The Role of Uncertainty in Assessing the Fairness of Machine Learning Models

Abstract:Machine learning models are widely used in clinical applications, social media, law enforcement and critical infrastructure. Verifying whether their outputs are biased against disadvantaged groups or individuals is crucial to ensuring they are fair and allowing their use in such settings. A rigorous risk assessment of possible fairness violations requires quantifying the uncertainty associated with selecting and estimating such models. Yet, this is rarely done in the literature, which focuses on identifying a single model with a suitable trade-off between predictive accuracy and fairness. In this paper, we move beyond point estimation and discuss frequentist and Bayesian approaches to uncertainty quantification for fair machine learning, with practical examples and implications for simulated and real data.

Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)

arXiv:2609.07959 [stat.ML]

(or arXiv:2609.07959v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2609.07959 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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