Fairlearn: Assessing and Improving Fairness of AI Systems

Hilde Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz +1 more
3/29/2023
cs.LGcs.AIcs.CY

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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.

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Cite this paper

@article{weerts2023fairlearn,
  title  = {Fairlearn: Assessing and Improving Fairness of AI Systems},
  author = {Hilde Weerts and Miroslav Dudík and Richard Edgar and Adrin Jalali and Roman Lutz and Michael Madaio},
  year   = {2023},
  eprint = {2303.16626},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.16626v1}
}

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