论文标题
Fairlib：评估和改善分类公平性的统一框架
fairlib: A Unified Framework for Assessing and Improving Classification Fairness
论文作者
论文摘要
本文介绍了Fairlib，这是一个用于评估和改善分类公平性的开源框架。它提供了一个系统的框架，用于快速再现现有的基线模型，开发新方法，评估不同指标的模型以及可视化其结果。它的模块化和可扩展性使该框架可以用于不同类型的输入，包括自然语言，图像和音频。详细说明，我们实施了14种偏见方法，包括预处理，训练时间和后处理方法。内置指标涵盖了最常用的公平标准，可以进一步概括和定制以进行公平评估。
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.
