Bias in algorithms of AI systems developed for COVID-19: A scoping review

To analyze which ethically relevant biases have been identified by academic literature in artificial intelligence (AI) algorithms developed either for patient risk prediction and triage, or for contact tracing to deal with the COVID-19 pandemic. Additionally, to specifically investigate whether the...

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Autores: Delgado, Janet, de Manuel, Alicia, Parra, Iris, Moyano, Cristian, Rueda, Jon, Guersenzvaig, Ariel, Ausin, Txetxu, Cruz, Maite, Casacuberta, David, Puyol, Angel
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:UVic-UCC
Repositorio:RiUVic. Repositori institucional de la UVic-UCC
OAI Identifier:oai:dspace.uvic.cat:10854/180262
Acceso en línea:http://hdl.handle.net/10854/180262
https://doi.org/10.1007/s11673-022-10200-z
Access Level:acceso abierto
Palabra clave:Artificial intelligence
Bias
Digital contact tracing
COVID-19
Patient risk prediction
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spelling Bias in algorithms of AI systems developed for COVID-19: A scoping reviewDelgado, Janetde Manuel, AliciaParra, IrisMoyano, CristianRueda, JonGuersenzvaig, ArielAusin, TxetxuCruz, MaiteCasacuberta, DavidPuyol, AngelArtificial intelligenceBiasDigital contact tracingCOVID-19Patient risk predictionTo analyze which ethically relevant biases have been identified by academic literature in artificial intelligence (AI) algorithms developed either for patient risk prediction and triage, or for contact tracing to deal with the COVID-19 pandemic. Additionally, to specifically investigate whether the role of social determinants of health (SDOH) have been considered in these AI developments or not. We conducted a scoping review of the literature, which covered publications from March 2020 to April 2021. Studies mentioning biases on AI algorithms developed for contact tracing and medical triage or risk prediction regarding COVID-19 were included. From 1054 identified articles, 20 studies were finally included. We propose a typology of biases identified in the literature based on bias, limitations and other ethical issues in both areas of analysis. Results on health disparities and SDOH were classified into five categories: racial disparities, biased data, socioeconomic disparities, unequal accessibility and workforce, and information communication. SDOH needs to be considered in the clinical context, where they still seem underestimated. Epidemiological conditions depend on geographic location, so the use of local data in studies to develop international solutions may increase some biases. Gender bias was not specifically addressed in the articles included. The main biases are related to data collection and management. Ethical problems related to privacy, consent, and lack of regulation have been identified in contact tracing while some biasrelated health inequalities have been highlighted. There is a need for further research focusing on SDOH and these specific AI apps.info:eu-repo/semantics/publishedVersionSpringerUniversitat de Vic - Universitat Central de Catalunya. Elisava, Facultat de Disseny i Enginyeria de Barcelona202520252022info:eu-repo/semantics/article13 p.application/pdfhttp://hdl.handle.net/10854/180262https://doi.org/10.1007/s11673-022-10200-zreponame:RiUVic. Repositori institucional de la UVic-UCCinstname:UVic-UCCInglésJournal of bioethical inquiry, 19, 407-419info:eu-repo/semantics/openAccessoai:dspace.uvic.cat:10854/1802622026-06-07T19:15:21Z
dc.title.none.fl_str_mv Bias in algorithms of AI systems developed for COVID-19: A scoping review
title Bias in algorithms of AI systems developed for COVID-19: A scoping review
spellingShingle Bias in algorithms of AI systems developed for COVID-19: A scoping review
Delgado, Janet
Artificial intelligence
Bias
Digital contact tracing
COVID-19
Patient risk prediction
title_short Bias in algorithms of AI systems developed for COVID-19: A scoping review
title_full Bias in algorithms of AI systems developed for COVID-19: A scoping review
title_fullStr Bias in algorithms of AI systems developed for COVID-19: A scoping review
title_full_unstemmed Bias in algorithms of AI systems developed for COVID-19: A scoping review
title_sort Bias in algorithms of AI systems developed for COVID-19: A scoping review
dc.creator.none.fl_str_mv Delgado, Janet
de Manuel, Alicia
Parra, Iris
Moyano, Cristian
Rueda, Jon
Guersenzvaig, Ariel
Ausin, Txetxu
Cruz, Maite
Casacuberta, David
Puyol, Angel
author Delgado, Janet
author_facet Delgado, Janet
de Manuel, Alicia
Parra, Iris
Moyano, Cristian
Rueda, Jon
Guersenzvaig, Ariel
Ausin, Txetxu
Cruz, Maite
Casacuberta, David
Puyol, Angel
author_role author
author2 de Manuel, Alicia
Parra, Iris
Moyano, Cristian
Rueda, Jon
Guersenzvaig, Ariel
Ausin, Txetxu
Cruz, Maite
Casacuberta, David
Puyol, Angel
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universitat de Vic - Universitat Central de Catalunya. Elisava, Facultat de Disseny i Enginyeria de Barcelona
dc.subject.none.fl_str_mv Artificial intelligence
Bias
Digital contact tracing
COVID-19
Patient risk prediction
topic Artificial intelligence
Bias
Digital contact tracing
COVID-19
Patient risk prediction
description To analyze which ethically relevant biases have been identified by academic literature in artificial intelligence (AI) algorithms developed either for patient risk prediction and triage, or for contact tracing to deal with the COVID-19 pandemic. Additionally, to specifically investigate whether the role of social determinants of health (SDOH) have been considered in these AI developments or not. We conducted a scoping review of the literature, which covered publications from March 2020 to April 2021. Studies mentioning biases on AI algorithms developed for contact tracing and medical triage or risk prediction regarding COVID-19 were included. From 1054 identified articles, 20 studies were finally included. We propose a typology of biases identified in the literature based on bias, limitations and other ethical issues in both areas of analysis. Results on health disparities and SDOH were classified into five categories: racial disparities, biased data, socioeconomic disparities, unequal accessibility and workforce, and information communication. SDOH needs to be considered in the clinical context, where they still seem underestimated. Epidemiological conditions depend on geographic location, so the use of local data in studies to develop international solutions may increase some biases. Gender bias was not specifically addressed in the articles included. The main biases are related to data collection and management. Ethical problems related to privacy, consent, and lack of regulation have been identified in contact tracing while some biasrelated health inequalities have been highlighted. There is a need for further research focusing on SDOH and these specific AI apps.
publishDate 2022
dc.date.none.fl_str_mv 2022
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10854/180262
https://doi.org/10.1007/s11673-022-10200-z
url http://hdl.handle.net/10854/180262
https://doi.org/10.1007/s11673-022-10200-z
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Journal of bioethical inquiry, 19, 407-419
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 13 p.
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:RiUVic. Repositori institucional de la UVic-UCC
instname:UVic-UCC
instname_str UVic-UCC
reponame_str RiUVic. Repositori institucional de la UVic-UCC
collection RiUVic. Repositori institucional de la UVic-UCC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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