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arXiv · 1803.03239

Fairness Through Computationally-Bounded Awareness

Michael P. Kim·Omer Reingold·Guy N. Rothblum
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Abstract

We study the problem of fair classification within the versatile framework of Dwork et al. [ITCS '12], which assumes the existence of a metric that measures similarity between pairs of individuals. Unlike earlier work, we do not assume that the entire metric is known to the learning algorithm; instead, the learner can query this arbitrary metric a bounded number of times. We propose a new notion of fairness called metric multifairness and show how to achieve this notion in our setting. Metric multifairness is parameterized by a similarity metric $d$ on pairs of individuals to classify and a rich collection ${\cal C}$ of (possibly overlapping) "comparison sets" over pairs of individuals. At a high level, metric multifairness guarantees that similar subpopulations are treated similarly, as long as these subpopulations are identified within the class ${\cal C}$.

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Michael P. Kim, Omer Reingold, Guy N. Rothblum. 2018-03-08. Fairness Through Computationally-Bounded Awareness. https://arxiv.org/abs/1803.03239

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