Nov 27, 2023 · This paper proposes a framework to ensure fairness of classifiers while handling the uncertainty from point estimates using finite samples. In ...
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In this paper, we use the covariance as a proxy for the fairness and develop the confidence region of the covariance vector using empirical likelihood. (Owen, ...
Mar 14, 2024 · In this paper, we use the covariance as a proxy for the fairness and develop the confidence region of the covariance vector using empirical ...
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Simulation studies show that the method exactly covers the target Type I error rate and effectively balances the trade-off between accuracy and fairness.
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Sep 30, 2023 · In this paper, we closely study and empirically evaluate existing work on fair classification, across different research communities, with two ...
We construct a measure of fairness in calibration to estimate the extent to which observed event rates conditioned on the predicted risk differ across groups.
Sep 30, 2024 · Non-causal metrics depend entirely on empirical data and look for statistical relationships between the sensitive attribute and the prediction.
We address the problem of algorithmic fairness: ensuring that sensitive informa- tion does not unfairly influence the outcome of a classifier.
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Dec 6, 2023 · Training and deploying machine learning models that meet fairness criteria for protected groups are fundamental in modern artificial ...