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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