  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.
