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- W4297663312 abstract "We address the problem of algorithmic fairness: ensuring that sensitive variables do not unfairly influence the outcome of a classifier. We present an approach based on empirical risk minimization, which incorporates a fairness constraint into the learning problem. It encourages the conditional risk of the learned classifier to be approximately constant with respect to the sensitive variable. We derive both risk and fairness bounds that support the statistical consistency of our approach. We specify our approach to kernel methods and observe that the fairness requirement implies an orthogonality constraint which can be easily added to these methods. We further observe that for linear models the constraint translates into a simple data preprocessing step. Experiments indicate that the method is empirically effective and performs favorably against state-of-the-art approaches." @default.
- W4297663312 created "2022-09-30" @default.
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- W4297663312 date "2018-02-23" @default.
- W4297663312 modified "2023-09-26" @default.
- W4297663312 title "Empirical Risk Minimization under Fairness Constraints" @default.
- W4297663312 doi "https://doi.org/10.48550/arxiv.1802.08626" @default.
- W4297663312 hasPublicationYear "2018" @default.
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