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

Journal Article (2024)
Author(s)

Michiel De Proost (Universiteit Gent)

Giorgia Pozzi (TU Delft - Ethics & Philosophy of Technology)

Research Group
Ethics & Philosophy of Technology
DOI related publication
https://doi.org/10.1007/s11019-024-10235-6
More Info
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Publication Year
2024
Language
English
Research Group
Ethics & Philosophy of Technology
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Issue number
1
Volume number
28
Pages (from-to)
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.

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