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Thorsten Schmidt

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#machine learning Preprint May 2024

When fairness metrics fail: A utility-based perspective on $\varepsilon$-fairness

A utility-based framework that incorporates the consequences of decisions for the affected individuals and groups into the assessment of fairness and shows that a decision-making process can satisfy $\varepsilon$-fairness while nevertheless being maximally unfair once the utilities associated with its outcomes are take...

T. Fadina, Thorsten Schmidt · 0 citations

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