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Why we should talk about institutional (dis)trustworthiness and medical machine learning

Michiel De Proost (UGent) and Giorgia Pozzi
(2025) MEDICINE HEALTH CARE AND PHILOSOPHY. 28. p.83-92
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Abstract
The principle of trust has been placed at the centre as an attitude for engaging with clinical machine learning systems. However, the notions of trust and distrust remain fiercely debated in the philosophical and ethical literature. In this article, we proceed on a structural level ex negativo as we aim to analyse the concept of institutional distrustworthiness to achieve a proper diagnosis of how we should not engage with medical machine learning. First, we begin with several examples that hint at the emergence of a climate of distrust in the context of medical machine learning. Second, we introduce the concept of institutional trustworthiness based on an expansion of Hawley's commitment account. Third, we argue that institutional opacity can undermine the trustworthiness of medical institutions and can lead to new forms of testimonial injustices. Finally, we focus on possible building blocks for repairing institutional distrustworthiness.
Keywords
Trust, Institutional distrustworthiness, Institutional opacity, Medical machine learning, Epistemic injustice, AI ethics, RACIAL BIAS, TRUST, HEALTH

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Citation

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MLA
De Proost, Michiel, and Giorgia Pozzi. “Why We Should Talk about Institutional (Dis)Trustworthiness and Medical Machine Learning.” MEDICINE HEALTH CARE AND PHILOSOPHY, vol. 28, 2025, pp. 83–92, doi:10.1007/s11019-024-10235-6.
APA
De Proost, M., & Pozzi, G. (2025). Why we should talk about institutional (dis)trustworthiness and medical machine learning. MEDICINE HEALTH CARE AND PHILOSOPHY, 28, 83–92. https://doi.org/10.1007/s11019-024-10235-6
Chicago author-date
De Proost, Michiel, and Giorgia Pozzi. 2025. “Why We Should Talk about Institutional (Dis)Trustworthiness and Medical Machine Learning.” MEDICINE HEALTH CARE AND PHILOSOPHY 28: 83–92. https://doi.org/10.1007/s11019-024-10235-6.
Chicago author-date (all authors)
De Proost, Michiel, and Giorgia Pozzi. 2025. “Why We Should Talk about Institutional (Dis)Trustworthiness and Medical Machine Learning.” MEDICINE HEALTH CARE AND PHILOSOPHY 28: 83–92. doi:10.1007/s11019-024-10235-6.
Vancouver
1.
De Proost M, Pozzi G. Why we should talk about institutional (dis)trustworthiness and medical machine learning. MEDICINE HEALTH CARE AND PHILOSOPHY. 2025;28:83–92.
IEEE
[1]
M. De Proost and G. Pozzi, “Why we should talk about institutional (dis)trustworthiness and medical machine learning,” MEDICINE HEALTH CARE AND PHILOSOPHY, vol. 28, pp. 83–92, 2025.
@article{01JDM2QV3NV5XM3YV28CGN6YMX,
  abstract     = {{The principle of trust has been placed at the centre as an attitude for engaging with clinical machine learning systems. However, the notions of trust and distrust remain fiercely debated in the philosophical and ethical literature. In this article, we proceed on a structural level ex negativo as we aim to analyse the concept of institutional distrustworthiness to achieve a proper diagnosis of how we should not engage with medical machine learning. First, we begin with several examples that hint at the emergence of a climate of distrust in the context of medical machine learning. Second, we introduce the concept of institutional trustworthiness based on an expansion of Hawley's commitment account. Third, we argue that institutional opacity can undermine the trustworthiness of medical institutions and can lead to new forms of testimonial injustices. Finally, we focus on possible building blocks for repairing institutional distrustworthiness.}},
  author       = {{De Proost, Michiel and Pozzi, Giorgia}},
  issn         = {{1386-7423}},
  journal      = {{MEDICINE HEALTH CARE AND PHILOSOPHY}},
  keywords     = {{Trust,Institutional distrustworthiness,Institutional opacity,Medical machine learning,Epistemic injustice,AI ethics,RACIAL BIAS,TRUST,HEALTH}},
  language     = {{eng}},
  pages        = {{83--92}},
  title        = {{Why we should talk about institutional (dis)trustworthiness and medical machine learning}},
  url          = {{http://doi.org/10.1007/s11019-024-10235-6}},
  volume       = {{28}},
  year         = {{2025}},
}
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