Statistical Guarantees for Fairness Aware Plug-In Algorithms

Drona Khurana; Srinivasan Ravichandran; Sparsh Jain; Narayanan Unny Edakunni

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arXiv · 2107.12783

Statistical Guarantees for Fairness Aware Plug-In Algorithms

Drona Khurana·Srinivasan Ravichandran·Sparsh Jain·Narayanan Unny Edakunni

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Abstract

A plug-in algorithm to estimate Bayes Optimal Classifiers for fairness-aware binary classification has been proposed in (Menon & Williamson, 2018). However, the statistical efficacy of their approach has not been established. We prove that the plug-in algorithm is statistically consistent. We also derive finite sample guarantees associated with learning the Bayes Optimal Classifiers via the plug-in algorithm. Finally, we propose a protocol that modifies the plug-in approach, so as to simultaneously guarantee fairness and differential privacy with respect to a binary feature deemed sensitive.

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Drona Khurana, Srinivasan Ravichandran, Sparsh Jain, Narayanan Unny Edakunni. 2021-07-27. Statistical Guarantees for Fairness Aware Plug-In Algorithms. https://arxiv.org/abs/2107.12783

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