Testimonial injustice in medical machine learning
Open accessAbstract
Machine learning (ML) systems play an increasingly relevant role in medicine and healthcare. As their applications move ever closer to patient care and cure in clinical settings, ethical concerns about the responsibility of their use come to the fore. I analyse an aspect of responsible ML use that bears not only an ethical but also a significant epistemic dimension. I focus on ML systems’ role in mediating patient–physician relations. I thereby consider how ML systems may silence patients’ voices and relativise the credibility of their opinions, which undermines their overall credibility status without valid moral and epistemic justification. More specifically, I argue that withholding credibility due to how ML systems operate can be particularly harmful to patients and, apart from adverse outcomes, qualifies as a form of testimonial injustice. I make my case for testimonial injustice in medical ML by considering ML systems currently used in the USA to predict patients’ risk of misusing opioids (automated Prediction Drug Monitoring Programmes, PDMPs for short). I argue that the locus of testimonial injustice in ML-mediated medical encounters is found in the fact that these systems are treated as markers of trustworthiness on which patients’ credibility is assessed. I further show how ML-based PDMPs exacerbate and further propagate social inequalities at the expense of vulnerable social groups.
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Ferlito and De Proost (2023) offer ubuntu to mediate testimonial epistemic injustice in automated Prediction Drug Monitoring Programmes (PDMP) observed by Pozzi (2023) in healthcare.
“For instance, Ferlito and De Proost (2023) offer ubuntu to mediate testimonial epistemic injus-tice in automated Prediction Drug Monitoring Programmes (PDMP) observed by Pozzi (2023) in healthcare.”
From Generative Artificial Intelligence, Formative Epistemic Injustice, and Educators’ Solutions (2026) · cites this paper as Pozzi 2023
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Cited by
Sorted by citations
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Generative Artificial Intelligence, Formative Epistemic Injustice, and Educators’ Solutions
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- Authors
- Pozzi, G.
- Affiliations
- Delft University of Technology (NL)
- Corresponding author
- Pozzi, Giorgia
- ORCID
- Pozzi, Giorgia
- Venue
- Journal of Medical Ethics
- Publisher
- BMJ
- Published
- 2023-01-12
- Volume
- 49
- Issue
- 8
- Pages
- 536-540
- ISSN
- 0306-6800 (ISSN-L)
- 1473-4257
- OpenAlex
- W4315784405
- Cited by
- 62
- References
- 25
- Citations by year
- 2026: 10 · 2025: 24 · 2024: 14 · 2023: 13 · 2022: 1
- FWCI
- 15.30
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Medical Ethics (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Research Repository (Delft University of Technology) (submittedVersion)
- Funders
- Horizon 2020 Framework Programme (871042)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Sociology and Political Science › Feminist Epistemology and Gender Studies
- Keywords
- testimonial injustice, machine learning in medicine, physician-patient relationship, trustworthiness, social inequality, vulnerable populations, epistemic injustice
- MeSH terms
- Humans, Morals, Physician-Patient Relations
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