Latent bias and the implementation of artificial intelligence in medicine
Open accessAbstract
Increasing recognition of biases in artificial intelligence (AI) algorithms has motivated the quest to build fair models, free of biases. However, building fair models may be only half the challenge. A seemingly fair model could involve, directly or indirectly, what we call “latent biases.” Just as latent errors are generally described as errors “waiting to happen” in complex systems, latent biases are biases waiting to happen. Here we describe 3 major challenges related to bias in AI algorithms and propose several ways of managing them. There is an urgent need to address latent biases before the widespread implementation of AI algorithms in clinical practice.
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Disparities related to benefit or harm are introduced or exacerbated for specific individuals or groups, eroding the capacity for healthcare to be delivered in a fair and equitable manner.
“Through this, disparities related to benefit or harm are introduced or exacerbated for specific individuals or groups, eroding the capacity for healthcare to be delivered in a fair and equitable manner11.”
From Bias recognition and mitigation strategies in artificial intelligence healthcare applications (2025) · cites this paper as [11]
Continuous learning possesses the risk of increasing or adding new bias if the new data are biased.
“However, continuous learning also possesses the risk of increasing or adding new bias if the new data are biased17.”
From Bias in AI-based models for medical applications: challenges and mitigation strategies (2023) · cites this paper as [17]
Continuous learning can increase bias if the new data are biased.
“Continuous learning can increase bias if the new data are biased.139”
From Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects (2024) · cites this paper as [139]
Using zip codes, which are strongly correlated with race, to predict housing prices for tax purposes raises concerns about redlining, which the Fair Housing Act of 1968 outlawed.
“Furthermore, using zip codes -which are strongly correlated with race [21,41] -to predict housing prices for tax purposes raise concerns about redlining, which the Fair Housing Act of 1968 explicitly outlawed as a practice in the United States [14].”
From It’s Just Not That Simple: An Empirical Study of the Accuracy-Explainability Trade-off in Machine Learning for Public Policy (2022) · cites this paper as [21]
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Self-citation
- Authors
- DeCamp, M., & Lindvall, C.
- Affiliations
- University of Colorado Anschutz (US)
- Brigham and Women's Hospital (US)
- Corresponding author
- Lindvall, Charlotta
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-06-18
- Volume
- 27
- Issue
- 12
- Pages
- 2020-2023
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC7727353
- OpenAlex
- W3037480398
- Cited by
- 198
- References
- 10
- Citations by year
- 2026: 20 · 2025: 33 · 2024: 30 · 2023: 68 · 2022: 19 · 2021: 25 · 2020: 3
- FWCI
- 39.56
- 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)
- Funders
- Cambia Health Foundation Sojourns Scholar
- 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 in healthcare, clinical implementation of AI, bias management, algorithmic errors
- MeSH terms
- Algorithms, Artificial Intelligence, Humans, Prejudice, Bias, Decision Support Systems, Clinical
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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