Aequitas Flow: Streamlining Fair ML Experimentation

arXiv:2405.05809v1 Announce Type: new
Abstract: 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. …

abstract arxiv cs.ai cs.cy cs.lg enabling evaluation experimentation fair fairness flow framework hyperparameter integration machine machine learning optimization package pipeline python simple training type