Aequitas Flow is an open-source framework and toolkit for end-to-end Fair Machine Learning (ML) experimentation, and benchmarking in Python. This package fills integration gaps that exist in other fair ML packages. In addition to the existing audit capabilities in Aequitas, the Aequitas Flow module provides a pipeline for fairness-aware model training, hyperparameter optimization, and evaluation, enabling easy-to-use and rapid experiments and analysis of results. Aimed at ML practitioners and researchers, the framework offers implementations of methods, datasets, metrics, and standard interfaces for these components to improve extensibility. By facilitating the development of fair ML practices, Aequitas Flow hopes to enhance the incorporation of fairness concepts in AI systems making AI systems more robust and fair.

翻译：Aequitas Flow 是一个用于端到端公平机器学习（ML）实验和基准测试的开源框架与工具包，基于 Python 实现。该软件包弥补了其他公平 ML 工具包在集成方面的不足。除了 Aequitas 现有的审计功能外，Aequitas Flow 模块还提供了一个支持公平性考量的模型训练、超参数优化和评估的流水线，使得实验与结果分析更易于使用且快速。该框架面向机器学习从业者和研究人员，提供了多种方法、数据集、度量的实现以及这些组件的标准接口，以提高可扩展性。通过促进公平 ML 实践的发展，Aequitas Flow 旨在推动公平性概念在 AI 系统中的融入，从而使 AI 系统更加稳健和公平。