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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Vydané v:	Optimization letters Ročník 18; číslo 9; s. 1975 - 1991
Hlavní autori:	Curtis, Frank E., Liu, Suyun, Robinson, Daniel P.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024
Predmet:	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
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