Beyond Trustworthy AI: Unstable Trust, Mental Health AI, and Proof Infrastructures in Healthcare Governance

Céline Gauthier-Maxence () and Carine Milcent ()
Additional contact information
Céline Gauthier-Maxence: PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - ENPC - École nationale des ponts et chaussées - IP Paris - Institut Polytechnique de Paris
Carine Milcent: PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - ENPC - École nationale des ponts et chaussées - IP Paris - Institut Polytechnique de Paris, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - ENPC - École nationale des ponts et chaussées - IP Paris - Institut Polytechnique de Paris

PSE-Ecole d'économie de Paris (Postprint) from HAL

Abstract: Healthcare AI governance increasingly relies on the paradigm of "trustworthy AI," which assumes that explainability, transparency, and compliance mechanisms can stabilize trust in automated systems. This article challenges that assumption through a socio-legal analysis of healthcare AI governance based on findings from 28 interviews conducted with French health-data governance actors. The interviews revealed fluctuating perceptions of AI depending on institutional position, operational exposure, and responsibility allocation. Trust appeared conditional upon supervision, secure infrastructures, traceability, and accountability rather than solely upon technical explainability. These tensions become particularly visible in mental-health AI, where systems increasingly rely on multimodal behavioral and contextual data to infer psychiatric or psychological states under conditions of persistent uncertainty. The article argues that explainability alone struggles to stabilize trust in such environments because uncertainty concerns both the interpretation of AI systems and the meaning of the underlying data. It consequently proposes a shift from stable trust assumptions toward proof-oriented governance centered on traceability, auditability, contextual continuity, and distributed accountability. Within this perspective, blockchain-based architectures such as Galéon are examined as limited proof infrastructures supporting evidentiary continuity rather than as autonomous "trust machines." The paper concludes that healthcare AI governance should focus less on producing durable trust than on preserving the conditions under which uncertainty remains visible, verifiable, and governable.

Keywords: Trustworthy AI; Mental Health AI; Proof Infrastructures; Trustworthy AI Mental Health AI Proof Infrastructures (search for similar items in EconPapers)
Date: 2026-08
Note: View the original document on HAL open archive server: https://hal.science/hal-05720365v1
References: Add references at CitEc
Citations:

Published in Lecture Notes in Computer Science, 2026, Artificial Intelligence in Healthcare Third International Conference, AIiH 2026, London, UK, August 26–28, 2026, Proceedings, Part I, pp.30-46. ⟨10.1007/978-3-032-35387-0_3⟩

Downloads: (external link)
https://hal.science/hal-05720365v1/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:pseptp:hal-05720365

DOI: 10.1007/978-3-032-35387-0_3

Access Statistics for this paper

More papers in PSE-Ecole d'économie de Paris (Postprint) from HAL
Bibliographic data for series maintained by Caroline Bauer ().