The Sharpe predictor for fairness in machine learning
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 con icting objectives to be optimized simultaneously. The entire trade-offs range is dened as the Pareto front of the SMOO problem, which can then be effciently computed using stochastic-gradient type algorithms. SMOO also allows dening 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).
Keywords: Fair Machine Learning, Sharpe Ratio, Prediction Risk, Sharpe Predictor, Pareto Front.
Category 1: Stochastic Programming
Category 2: Other Topics (Multi-Criteria Optimization )
Category 3: Convex and Nonsmooth Optimization
Citation: S. Liu and L. N. Vicente, The Sharpe predictor for fairness in machine learning, ISE Technical Report 21T-019, Lehigh University.
Entry Submitted: 08/11/2021
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