Uncertainty-based Fairness Measures
This work addresses fairness issues in machine learning for real-world applications, offering incremental improvements by extending fairness measures to account for uncertainties.
The paper tackles the problem of fairness in machine learning models by showing that existing fairness measures, which rely on point predictions, can overlook biases in prediction uncertainties, and introduces new fairness measures based on aleatoric and epistemic uncertainties to complement existing approaches and provide deeper insights into bias.
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 an 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.