Bias in algorithms of AI systems developed for COVID-19 : a scoping review
| Autor: | Janet Delgado, Alicia de Manuel, Iris Parra, Cristian Moyano, Jon Rueda, Ariel Gueresenzvaig, Txetxu Ausín, Maite Cruz, David Casacuberta, Angel Puyol |
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| Přispěvatelé: | Delgado Rodríguez, Janet, Universidad Autónoma de Barcelona, Fundación BBVA, Delgado Rodríguez, Janet [0000-0002-3681-8571] |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Dipòsit Digital de Documents de la UAB Universitat Autònoma de Barcelona Digital.CSIC. Repositorio Institucional del CSIC instname |
| Popis: | 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. 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). |
| Databáze: | OpenAIRE |
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