We present FairX, an open-source Python-based benchmarking tool designed for the comprehensive analysis of models under the umbrella of fairness, utility, and eXplainability (XAI). FairX enables users to train benchmarking bias-removal models and evaluate their fairness using a wide array of fairness metrics, data utility metrics, and generate explanations for model predictions, all within a unified framework. Existing benchmarking tools do not have the way to evaluate synthetic data generated from fair generative models, also they do not have the support for training fair generative models either. In FairX, we add fair generative models in the collection of our fair-model library (pre-processing, in-processing, post-processing) and evaluation metrics for evaluating the quality of synthetic fair data. This version of FairX supports both tabular and image datasets. It also allows users to provide their own custom datasets. The open-source FairX benchmarking package is publicly available at https://github.com/fahim-sikder/FairX.

翻译：本文提出FairX，一个基于Python的开源基准测试工具，旨在从公平性、效用与可解释性（XAI）的综合视角对模型进行分析。FairX使用户能够在统一框架内训练基准去偏模型，利用广泛的公平性指标与数据效用指标评估其公平性，并为模型预测生成解释。现有基准测试工具既无法评估来自公平生成模型生成的合成数据，也不支持训练公平生成模型。在FairX中，我们在公平模型库（预处理、处理中、后处理）中加入了公平生成模型，并提供了用于评估合成公平数据质量的评估指标。此版本FairX同时支持表格与图像数据集，并允许用户提供自定义数据集。开源FairX基准测试包已在https://github.com/fahim-sikder/FairX 公开提供。