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.
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BibTeX
@misc{panero2026role,
title = {{The Role of Uncertainty in Assessing the Fairness of Machine Learning Models}},
author = {Francesca Panero and Ernst C. Wit and Marco Scutari},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.07959},
eprint = {2609.07959},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.07959},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - The Role of Uncertainty in Assessing the Fairness of Machine Learning Models
AU - Francesca Panero
AU - Ernst C. Wit
AU - Marco Scutari
PY - 2026
DA - 2026/09/07
PB - arXiv
DO - 10.48550/arxiv.2609.07959
UR - https://arxiv.org/abs/2609.07959
AB - 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.
ER -