Aequitas Flow: Streamlining Fair ML Experimentation

TL;DR

Aequitas Flow is an open-source Python toolkit that streamlines fair machine learning experimentation, offering an integrated pipeline for model training, hyperparameter tuning, and evaluation to promote fairness in AI systems.

Contribution

It introduces a comprehensive, extensible framework that fills integration gaps in fair ML tools, enabling rapid experimentation and benchmarking.

Findings

Provides an end-to-end pipeline for fair ML experiments

Enhances extensibility with standard interfaces and datasets

Facilitates rapid development of fair AI systems

Abstract

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.