Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19
Open accessAbstract
The COVID-19 pandemic is presenting a disproportionate impact on minorities in terms of infection rate, hospitalizations, and mortality. Many believe artificial intelligence (AI) is a solution to guide clinical decision-making for this novel disease, resulting in the rapid dissemination of underdeveloped and potentially biased models, which may exacerbate the disparities gap. We believe there is an urgent need to enforce the systematic use of reporting standards and develop regulatory frameworks for a shared COVID-19 data source to address the challenges of bias in AI during this pandemic. There is hope that AI can help guide treatment decisions within this crisis; yet given the pervasiveness of biases, a failure to proactively develop comprehensive mitigation strategies during the COVID-19 pandemic risks exacerbating existing health disparities.
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AI biases in medical research have generated considerable concern, since biases related to race, ethnicity, gender, sexuality, age, nationality, and socioeconomic status in health-related datasets can perpetuate health disparities by supporting biased hypotheses, models, theories, and policies.
“AI biases in medical research have generated considerable concern, since biases related to race, ethnicity, gender, sexuality, age, nationality, and socioeconomic status in health-related datasets can perpetuate health disparities by supporting biased hypotheses, models, theories, and policies [177, 198, 211].”
From The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool (2025) · cites this paper as [198]
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- Authors
- Röösli, E., Rice, B., & Hernandez-Boussard, T.
- Affiliations
- Stanford Medicine (US)
- Department of Medicine (Biomedical Informatics), Stanford University, Stanford, California, USA
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-08-17
- Volume
- 28
- Issue
- 1
- Pages
- 190-192
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC7454645
- OpenAlex
- W3049243480
- Cited by
- 76
- References
- 8
- Citations by year
- 2026: 4 · 2025: 11 · 2024: 15 · 2023: 11 · 2022: 17 · 2021: 15 · 2020: 3
- FWCI
- 15.65
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- algorithmic bias, artificial intelligence, COVID-19 pandemic, health disparities, racial and ethnic minorities, clinical decision support, reporting guidelines, regulatory framework, bias mitigation, health equity
- MeSH terms
- Clinical Decision-Making, COVID-19, Artificial Intelligence, Humans, Minority Groups, United States, Bias, Information Storage and Retrieval, Resource Allocation, Health Status Disparities, Healthcare Disparities
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