AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and
  Mitigating Unwanted Algorithmic Bias
  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 paper 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 to 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
(https://aif360.mybluemix.net) that provides a gentle introduction to the
concepts and capabilities for line-of-business users, as well as extensive
documentation, usage guidance, and industry-specific tutorials to enable data
scientists and practitioners to incorporate the most appropriate tool for their
problem into their work products. The architecture of the package has been
engineered to conform to a standard paradigm used in data science, thereby
further improving usability for practitioners. Such architectural design and
abstractions enable 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.
