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.

翻译：本文件介绍了公平性,这是一个用于评估和改进分类公正性的开放源码框架,为快速复制现有基线模型、制定新方法、用不同计量标准评价模型和将结果直观化提供了一个系统框架,其模块性和可扩展性使框架能够用于各种投入,包括自然语言、图像和音频,详细而言,我们采用了14种贬低方法,包括预处理、培训时间和后处理方法,内置计量标准涵盖最常用的公平标准,可以进一步普及和定制用于公平评价。