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Autor:
David Munechika, Zijie J. Wang, Jack Reidy, Josh Rubin, Krishna Gade, Krishnaram Kenthapadi, Duen Horng Chau
As machine learning (ML) systems become increasingly widespread, it is necessary to audit these systems for biases prior to their deployment. Recent research has developed algorithms for effectively identifying intersectional bias in the form of inte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6d8fc7cf8c550df7f43bf5bfe752d3ab