The Sharpe predictor for fairness in machine learning

TL;DR

This paper introduces the Sharpe predictor, a novel fairness metric in machine learning that optimizes the ratio of accuracy to unfairness, using a stochastic multi-objective optimization framework inspired by finance.

Contribution

It proposes the Sharpe predictor as a new fairness metric and demonstrates its effectiveness within a stochastic multi-objective optimization approach for fair ML.

Findings

Sharpe predictor maximizes accuracy-to-unfairness ratio

Efficient computation of Pareto front for fairness trade-offs

Improved fairness-accuracy trade-off in ML models

Abstract

In machine learning (ML) applications, unfair predictions may discriminate against a minority group. Most existing approaches for fair machine learning (FML) treat fairness as a constraint or a penalization term in the optimization of a ML model, which does not lead to the discovery of the complete landscape of the trade-offs among learning accuracy and fairness metrics, and does not integrate fairness in a meaningful way. Recently, we have introduced a new paradigm for FML based on Stochastic Multi-Objective Optimization (SMOO), where accuracy and fairness metrics stand as conflicting objectives to be optimized simultaneously. The entire trade-offs range is defined as the Pareto front of the SMOO problem, which can then be efficiently computed using stochastic-gradient type algorithms. SMOO also allows defining and computing new meaningful predictors for FML, a novel one being the Sharpe predictor that we introduce and explore in this paper, and which gives the highest ratio of accuracy-to-unfairness. Inspired from SMOO in finance, the Sharpe predictor for FML provides the highest prediction return (accuracy) per unit of prediction risk (unfairness).