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

Rachel K. E. Bellamy,Kuntal Dey,Michael Hind,Samuel C. Hoffman,Stephanie Houde,Kalapriya Kannan,Pranay Lohia,Jacquelyn Martino,Sameep Mehta,Aleksandra Mojsilovic,Seema Nagar,Karthikeyan Natesan Ramamurthy,John Richards,Diptikalyan Saha,Prasanna Sattigeri,Moninder Singh,Kush Raj Varshney,Yunfeng Zhang

IBM Journal of Research and Development（2019）

引用 1400|浏览215

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摘要

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 thi...

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关键词

Measurement,Prediction algorithms,Artificial intelligence,Machine learning algorithms,Pipelines,Open source software,Data models

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