Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: What does it mean for an algorithm to be fair? Different papers use different notions of algorithmic fairness, and although these appear internally consistent, they also seem mutually incompatible.  present a mathematical setting in which the distinctions in previous papers can be made formal. In addition to characterizing the spaces of inputs (the "observed" space) and outputs (the "decision" space),  introduce the notion of a construct space: a space that captures unobservable, but meaningful variables for the prediction. #R##N# show that in order to prove desirable properties of the entire decision-making process, different mechanisms for fairness require different assumptions about the nature of the mapping from construct space to decision space. The results in this paper imply that future treatments of algorithmic fairness should more explicitly state assumptions about the relationship beten constructs and observations.
['Sorelle A. Friedler', 'Carlos Eduardo Scheidegger', 'Suresh Venkatasubramanian']
I am writing a research paper on a idea On the (im)possibility of fairness
[{"from": "human", "value": "I am writing a research paper on a idea On the (im)possibility of fairness"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: What does it mean for an algorithm to be fair? Different papers use different notions of algorithmic fairness, and although these appear internally consistent, they also seem mutually incompatible.  present a mathematical setting in which the distinctions in previous papers can be made formal. In addition to characterizing the spaces of inputs (the \"observed\" space) and outputs (the \"decision\" space),  introduce the notion of a construct space: a space that captures unobservable, but meaningful variables for the prediction. #R##N# show that in order to prove desirable properties of the entire decision-making process, different mechanisms for fairness require different assumptions about the nature of the mapping from construct space to decision space. The results in this paper imply that future treatments of algorithmic fairness should more explicitly state assumptions about the relationship beten constructs and observations."}]