Unfair Utilities and First Steps Towards Improving Them

Abstract:Many fairness criteria constrain the policy or choice of predictors, which can have unwanted consequences, in particular, when optimizing the policy under such constraints. Here, we in- stead suggest that fairness can be directly analyzed as a property of the utility function. Instead of imposing fairness constraints on the policy, we suggest to simply maximize a utility function satisfying certain fairness properties. Concretely, we define value of information fairness, which prescribes that there must not be an incentive to infer the protected attribute. This principle sug- gests modifying utility functions such that they satisfy value of information fairness. We describe how such modifications can be achieved and discuss consequences for the corresponding optimal policies. We apply our framework to thought experiments and the COMPAS data, demonstrating that focusing on utility functions sometimes provides answers that better align with intuitive judg- ments about what is fair. Moreover, we are not aware of any intuitively fair policy that violates value of information fairness; and when we find that value of information fairness recommends an intuitively unfair policy, no realizable policy is intuitively fair.

Machine Learning (stat.ML); Computers and Society (cs.CY); Machine Learning (cs.LG)

arXiv:2306.00636 [stat.ML]

(or arXiv:2306.00636v3 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2306.00636 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1515/jci-2025-0013 Focus to learn more DOI(s) linking to related resources

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