Work

Statistical Guarantees for Fairness Aware Plug-In Algorithms

Year: 2021

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2107.12783

Authors: Khurana, Drona, Ravichandran, Srinivasan, Jain, Sparsh, Edakunni, Narayanan Unny

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: green

OpenAlex ID: W3183839669

Cites: 23

Cited by:

Related to: 10