Uncertainty in Fairness Assessment: Maintaining Stable Conclusions Despite Fluctuations
This work addresses the challenge of reliable fairness assessment for machine learning practitioners, though it is incremental as it builds on existing Bayesian methods.
The paper tackles the problem of unstable fairness and performance assessments in classification algorithms by proposing the Uncertainty Matters (UM) framework, which uses a Bayesian approach to derive posterior distributions for criteria combinations, resulting in improved informativeness and stability in evaluations.
Several recent works encourage the use of a Bayesian framework when assessing performance and fairness metrics of a classification algorithm in a supervised setting. We propose the Uncertainty Matters (UM) framework that generalizes a Beta-Binomial approach to derive the posterior distribution of any criteria combination, allowing stable performance assessment in a bias-aware setting.We suggest modeling the confusion matrix of each demographic group using a Multinomial distribution updated through a Bayesian procedure. We extend UM to be applicable under the popular K-fold cross-validation procedure. Experiments highlight the benefits of UM over classical evaluation frameworks regarding informativeness and stability.