Towards a pragmatist dealing with algorithmic bias in medical machine learning
Open accessAbstract
Machine Learning (ML) is on the rise in medicine, promising improved diagnostic, therapeutic and prognostic clinical tools. While these technological innovations are bound to transform health care, they also bring new ethical concerns to the forefront. One particularly elusive challenge regards discriminatory algorithmic judgements based on biases inherent in the training data. A common line of reasoning distinguishes between justified differential treatments that mirror true disparities between socially salient groups, and unjustified biases which do not, leading to misdiagnosis and erroneous treatment. In the curation of training data this strategy runs into severe problems though, since distinguishing between the two can be next to impossible. We thus plead for a pragmatist dealing with algorithmic bias in healthcare environments. By recurring to a recent reformulation of William James’s pragmatist understanding of truth, we recommend that, instead of aiming at a supposedly objective truth, outcome-based therapeutic usefulness should serve as the guiding principle for assessing ML applications in medicine.
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Algorithmic bias can amplify and exacerbate existing social, cultural, or historical biases, leading to unfair treatment or discrimination against certain individuals or groups.
“It can amplify and exacerbate existing social, cultural, or historical biases, leading to unfair treatment or discrimination against certain individuals or groups [50-52].”
From Ethical Considerations of Using ChatGPT in Health Care (2023) · cites this paper as [51]
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- Authors
- Starke, G., De Clercq, E., & Elger, B. S.
- Affiliations
- University of Basel (CH)
- University of Geneva (CH)
- Corresponding author
- Starke, Georg
- Venue
- Medicine, Health Care and Philosophy
- Published
- 2021-03-13
- Volume
- 24
- Issue
- 3
- Pages
- 341-349
- ISSN
- 1386-7423 (ISSN-L)
- 1572-8633
- PMC
- PMC7955212
- OpenAlex
- W3133622253
- Cited by
- 54
- References
- 63
- Citations by year
- 2026: 4 · 2025: 11 · 2024: 17 · 2023: 13 · 2022: 7 · 2021: 2
- FWCI
- 12.19
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Medicine Health Care and Philosophy (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- edoc (University of Basel) (submittedVersion)
- Funders
- Universität Basel
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics and bioethics in healthcare
- Keywords
- algorithmic bias, machine learning in medicine, pragmatism, William James, differential treatment, training data, health disparities, misdiagnosis
- MeSH terms
- Machine Learning, Delivery of Health Care, Education, Medical, Humans, Morals, Bias
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