Finding Representative Group Fairness Metrics Using Correlation Estimations
This work addresses the challenge for practitioners and data scientists in choosing appropriate fairness metrics to mitigate bias in predictive models, though it is incremental as it builds on existing fairness notions.
The authors tackled the problem of selecting fairness metrics by proposing a framework that estimates correlations among them to identify a representative subset, reducing the burden of exploring combinatorial spaces, and validated it with experiments on real-world datasets.
It is of critical importance to be aware of the historical discrimination embedded in the data and to consider a fairness measure to reduce bias throughout the predictive modeling pipeline. Given various notions of fairness defined in the literature, investigating the correlation and interaction among metrics is vital for addressing unfairness. Practitioners and data scientists should be able to comprehend each metric and examine their impact on one another given the context, use case, and regulations. Exploring the combinatorial space of different metrics for such examination is burdensome. To alleviate the burden of selecting fairness notions for consideration, we propose a framework that estimates the correlation among fairness notions. Our framework consequently identifies a set of diverse and semantically distinct metrics as representative for a given context. We propose a Monte-Carlo sampling technique for computing the correlations between fairness metrics by indirect and efficient perturbation in the model space. Using the estimated correlations, we then find a subset of representative metrics. The paper proposes a generic method that can be generalized to any arbitrary set of fairness metrics. We showcase the validity of the proposal using comprehensive experiments on real-world benchmark datasets.