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This paper introduces the Fair Fairness Benchmark (\textsf{FFB}), abenchmarking framework for in-processing group fairness methods. Ensuringfairness in machine learning is critical for ethical and legal compliance.However, there exist challenges in comparing and developing of fairness methodsdue to inconsistencies in experimental settings, lack of accessible algorithmicimplementations, and limited extensibility of current fairness packages andtools. To address these issues, we introduce an open-source, standardizedbenchmark for evaluating in-processing group fairness methods and provide acomprehensive analysis of state-of-the-art methods to ensure different notionsof group fairness. This work offers the following key contributions: theprovision of flexible, extensible, minimalistic, and research-orientedopen-source code; the establishment of unified fairness method benchmarkingpipelines; and extensive benchmarking, which yields key insights from$\mathbf{45,079}$ experiments. We believe our work will significantlyfacilitate the growth and development of the fairness research community. Thebenchmark, including code and running logs, is available athttps://github.com/ahxt/fair_fairness_benchmark

FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods