  This paper introduces the Fair Fairness Benchmark (\textsf{FFB}), a
benchmarking framework for in-processing group fairness methods. Ensuring
fairness in machine learning is important for ethical compliance. However,
there exist challenges in comparing and developing fairness methods due to
inconsistencies in experimental settings, lack of accessible algorithmic
implementations, and limited extensibility of current fairness packages and
tools. To address these issues, we introduce an open-source standardized
benchmark for evaluating in-processing group fairness methods and provide a
comprehensive analysis of state-of-the-art methods to ensure different notions
of group fairness. This work offers the following key contributions: the
provision of flexible, extensible, minimalistic, and research-oriented
open-source code; the establishment of unified fairness method benchmarking
pipelines; and extensive benchmarking, which yields key insights from
$\mathbf{45,079}$ experiments, $\mathbf{14,428}$ GPU hours. We believe that our
work will significantly facilitate the growth and development of the fairness
research community.
