Why we should talk about institutional (dis)trustworthiness and medical machine learning
Michiel De Proost1, Giorgia Pozzi2
1Bioethics Institute Ghent, Department of Philosophy and Moral Sciences, Ghent University, Blandijnberg 2, Ghent, 9000, Belgium. michiel.deproost@ugent.be.
Medicine, Health Care, and Philosophy
|November 13, 2024
Summary
Understanding institutional distrustworthiness is key to navigating ethical engagement with medical machine learning. This analysis explores how to avoid distrust by examining institutional opacity and its impact on trust in healthcare AI.
Area of Science:
- Medical Artificial Intelligence Ethics
- Clinical Machine Learning Trust
- Philosophy of Technology
Background:
- Trust is central to engaging with clinical machine learning (ML) systems.
- The concepts of trust and distrust in ML are subjects of ongoing philosophical and ethical debate.
- A climate of distrust is emerging in the context of medical ML applications.
Purpose of the Study:
- To analyze the concept of "institutional distrustworthiness" in medical ML.
- To provide a diagnosis of how not to engage with medical ML systems.
- To identify building blocks for repairing institutional distrustworthiness.
Main Methods:
- Structural analysis "ex negativo" focusing on distrustworthiness.
- Examination of institutional opacity and its role in undermining trust.
- Expansion of Hawley's commitment account to define institutional trustworthiness.
Main Results:
- Institutional opacity can erode the trustworthiness of medical institutions.
- Opacity in medical ML can lead to novel forms of testimonial injustice.
- Understanding how not to engage is crucial for building trust in healthcare AI.
Conclusions:
- Addressing institutional distrustworthiness is essential for ethical medical ML deployment.
- Strategies to combat opacity and testimonial injustice are needed.
- Repairing distrust requires a focus on building robust institutional trustworthiness.
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