FairLib: A Unified Framework for Assessing and Improving Fairness

Abstract

This paper presents FairLib, an open-source Python library for assessing and improving model fairness. It provides a systematic framework for quickly accessing benchmark datasets, reproducing existing debiasing baseline models, developing new methods, evaluating models with different metrics, and visualizing their results. Its modularity and extensibility enable the framework to be used for diverse types of inputs, including natural language, images, and audio. It incorporates 14 debiasing methods, including pre-processing, at-training-time, and post-processing approaches. The built-in metrics cover the most commonly acknowledged fairness criteria, and can be further generalized and customiz..