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

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Titel: Bias in algorithms of AI systems developed for COVID-19 : a scoping review
Autoren: Delgado, Janet, de Manuel Lozano, Alicia, Parra Jounou, Iris, Moyano, Cristian, Rueda, Jon, Guersenzvaig, Ariel, Ausín, Txetxu, Cruz Piqueras, Maite, Casacuberta, David, Puyol González, Àngel
Publikationsjahr: 2022
Bestand: Universitat Autònoma de Barcelona: Dipòsit Digital de Documents de la UAB
Schlagwörter: Artificial intelligence, Bias, Digital contact tracing, COVID-19, Patient risk prediction
Beschreibung: Altres ajuts: acords transformatius de la UAB ; Altres ajuts: Fundación BBVA ; 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.
Publikationsart: article in journal/newspaper
Dateibeschreibung: application/pdf
Sprache: English
ISSN: 18724353
Relation: Journal of Bioethical Inquiry; Vol. 19 (july 2022), p. 407-419; https://ddd.uab.cat/record/265657; urn:oai:ddd.uab.cat:265657; urn:pmcid:PMC9463236; urn:pmid:35857214; urn:oai:pubmedcentral.nih.gov:9463236; urn:articleid:18724353v19p407; urn:scopus_id:85134596281
Verfügbarkeit: https://ddd.uab.cat/record/265657
Rights: open access ; Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. ; https://creativecommons.org/licenses/by/4.0/
Dokumentencode: edsbas.9FB4C13F
Datenbank: BASE
Beschreibung
Abstract:Altres ajuts: acords transformatius de la UAB ; Altres ajuts: Fundación BBVA ; 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.
ISSN:18724353