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Uncertainty as a Fairness Measure
Date
2024-01-01
Author
Kuzucu, Selim
Cheong, Jiaee
Gunes, Hatice
Kalkan, Sinan
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Unfair predictions of machine learning (ML) models impede their broad acceptance in real-world settings. Tackling this arduous challenge first necessitates defining what it means for an ML model to be fair. This has been addressed by the ML community with various measures of fairness that depend on the prediction outcomes of the ML models, either at the group-level or the individual-level. These fairness measures are limited in that they utilize point predictions, neglecting their variances, or uncertainties, making them susceptible to noise, missingness and shifts in data. In this paper, we first show that a ML model may appear to be fair with existing point-based fairness measures but biased against a demographic group in terms of prediction uncertainties. Then, we introduce new fairness measures based on different types of uncertainties, namely, aleatoric uncertainty and epistemic uncertainty. We demonstrate on many datasets that (i) our uncertainty-based measures are complementary to existing measures of fairness, and (ii) they provide more insights about the underlying issues leading to bias.
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85208283964&origin=inward
https://hdl.handle.net/11511/112511
Journal
Journal of Artificial Intelligence Research
DOI
https://doi.org/10.1613/jair.1.16041
Collections
Department of Computer Engineering, Article
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BibTeX
S. Kuzucu, J. Cheong, H. Gunes, and S. Kalkan, “Uncertainty as a Fairness Measure,”
Journal of Artificial Intelligence Research
, vol. 81, pp. 307–335, 2024, Accessed: 00, 2024. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85208283964&origin=inward.