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Fairness Through Awareness
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In this article, the authors proposed a framework for fair classification comprising a task-specific metric for determining the degree to which individuals are similar with respect to the classification task at hand, and an algorithm for maximizing utility subject to the fairness constraint that similar individuals are treated similarly.Abstract:
We study fairness in classification, where individuals are classified, e.g., admitted to a university, and the goal is to prevent discrimination against individuals based on their membership in some group, while maintaining utility for the classifier (the university). The main conceptual contribution of this paper is a framework for fair classification comprising (1) a (hypothetical) task-specific metric for determining the degree to which individuals are similar with respect to the classification task at hand; (2) an algorithm for maximizing utility subject to the fairness constraint, that similar individuals are treated similarly. We also present an adaptation of our approach to achieve the complementary goal of "fair affirmative action," which guarantees statistical parity (i.e., the demographics of the set of individuals receiving any classification are the same as the demographics of the underlying population), while treating similar individuals as similarly as possible. Finally, we discuss the relationship of fairness to privacy: when fairness implies privacy, and how tools developed in the context of differential privacy may be applied to fairness.read more
Citations
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References
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Book ChapterDOI
Calibrating noise to sensitivity in private data analysis
TL;DR: In this article, the authors show that for several particular applications substantially less noise is needed than was previously understood to be the case, and also show the separation results showing the increased value of interactive sanitization mechanisms over non-interactive.
Posted Content
Incorporating Fairness into Game Theory and Economics
TL;DR: In this article, it is shown that every mutual-max or mutual-min Nash equilibrium is a fairness equilibrium, and that if payoffs are small, fairness equilibria are roughly the set of mutualmax and mutualmin outcomes; if payoff are large, fairness equilibrium are roughly a set of Nash equilibra.
Journal Article
Calibrating noise to sensitivity in private data analysis
TL;DR: The study is extended to general functions f, proving that privacy can be preserved by calibrating the standard deviation of the noise according to the sensitivity of the function f, which is the amount that any single argument to f can change its output.
Proceedings ArticleDOI
Mechanism Design via Differential Privacy
Frank McSherry,Kunal Talwar +1 more
TL;DR: It is shown that the recent notion of differential privacv, in addition to its own intrinsic virtue, can ensure that participants have limited effect on the outcome of the mechanism, and as a consequence have limited incentive to lie.