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

Abstract:Fairness in decision-making processes is often quantified using probabilistic metrics. However, these metrics need not reflect the consequences of decisions for the affected individuals and groups. We develop a utility-based framework that incorporates these consequences into the assessment of fairness. Our main result shows that a decision-making process can satisfy $\varepsilon$-fairness while nevertheless being maximally unfair once the utilities associated with its outcomes are taken into account. To address applications in which information on false negatives is unavailable, we also formulate a reduced setting that retains the essential elements of the utility-based fairness assessment. We illustrate the framework through two applications: college admissions and credit-risk assessment. In both cases, probabilistic metrics may classify a decision-making process as approximately fair even though the corresponding utility outcomes are highly unequal. In the college-admissions example, our analysis shows that improving completion rates is necessary to achieve equality of utility across groups, while in the mortgage example, mitigating unfairness requires not only adjusting approval rates but also reducing the adverse consequences of default. These findings demonstrate that fairness assessments should account not only for the probabilities of different decisions but also for the consequences of those decisions.

Revised version with a new title, updated results, and additional references. The main conclusions remain unchanged

Machine Learning (cs.LG); Theoretical Economics (econ.TH); Mathematical Finance (q-fin.MF); Machine Learning (stat.ML)

arXiv:2405.09360 [cs.LG]

(or arXiv:2405.09360v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2405.09360 Focus to learn more arXiv-issued DOI via DataCite

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