Aequitas Flow: Streamlining Fair ML Experimentation
This framework addresses the need for streamlined fairness experimentation for ML practitioners and researchers, though it appears incremental as it builds on existing audit capabilities.
The authors tackled the problem of gaps in existing fair machine learning packages by introducing Aequitas Flow, an open-source Python framework for end-to-end fairness experimentation, which provides a pipeline for fairness-aware model training, hyperparameter optimization, and evaluation to enable rapid experiments.
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.