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Fairlearn: Assessing and Improving Fairness of AI Systems

Fairlearn: Assessing and Improving Fairness of AI Systems

Authors :
Weerts, Hilde
Dudík, Miroslav
Edgar, Richard
Jalali, Adrin
Lutz, Roman
Madaio, Michael
Publication Year :
2023

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.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2303.16626
Document Type :
Working Paper