ABCFair: an Adaptable Benchmark approach for Comparing Fairness Methods
This provides a standardized tool for researchers and practitioners to compare fairness methods in diverse real-world scenarios, though it is incremental as it builds on existing benchmarking efforts.
The authors tackled the challenge of benchmarking fairness methods in machine learning due to varying problem settings, and introduced ABCFair, an adaptable benchmark approach that enables proper comparability across methods, applied to various datasets to avoid the fairness-accuracy trade-off.
Numerous methods have been implemented that pursue fairness with respect to sensitive features by mitigating biases in machine learning. Yet, the problem settings that each method tackles vary significantly, including the stage of intervention, the composition of sensitive features, the fairness notion, and the distribution of the output. Even in binary classification, these subtle differences make it highly complicated to benchmark fairness methods, as their performance can strongly depend on exactly how the bias mitigation problem was originally framed. Hence, we introduce ABCFair, a benchmark approach which allows adapting to the desiderata of the real-world problem setting, enabling proper comparability between methods for any use case. We apply ABCFair to a range of pre-, in-, and postprocessing methods on both large-scale, traditional datasets and on a dual label (biased and unbiased) dataset to sidestep the fairness-accuracy trade-off.