[2001.09773] Algorithmic Fairness from a Non-ideal Perspectivecontact arXivarXiv Twitter

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 off the degree to which they are satisfied against utility. In this paper, we connect this approach to \emph{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 m

2 mentions: @zacharylipton
Keywords: fairness
Date: 2020/01/28 20:20

Referring Tweets

@zacharylipton *Our paper diagnosing problems in fair ML now on arXiv!* t.co/HhhpKEKrCM. Took a few weeks, b/c our interdisciplinary collaboration broke the paper categorization system. 😂 #aies2020

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