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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Authors: Delgado Rodríguez, Janet, Manuel, Alicia de, Parra, Iris, Moyano Fernández, Cristian, Rueda, Jon, Gueresenzvaig, Ariel, Ausín, Txetxu, Cruz-Piqueras, Maite, Casacuberta, David, Puyol, Àngel
Format: article
Status:Published version
Publication Date:2022
Country:España
Institution:Consejo Superior de Investigaciones Científicas (CSIC)
Repository:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/280569
Online Access:http://hdl.handle.net/10261/280569
Access Level:Open access
Keyword: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 Rodríguez, JanetManuel, Alicia deParra, IrisMoyano Fernández, CristianRueda, JonGueresenzvaig, ArielAusín, TxetxuCruz-Piqueras, MaiteCasacuberta, DavidPuyol, ÀngelArtificial 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, socio-economic 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 bias-related health inequalities have been highlighted. There is a need for further research focusing on SDOH and these specific AI apps.Open Access Funding provided by Universitat Autonoma de Barcelona. This work has been funded by the BBVA Foundation for SARS-CoV-2 and COVID-19 Research in Humanities (Detección y eliminación de sesgos en algoritmos de triaje y localización para la COVID-19).Peer reviewedSpringer NatureUniversidad Autónoma de BarcelonaFundación BBVAConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202220222022info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/280569reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://doi.org/10.1007/s11673-022-10200-zSíinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2805692026-05-22T06:33:51Z
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 Rodríguez, 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 Rodríguez, Janet
Manuel, Alicia de
Parra, Iris
Moyano Fernández, Cristian
Rueda, Jon
Gueresenzvaig, Ariel
Ausín, Txetxu
Cruz-Piqueras, Maite
Casacuberta, David
Puyol, Àngel
author Delgado Rodríguez, Janet
author_facet Delgado Rodríguez, Janet
Manuel, Alicia de
Parra, Iris
Moyano Fernández, Cristian
Rueda, Jon
Gueresenzvaig, Ariel
Ausín, Txetxu
Cruz-Piqueras, Maite
Casacuberta, David
Puyol, Àngel
author_role author
author2 Manuel, Alicia de
Parra, Iris
Moyano Fernández, Cristian
Rueda, Jon
Gueresenzvaig, Ariel
Ausín, Txetxu
Cruz-Piqueras, Maite
Casacuberta, David
Puyol, Àngel
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidad Autónoma de Barcelona
Fundación BBVA
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
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, socio-economic 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 bias-related 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
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/280569
url http://hdl.handle.net/10261/280569
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.1007/s11673-022-10200-z
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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