Fair machine learning through constrained stochastic optimization and an ϵ-constraint method

A strategy for fair supervised learning is proposed. It involves formulating an optimization problem to minimize loss subject to a prescribed bound on a measure of unfairness (e.g., disparate impact). It can be embedded within an ϵ -constraint method for multiobjective optimization, allowing one to...

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Veröffentlicht in:Optimization letters Jg. 18; H. 9; S. 1975 - 1991
Hauptverfasser: Curtis, Frank E., Liu, Suyun, Robinson, Daniel P.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024
Schlagworte:
Computational Intelligence
Mathematics
Mathematics and Statistics
Numerical and Computational Physics
Operations Research/Decision Theory
Optimization
Original Paper
Simulation
Stochastic multiobjective optimization
Fair machine learning
ISSN:1862-4472, 1862-4480
Online-Zugang:Volltext
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Zusammenfassung:A strategy for fair supervised learning is proposed. It involves formulating an optimization problem to minimize loss subject to a prescribed bound on a measure of unfairness (e.g., disparate impact). It can be embedded within an ϵ -constraint method for multiobjective optimization, allowing one to produce a Pareto front for minimizing loss and unfairness. A stochastic optimization algorithm, designed to be scalable for large data settings, is proposed for solving the arising constrained optimization problems. Numerical experiments on problems pertaining to predicting recidivism and income provide evidence that the strategy can be effective for large-scale fair learning.
ISSN:1862-4472
1862-4480
DOI:10.1007/s11590-023-02024-6