the context-situated nuances of how various values may come to play with any given system, various pitfalls and constraints can begin to be envisioned, particularly how the initial core values can be understood in terms of technical design requirements (in phase 3). 2. Value identification. The second phase concerns the identification of a set of values that form the starting point of the design process. We suggest three main sources of such values: 1) Values that are to be promoted by the design, for example deriving from the SDGs formulated by the UN, 2) Values that should be respected, in particular those values that have been identified in relation to AI: respect for human autonomy, prevention of harm (nonmaleficence), fairness and explicability (Floridi et al. 2018b; Draft Version – 31 August 2020 15 High-Level Expert Group on AI 2019), 3) context-specific values that are not covered by 1) and 2) but which derive from the analysis of the specific context in the first phase, in particular values held by stakeholders. It should be noted that phase 2 does not just involve empirical investigations, but has a distinct normative flavor, in the sense that it results in an identification of values that are to be upheld in the further design from a normative point of view. In addition, this phase involves conceptual investigations geared at interpreting (in context) and conceptualising the relevant values. 3. Formulating design requirements. The third phase involves the formulation of design requirements on basis of the values identified (phase 2) and the contextual analysis (phase 1). Here tools like the value hierarchy (van de Poel 2013) can be useful to mutually relate values and design requirements or to translate values into design requirements (Fig. 5). We suggest that the translation of values into design requirements is somewhat different for the different sets of values that were formulated in the second phase. The first set of values, derived for example from the SDGs are values that are to be promoted. They are typically translated into design requirements that are formulated as criteria that should be achieved as much as possible. The second set of values are values that need to be respected, in particular in relation to AI. Here the AI4SG principles are particularly helpful to formulate more specific design requirements. These requirements will most likely be formulated as constraints or boundary conditions rather than as criteria that should be achieved as much as possible; these boundary conditions set the deontological constraints that any design need to meet to be ethically (minimally) acceptable. For the third set of contextual values, the context analysis and in particular the stakeholder analysis will most likely play an important role in how these are to be translated into design requirements. VSD provides a principled and widely disseminated approach to aiding designers in putting such processes and abstract values into technical practice (see also Friedman et al. 2015; Umbrello and De Bellis 2018). 4. Prototyping. The fourth phase is the building of testing of prototypes that meet the design requirements. The idea is here in line with what is described more generally in VSD as value-oriented mock-up, prototype, or field deployment, which aims at the "development, analysis, and co-design of mockups, prototypes and field deployments to scaffold the investigation of value implications of technologies that are yet to be built or widely adopted. Mock-ups, prototypes or field deployments emphasize implications for direct and indirect stakeholders, value tensions, and technology situated in human contexts" (Friedman and Hendry 2019, 62). Our proposal is to extend this phase to he entire life-cycle of an AI technology because even if such technologies may initially meet value-based design requirements, they may develop in such that unexpected and undesirable effects materialise; or they no longer achieve the value for which they were intended or they may have unforeseen side effects, which requires additional values to be considered (van de Poel 2018). In such cases, there is a reason to do redesign the technology and to do another iteration of the cycle. In order to ensure the adoptability and illustrate the efficacy of this approach, we provide timely examples to more clearly show how the process works by situating it in figurative context for a specific AI system. Draft Version – 31 August 2020 16 6. AI4SG-VSD design process in action: SARS-CoV-2 Contact-Tracing Apps On Tuesday April 7th 2020, the Robert Koch Institute (RKI), the German federal research institute responsible for disease control and prevention, prompted German citizens with smartphones and smart watches to voluntarily share their health data to keep track of the spread of the COVID-19 virus (RKI 2020). The RKI is rolling out a new app called Corona Datenspende (Corona Data Donation) which allows users to voluntarily (and anonymously) share their health data to aid scientists in determining symptoms correlated with COVID-19 infections and its distribution across the nation, as well as to gauge the efficacy of the amelioration measures. The app allows the user to record their age, height, weight, gender, health metrics such as physical activity, body temperature, sleep behavior and heart rate, as well as their postal code. Lothar Wieler, head of the RKI, said that the collected information will "help to better estimate where and how fast Covid-19 is spreading in Germany," (Hollersen 2020). The RKI is explicit that the collected data of individual users are labeled as pseudonyms, and are thus anonymized to ensure that the personal information of users, such as names and addresses remain private. Likewise, machine learning system underlying the app are designed to: recognize symptoms that are associated with, among other things, a coronavirus infection. These include, for example, an increased resting heart rate and changes in sleep and activity behavior. The donated data will only be used for scientific purposes. After careful preparation, the data flows into a map that visually shows the spread of potentially infected people down to the zip code level. (RKI 2020) Although still in its infancy regarding its deployment stages, we can still illustrate the design of the Corona Datenspende, albeit ex post facto in this case, using the framework described above (i.e., Fig. 4). 1. Context As mentioned, VSD acknowledges that technology design can begin with the discrete technology itself as a starting point, the context of use or a certain value. In this case, the context of use can be understood as the motivating factor behind a technological solution. Simply put, the outbreak, spread, and eventual declaration of a global pandemic of COVID-19 provides the context of use and development. The immediate (health) crisis demands swift action to be taken in order to stifle further spreading, but also the desire to return to less strict measures at some point postpandemic. A prima facie analysis of the values at play here can be said to be tensions between more immediate public health and economic stability/prosperity The development of an app can specifically be targeted at trying to balance this tension, as a tracking and tracing app may assist in resuming certain societal activities, like travelling or work, in a way that still reduces health risk as much as possible by tracing who is potentially infected. 2. Value Identification 1. Values that are to be promoted by the design, for example deriving from the SDGs. Draft Version – 31 August 2020 17 The design of Corona Datenspende can be said to be part of a large network to support SDG #3 "Good health and Well-Being" which aims, among other objectives to focus on providing more efficient funding of health systems, improved sanitation and hygiene, increased access to physicians and more tips on ways to reduce ambient pollution. Albeit an impromptu technology introduced as a response to an immediate context, in situ deployment and use may encourage applications outside the original context (i.e., Outside of Germany, and also for other illnesses).2 2. Values that should be respected, in particular those values that have been identified in relation to AI: respect for human autonomy, prevention of harm (nonmaleficence), fairness and explicability Respect for Human Autonomy: In the context of AI systems, autonomy refers to the balance between the power human have in making decisions and how much of that power is abdicated to those systems. Not only should machines be designed in such a way as to promote human autonomy, but they should be designed also to constrain the abdication of too much human decision-making power, particularly where such human decision making outweighs the value of the efficacy of the machine's decision making capability (Floridi et al. 2018b). This is aligned with SDG #16 (Peace, Justice and Strong Institutions), particularly 16.7: Ensure responsive, inclusive, participatory and representative decision-making at all levels (United Nations 2019). Prevention of harm or (Nonmaleficence) is framed as preventing potential risks and harms from manifesting themselves in systems by understanding their capabilities and limits. Often questions of data privacy and security are evoked as to how individuals control their personal data (Floridi et al. 2018b). RKI is explicit that it does not collect personal user information beyond the level of postal codes (to understand transmission densities). However, privacy concerns still exist at the community level nonetheless, particularly in the practices used to store, use, share, archive, and destroy collected data. Risks of regional gerrymandering, targeted solicitation and/or discrimination are not excluded solely on account of delimiting data collection to the postal code level. Harm may also occur due to specific ways the apps is used, particularly if the app is not only used to map the spread of the virus, but also to trace individuals as potential bearers of the disease and 'risk factors.' We discuss these in more detail below under contextual values. Fairness: Although ambiguous, and often described and defined in different ways and specified across different points in the lifecycle of AI and their relations with human beings. Fairness can be understood as being framed as justice. Floridi et al. (2018b) sum various definitions justice in three ways: (1) Using AI to correct past wrongs such as 2 Goal 3 target 3.B more aptly aims to "Support the research and development of vaccines and medicines for the communicable and noncommunicable diseases that primarily affect developing countries, provide access to affordable essential medicines and vaccines, in accordance with the Doha Declaration on the TRIPS Agreement and Public Health, which affirms the right of developing countries to use to the full the provisions in the Agreement on Trade Related Aspects of Intellectual Property Rights regarding flexibilities to protect public health, and, in particular, provide access to medicines for all" (United Nations 2019). Draft Version – 31 August 2020 18 eliminating unfair discrimination; (2) Ensuring that the use of AI creates benefits that are shared (or at least shareable); and (3) Preventing the creation of new harms, such as the undermining of existing social structures. This is directly aligned with SDG #16 (Peace, Justice and Strong Institutions). More specifically: 16.3: Promote the rule of law at the national and international levels and ensure equal access to justice for all; as well as 6.A: Strengthen relevant national institutions, including through international cooperation, for building capacity at all levels, in particular in developing countries, to prevent violence and combat terrorism and crime 16.B: Promote and enforce non-discriminatory laws and policies for sustainable development (United Nations 2019) Explicability: the employed AI systems, in order to support the other values, must be explicable, this means that its inner workings must be intelligible (i.e., not opaque) and there must be at least one agent that is accountable for the way it works (they understand the way it works and are thus responsible for its actions) (Floridi et al. 2018b). 3) context-specific values that are not covered by 1) and 2) by which derive from the analysis of the specific context in phase, in particular values held by stakeholders. We will refer here to the development of the Dutch tracing and tracking app to illustrate how contextual values may be relevant for the development of such an app. In the Netherlands, sixty scientists and experts wrote an open letter to the Dutch government in which they warned for a number of risks and unintended effects of a tracing and tracking app (Muller et al. 2020). Among other things, they pointed out that such an app might lead to stigmatisation and discrimination, and might depending on how it would be used – endanger fundamental human rights like the right of association. They also draw attention to the fact that an app might give a false sense of security, which might lead to people no longer strictly following requirements for social distancing, which may increase, rather than decrease, health risks. Although it was announced by the German government that Corona Datenspende would be voluntary, scholars also pointed out that the app might nevertheless be used to allow access to certain services (like public transport) or might become required by employers for their employees, which would then endanger the voluntariness of use . Such potential uses might, in turn, also invite individuals to not properly use the app (e.g. in order to keep maximum freedom of movement) and to conceal certain contacts (by turning off their phone), which again might contribute to health risks. Many of the risks and potential side effects mentioned by scholars regarding SARS-CoV-2 apps map on the values we already discussed above, in particular health values (under 1) and nonmaleficence, justice, autonomy, and explicability (under 2). For example, a false sense of Draft Version – 31 August 2020 19 security relates to the value of health, and privacy and voluntariness to the value of autonomy, while stigmatisation and discrimination relate to fairness (e.g., Klenk and Duijf 2020; Rijksoverheid 2020; Sharon 2020). Nevertheless, there seem are also values like the right to association and, for example, security against hacking or misuse that are less clearly related to one of the values, although they can perhaps be subsumed under non-maleficence. Nevertheless, what the issues particularly show is that we should consider values in context in order to gain full awareness of what is at stake and how to translate these concerns into tangible (design) requirements. In this specific case, it is for example particularly important what behavioural effects apps will have, and it is also crucial to view the values in a broader system context. In this sense, even if a contextual value analysis may not reveal completely new values, it will nevertheless be crucial in understanding how values are exactly at stake for a specific application, how these values are to be understood in that specific case and how they translate into design requirements. 3. Formulating Design Requirements To illustrate how tools like the value hierarchy (Fig. 4) can be used to visualize and aid designers in translating abstract value into technical design requirements, we provide a specific instance of the tool below (Fig. 6). Of course, there are numerous iterations that occupy any given vector in the hierarchy; this is just one example. Fig. 6. Translating reduction of harm (nonmaleficence) into design requirements through AI4SG norms. Here, the value of nonmaleficence was chosen as the more abstract value that was then translated through two of the AI4SG principles (5 and 6) and then into technical design requirements. In this paradigm, AI4SG principles are adopted as norms, and rightly so, given that they are framed as imperatives by Floridi et al. (2020). Naturally, any given context of use, value and specific technology will implicate any number of combinations, and there is no exclusive nor exhaustive route for satisfying a value translation. Situational fairness could just as easily, and probably should, be used as the normative tool for operationalizing other values such as explicability (i.e., transparent dataset collection, use, storage and destruction (e.g., Yang et al. 2020)) as well as justice (i.e., promoting non-discriminatory laws and practices through unbiased compliance [e.g., using for example Fairness Warnings and/or Fair-MAML described by Slack et al. (2020)). At a functional level, the normative structure of the AI4SG principles supports avoiding (most) ethical harms associated with AI systems. However, they per se do not guarantee at all that new Design Requirments Norms Values Nonmaleficence Privacy protection and data subject consent [AI4Sg #5] Clear terms of use and UI/UX integration Pseudonymization of data subject information (e.g., GDPR 2016/679 [Recital 28]) Situational fairness [AI4SG #6] Avoid data sets that may result in biases towards lower-income neighbourhoods Modality for data subjiect information Rectification and erasure (e.g., GDPR Article 17) Draft Version – 31 August 2020 20 AI applications will actively contribute to social good. The higher level values listed above, in conjunctions with related real operationalization of SDGs allows more salient AI systems to be developed that contribute to social good (i.e., global beneficence). This multi-tiered approach of coupling AI specific values, stakeholder values and their application to SDG attainment via AI4SG principles can mitigate the dangers posed by ethical white-washing that occurs through the legitimisation of AI technologies that do not respect some fundamental ethical principles (Bietti 2020; Metzinger 2019; Sloane 2019). Regardless, this type of visualization can be used across different sources of values, as listed above, such as SDGs and stakeholder values, to determine how accurately related values can produce both similar and different technical design requirements. Future research projects could do this empirically, by taking any particular AI technology and provide thorough value-design requirement translations to determine the effectiveness of this approach. All in all, our aim here is to help designers to more effectively design for various values in mind, ones that are often times erroneously conflated or all together sidelined. 4. Prototyping Prototyping, as mentioned above, involves building mock-ups of the technology in question according to the design requirements laid out in the previous step. This means that the technology is moved from the more controlled space of the lab or design space and in situ, which, of course, implicates direct and indirect stakeholder values. At this point, various design decisions may prove to be recalcitrant, or unforeseen recalcitrant behavior emerges that implicates other values. At this point, given the technology's limited deployment, it can be recalled into the design space so that corrective modifications can be implemented. Regarding Corona Datenspende, the crisis situation that underlies the motivation behind the app's inception invites direct deployment rather than prototyping, given the stakes at play and the urgency for amelioration. Although tempting, this may ultimately be an unwise avenue to take, given the significant risks that AI systems possess, particularly ones predicated on such large quantities of data subjects. Small scale deployment or in-house testing of the efficacy and fidelity of the app's underlying system are a necessary (albeit not sufficient) condition for responsibly developing an AI system of this type to ensure that it can help to achieve positive ethical/societal values (i.e., beneficence, justice, explicability, autonomy and associated distal SDGs) while reducing the ethical (AI) risks (i.e., nonmaleficence). It should be particularly stressed that prototyping should not be restricted to testing the proper technical functioning of an app, but should take into account behaviour as well as societal effects and ultimately the effects of these on values. Here the tracing and tracking app is a case in point. While some value issues, like privacy, may be addressed through technical choices, like pseudonymization and anonymization, local storage of data, and automatic destruction of data after a certain period of time, some of the other value concerns require insight in the behaviour effects of such an app. Such behavioural effects are very hard, if not impossible, to reliably predict without some form of prototyping and, at least, small-scale testing in situ. It would therefore be advisable to go through a number of trials for such app that scale up from very small-scale testing with mock-ups to testing in test settings of an increasing size (not unlike what Draft Version – 31 August 2020 21 is done in medical experiments with new drugs). Such testing trajectories might also reveal new values that are at stake and need to be taken into account, and, so, trigger a new iteration of the development cycle. 6. Conclusion This paper discusses how AI systems can pose certain challenges for the value sensitive design approach to technology design. These challenges are primarily the consequences of the use of machine learning approaches to AI. ML poses two challenges to VSD. First, it may opaque (to humans) how an AI systems has learned certain things, which requires attention for such values as transparency, explainability and accountability. Second, ML may lead to AI systems adapting themselves in such ways that they 'disembody' the values that have been embodied in them by VSD designers. In order to deal with these challenges, we proposed an extension of VSD to the whole lifecycle of AI systems design. More specifically, we showed how the AI4SG principles proposed by Floridi et al can be integrated as norms in VSD when considering AI design. In order to integrate the AI4SG principles into a more systematic VSD approach, we have proposed a design process that consists of four, iterative, basic steps: contextual analysis, value identification, translation of values into design requirements and prototyping. At the core of this model is a two-tiered approach to values in AI consisting of a 1) real commitment to contributing to social good (i.e., beneficence) through AI and 2) the formulation and adherence to a number of concrete AI4SG principles. Without the first tier, AI4SG principles may help to avoid (most) ethical harm but there is no guarantee at all that new AI applications will actively contribute to social good; without the second tier there is a danger that contribution that societal challenge and SDGs are used to for legitimisation of AI technologies that do not respect some fundamental ethical principles, i.e. there is a danger of ethical white-washing (which is already visible ta the webpages of some large companies). In addition to these two tiers of values, we have argued that it is important to pay attention to contextual values, or at least to the contextual interpretation of the values from the two mentioned tiers. This is necessary to understand why certain values are at stake for a specific application and how to translate the relevant values into design requirements. References Al-Abdulkarim, L., Atkinson, K., & Bench-Capon, T. (2016). 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