Program equivalence and context-free grammars
Barry K. Rosen
SWAT 1972
Fairness is an increasingly important concern as machine learning models are used to support decision making in high-stakes applications such as mortgage lending, hiring, and prison sentencing. This article introduces a new open-source Python toolkit for algorithmic fairness, AI Fairness 360 (AIF360), released under an Apache v2.0 license (https://github.com/ibm/aif360). The main objectives of this toolkit are to help facilitate the transition of fairness research algorithms for use in an industrial setting and to provide a common framework for fairness researchers to share and evaluate algorithms. The package includes a comprehensive set of fairness metrics for datasets and models, explanations for these metrics, and algorithms to mitigate bias in datasets and models. It also includes an interactive Web experience that provides a gentle introduction to the concepts and capabilities for line-of-business users, researchers, and developers to extend the toolkit with their new algorithms and improvements and to use it for performance benchmarking. A built-in testing infrastructure maintains code quality.
Barry K. Rosen
SWAT 1972
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
Bing Zhang, Mikio Takeuchi, et al.
NAACL 2025
P. Trespeuch, Y. Fournier, et al.
Civil-Comp Proceedings