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Fairness is an increasingly important concern as machine learning models areused to support decision making in high-stakes applications such as mortgagelending, hiring, and prison sentencing. This paper introduces a new open sourcePython toolkit for algorithmic fairness, AI Fairness 360 (AIF360), releasedunder an Apache v2.0 license {https://github.com/ibm/aif360). The mainobjectives of this toolkit are to help facilitate the transition of fairnessresearch algorithms to use in an industrial setting and to provide a commonframework for fairness researchers to share and evaluate algorithms.  The package includes a comprehensive set of fairness metrics for datasets andmodels, explanations for these metrics, and algorithms to mitigate bias indatasets and models. It also includes an interactive Web experience(https://aif360.mybluemix.net) that provides a gentle introduction to theconcepts and capabilities for line-of-business users, as well as extensivedocumentation, usage guidance, and industry-specific tutorials to enable datascientists and practitioners to incorporate the most appropriate tool for theirproblem into their work products. The architecture of the package has beenengineered to conform to a standard paradigm used in data science, therebyfurther improving usability for practitioners. Such architectural design andabstractions enable researchers and developers to extend the toolkit with theirnew algorithms and improvements, and to use it for performance benchmarking. Abuilt-in testing infrastructure maintains code quality.

AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias