ML Anthology

Fairlearn: Assessing and Improving Fairness of AI Systems

Hilde Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz, Michael Madaio
MLOSS 2023 pp. 1-8
/mloss/2023/weerts2023jmlr-fairlearn/

Abstract

Fairlearn is an open source project to help practitioners assess and improve fairness of artificial intelligence (AI) systems. The associated Python library, also named fairlearn, supports evaluation of a model's output across affected populations and includes several algorithms for mitigating fairness issues. Grounded in the understanding that fairness is a sociotechnical challenge, the project integrates learning resources that aid practitioners in considering a system's broader societal context.

PDF MLOSS Code Semantic Scholar

Cite

Text

Weerts et al. "Fairlearn: Assessing and Improving Fairness of AI Systems." Machine Learning Open Source Software, 2023.

Markdown

[Weerts et al. "Fairlearn: Assessing and Improving Fairness of AI Systems." Machine Learning Open Source Software, 2023.](https://mlanthology.org/mloss/2023/weerts2023jmlr-fairlearn/)

BibTeX

@article{weerts2023jmlr-fairlearn,
  title     = {{Fairlearn: Assessing and Improving Fairness of AI Systems}},
  author    = {Weerts, Hilde and Dudík, Miroslav and Edgar, Richard and Jalali, Adrin and Lutz, Roman and Madaio, Michael},
  journal   = {Machine Learning Open Source Software},
  year      = {2023},
  pages     = {1-8},
  volume    = {24},
  url       = {https://mlanthology.org/mloss/2023/weerts2023jmlr-fairlearn/}
}