mlsanityData to 14 Oct 2026

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

arXiv 2609.07959 ↗First posted 7 Sep 2026stat.ML cs.LG stat.MEcited by 0

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Abstract (arXiv)

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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References · 17

  1. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  2. 2017Counterfactual Fairnesscited by 804
  3. 2019Identifying and Correcting Label Bias in Machine Learningcited by 146
  4. 2019Inherent Tradeoffs in Learning Fair Representationscited by 144
  5. 2021Towards a Unified Framework for Fair and Stable Graph Representation Learningcited by 128
  6. 2020Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantificationcited by 78
  7. 2020Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inferencecited by 31
  8. 2022A review of predictive uncertainty estimation with machine learningcited by 29
  9. 2021Fairness in Ranking under Uncertaintycited by 27
  10. 2019General Fair Empirical Risk Minimizationcited by 23
  11. 2019A Causal Bayesian Networks Viewpoint on Fairnesscited by 19
  12. 2023Fairness through Aleatoric Uncertaintycited by 16
5 more
  1. 2023FRAPPE: A Group Fairness Framework for Post-Processing Everythingcited by 16
  2. 2021Achieving Fairness with a Simple Ridge Penaltycited by 13
  3. 2023Individual Fairness under Uncertaintycited by 11
  4. 2023fairml: A Statistician's Take on Fair Machine Learning Modellingcited by 5
  5. 2026Fairness under uncertainty in sequential decisionscited by 2

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