Given the growing concerns about bias in machine learning, dozens of metrics have been proposed to measure the fairness of machine learning. Several platforms have also been developed to compute and illustrate fairness metrics on platform-provided data. However, most platforms do not provide a user-friendly interface for users to upload and evaluate their own data or machine learning models. Moreover, no known platform is capable of training machine learning models, while considering their fairness and accuracy simultaneously. Motivated by the above insufficiency, this work develops FairerML, an extensible platform for analysing, visualising, and mitigating biases in machine learning. Three core functionalities are implemented in FairerML: fairness analysis of user-uploaded datasets, fairness analysis of user-uploaded machine learning models, and the training of a set of Pareto models considering accuracy and fairness metrics simultaneously by using multiobjective learning. The clear visualisation and description of the fairness analysis and the configurable model training process of FairerML make it easy for training fairer machine learning models and for educational purposes. In addition, new fairness metrics and training algorithms can be easily integrated into FairerML thanks to its extendability.

中文翻译：

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FairerML：用于分析、可视化和减轻机器学习偏差的可扩展平台 [应用说明]

鉴于人们对机器学习偏见的担忧日益增加，人们提出了数十种指标来衡量机器学习的公平性。还开发了几个平台来计算和说明平台提供的数据的公平性指标。然而，大多数平台并没有提供用户友好的界面供用户上传和评估自己的数据或机器学习模型。此外，还没有已知的平台能够在训练机器学习模型的同时同时考虑其公平性和准确性。受上述不足的启发，这项工作开发了 FairerML，这是一个用于分析、可视化和减轻机器学习偏差的可扩展平台。 FairerML 实现了三个核心功能：用户上传数据集的公平性分析、用户上传的机器学习模型的公平性分析以及通过使用多目标学习同时考虑准确性和公平性指标的一组 Pareto 模型的训练。 FairerML 的公平性分析清晰的可视化和描述以及可配置的模型训练过程，可以轻松训练更公平的机器学习模型并用于教育目的。此外，由于其可扩展性，新的公平性指标和训练算法可以轻松集成到 FairerML 中。