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We study the problem of classifier derandomization in machine learning: given a stochastic binary classifier f:X→[0,1], sample a deterministic classifier f̂ :X→{0,1} that approximates the output of f in aggregate over any data distribution. Recent work revealed how to efficiently derandomize a stochastic classifier with strong output approximation guarantees, but at the cost of individual fairness -- that is, if f treated similar inputs similarly, f̂ did not. In this paper, we initiate a systematic study of classifier derandomization with metric fairness guarantees. We show that the prior derandomization approach is almost maximally metric-unfair, and that a simple ``random threshold'' derandomization achieves optimal fairness preservation but with weaker output approximation. We then devise a derandomization procedure that provides an appealing tradeoff between these two: if f is α-metric fair according to a metric d with a locality-sensitive hash (LSH) family, then our derandomized f̂ is, with high probability, O(α)-metric fair and a close approximation of f. We also prove generic results applicable to all (fair and unfair) classifier derandomization procedures, including a bias-variance decomposition and reductions between various notions of metric fairness.
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Achieving Fair Treatment in Algorithmic Classification
Fairness in classification has become an increasingly relevant and controversial issue as com- puters replace humans in many of today’s classification tasks. In particular, a subject of much recent debate is that of finding, and subsequently achieving, suitable definitions of fairness in an algorithmic context. In this work, following the work of Hardt et al. (NIPS’16), we consider and formalize the task of sanitizing an unfair classifier C into a classifier C′ satisfying an approximate notion of “equalized odds” or fair treatment. Our main result shows how to take any (possibly unfair) classifier C over a finite outcome space, and transform it—by just perturbing the out- put of C—according to some distribution learned by just having black-box access to samples of labeled, and previously classified, data, to produce a classifier C′ that satisfies fair treatment; we additionally show that our derived classifier is near-optimal in terms of accuracy. We also experimentally evaluate the performance of our method.
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- Award ID(s):
- 1703846
- PAR ID:
- 10098363
- Date Published:
- Journal Name:
- Theory of Cryptography Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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