  This paper presents fairlib, an open-source framework for assessing and
improving classification fairness. It provides a systematic framework for
quickly reproducing existing baseline models, developing new methods,
evaluating models with different metrics, and visualizing their results. Its
modularity and extensibility enable the framework to be used for diverse types
of inputs, including natural language, images, and audio. In detail, we
implement 14 debiasing methods, including pre-processing, at-training-time, and
post-processing approaches. The built-in metrics cover the most commonly used
fairness criterion and can be further generalized and customized for fairness
evaluation.
