Aequitas Flow is an open-source framework for end-to-end Fair Machine Learning (ML) experimentation in Python. This package fills the existing integration gaps in other Fair ML packages of complete and accessible experimentation. It provides a pipeline for fairness-aware model training, hyperparameter optimization, and evaluation, enabling rapid and simple experiments and result analysis. 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 seeks to enhance the adoption of these concepts in AI technologies.

翻译：Aequitas Flow是一个基于Python的开源框架，用于端到端的公平机器学习（Fair ML）实验。该软件包填补了现有公平机器学习工具包在完整性和易用性实验方面的集成空白。它提供了一条涵盖公平感知模型训练、超参数优化与评估的流水线，支持快速、简便的实验与结果分析。该框架面向机器学习从业者与研究人员，提供了方法、数据集、指标的实现以及这些组件的标准接口，以提升可扩展性。通过促进公平机器学习实践的开发，Aequitas Flow旨在推动这些概念在人工智能技术中的采纳与应用。