Increasing Fairness via Combination with Learning Guarantees
This work addresses fairness in ML for real-world applications, but it is incremental as it builds on existing fairness measures and ensemble methods.
The paper tackled the problem of hidden discrimination in ML models by proposing a new fairness measure called 'discriminative risk' that combines individual and group fairness aspects, and established theoretical guarantees for boosting fairness via ensemble methods, with experiments showing effectiveness in binary and multi-class classification.
The concern about hidden discrimination in ML models is growing, as their widespread real-world application increasingly impacts human lives. Various techniques, including commonly used group fairness measures and several fairness-aware ensemble-based methods, have been developed to enhance fairness. However, existing fairness measures typically focus on only one aspect -- either group or individual fairness, and the hard compatibility among them indicates a possibility of remaining biases even when one of them is satisfied. Moreover, existing mechanisms to boost fairness usually present empirical results to show validity, yet few of them discuss whether fairness can be boosted with certain theoretical guarantees. To address these issues, we propose a fairness quality measure named 'discriminative risk (DR)' to reflect both individual and group fairness aspects. Furthermore, we investigate its properties and establish the first- and second-order oracle bounds to show that fairness can be boosted via ensemble combination with theoretical learning guarantees. The analysis is suitable for both binary and multi-class classification. A pruning method is also proposed to utilise our proposed measure and comprehensive experiments are conducted to evaluate the effectiveness of the proposed methods.