Data preprocessing to mitigate bias: A maximum entropy based approach

TL;DR

This paper introduces a maximum entropy-based data preprocessing algorithm that effectively mitigates bias related to social attributes, ensuring fairer representations while maintaining classifier accuracy.

Contribution

It develops a scalable maximum entropy framework for bias mitigation that adjusts group representations and achieves fairness metrics with minimal accuracy loss.

Findings

Samples from the learned distribution meet fairness criteria

The method efficiently handles large domains

Classifier training with adjusted data maintains high accuracy

Abstract

Data containing human or social attributes may over- or under-represent groups with respect to salient social attributes such as gender or race, which can lead to biases in downstream applications. This paper presents an algorithmic framework that can be used as a data preprocessing method towards mitigating such bias. Unlike prior work, it can efficiently learn distributions over large domains, controllably adjust the representation rates of protected groups and achieve target fairness metrics such as statistical parity, yet remains close to the empirical distribution induced by the given dataset. Our approach leverages the principle of maximum entropy - amongst all distributions satisfying a given set of constraints, we should choose the one closest in KL-divergence to a given prior. While maximum entropy distributions can succinctly encode distributions over large domains, they can be difficult to compute. Our main contribution is an instantiation of this framework for our set of constraints and priors, which encode our bias mitigation goals, and that runs in time polynomial in the dimension of the data. Empirically, we observe that samples from the learned distribution have desired representation rates and statistical rates, and when used for training a classifier incurs only a slight loss in accuracy while maintaining fairness properties.