Gender and sex bias in COVID-19 epidemiological data through the lens of causality.

Díaz-Rodríguez, Natalia; Binkyte, Ruta; Bakkali, Wafae; Bookseller, Sannidhi; Tubaro, Paola; Bacevicius, Andrius; Zhioua, Sami; Chatila, Raja

Díaz-Rodríguez, Natalia; Binkyte, Ruta; Bakkali, Wafae; Bookseller, Sannidhi; Tubaro, Paola; Bacevicius, Andrius; Zhioua, Sami; Chatila, Raja.

Afiliación

Díaz-Rodríguez N; DaSCI Andalusian Institute in Data Science and Computational Intelligence, CITIC, Dpt. of Computer Science and Artificial Intelligence, University of Granada, Spain.
Binkyte R; INRIA, École Polytechnique, IPP, Paris, France.
Bakkali W; Amazon Machine Learning Solutions Lab, Amazon Web Services, Paris, France.
Bookseller S; EPITA College, Le Kremlin-Bicêtre, France.
Tubaro P; LISN-TAU, CNRS, University Paris-Saclay, Inria, France.
Bacevicius A; OSE Immunotherapeutics, Paris, France.
Zhioua S; INRIA, École Polytechnique, IPP, Paris, France.
Chatila R; ISIR (Institute of Intelligent Systems and Robotics), Sorbonne University, Paris, France.

Inf Process Manag ; 60(3): 103276, 2023 May.

Article en En | MEDLINE | ID: mdl-36647369

Palabras clave

Artificial intelligence; COVID-19; Causal fairness; Causality; Equality; Explainability; Gender; Healthcare; Sex

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Etiology_studies / Observational_studies / Prognostic_studies Aspecto: Determinantes_sociais_saude Idioma: En Revista: Inf Process Manag Año: 2023 Tipo del documento: Article País de afiliación: España Pais de publicación: Reino Unido

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