Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (2020)

Authors
Sina Fazelpour
Northeastern University
Abstract
Inspired by recent breakthroughs in predictive modeling, practitioners in both industry and government have turned to machine learning with hopes of operationalizing predictions to drive automated decisions. Unfortunately, many social desiderata concerning consequential decisions, such as justice or fairness, have no natural formulation within a purely predictive framework. In efforts to mitigate these problems, researchers have proposed a variety of metrics for quantifying deviations from various statistical parities that we might expect to observe in a fair world and offered a variety of algorithms in attempts to satisfy subsets of these parities or to trade o the degree to which they are satised against utility. In this paper, we connect this approach to fair machine learning to the literature on ideal and non-ideal methodological approaches in political philosophy. The ideal approach requires positing the principles according to which a just world would operate. In the most straightforward application of ideal theory, one supports a proposed policy by arguing that it closes a discrepancy between the real and the perfectly just world. However, by failing to account for the mechanisms by which our non-ideal world arose, the responsibilities of various decision-makers, and the impacts of proposed policies, naive applications of ideal thinking can lead to misguided interventions. In this paper, we demonstrate a connection between the fair machine learning literature and the ideal approach in political philosophy, and argue that the increasingly apparent shortcomings of proposed fair machine learning algorithms reflect broader troubles faced by the ideal approach. We conclude with a critical discussion of the harms of misguided solutions, a reinterpretation of impossibility results, and directions for future research
Keywords Ethics of Artificial Intelligence  Fair Machine Learning  Ideal Theory  Algorithmic Decision-Making
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References found in this work BETA

Justice as Fairness: A Restatement.John Rawls (ed.) - 2001 - Harvard University Press.
Ideal Vs. Non‐Ideal Theory: A Conceptual Map.Laura Valentini - 2012 - Philosophy Compass 7 (9):654-664.
“Ideal Theory” as Ideology.Charles W. Mills - 2005 - Hypatia 20 (3):165-183.

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Citations of this work BETA

Fair Machine Learning Under Partial Compliance.Jessica Dai, Sina Fazelpour & Zachary Lipton - 2021 - In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. pp. 55–65.

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