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Translating Intersectionality to Fair Machine Learning in Health Sciences.
Lett, Elle; La Cava, William G.
Affiliation
  • Lett E; Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, United States of America.
  • La Cava WG; Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Nat Mach Intell ; 5(5): 476-479, 2023 May.
Article in En | MEDLINE | ID: mdl-37600144
Fairness approaches in machine learning should involve more than assessment of performance metrics across groups. Shifting the focus away from model metrics, we reframe fairness through the lens of intersectionality, a Black feminist theoretical framework that contextualizes individuals in interacting systems of power and oppression.

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Nat Mach Intell Year: 2023 Document type: Article Affiliation country: United States Country of publication: United kingdom

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Nat Mach Intell Year: 2023 Document type: Article Affiliation country: United States Country of publication: United kingdom