Rethinking trust in responsible AI
Citation Link: https://doi.org/10.15480/882.17200
Publikationstyp
Conference Paper
Date Issued
2025
Sprache
English
TORE-DOI
Journal
Volume
4132
Start Page
112
End Page
119
Citation
28th European Workshop on Trustworthy AI, TRUST-AI 2025 & European Conference of Artificial Intelligence, ECAI 2025
Scopus ID
Publisher
CEUR-WS
Trust is widely recognized as a core principle of Responsible AI, yet its interpretation varies significantly across disciplines. This paper examines how computer science, sociology, philosophy, and law conceptualize trust in AI systems, highlighting both tensions and complementarities. From a computer science perspective, trust is often approached as a set of system-level properties that should be formalized and evaluated with metrics. In contrast, the social sciences and humanities emphasize its relational, normative, and institutional dimensions. We argue that trust cannot be reduced to a single system property or technical measure, as it emerges from social-technical interactions involving users, developers, legal norms, and social expectations. To support interdisciplinary dialogue, we propose treating trust as a boundary concept that enables cooperation across epistemic communities acknowledging conceptual differences. Trust is widely recognized as a core principle of Responsible AI, yet its interpretation varies significantly across disciplines. This paper examines how computer science, sociology, philosophy, and law conceptualize trust in AI systems, highlighting both tensions and complementarities. From a computer science perspective, trust is often approached as a set of system-level properties that should be formalized and evaluated with metrics. In contrast, the social sciences and humanities emphasize its relational, normative, and institutional dimensions. We argue that trust cannot be reduced to a single system property or technical measure, as it emerges from social-technical interactions involving users, developers, legal norms, and social expectations. To support interdisciplinary dialogue, we propose treating trust as a boundary concept that enables cooperation across epistemic communities acknowledging conceptual differences.
Subjects
AI governance
boundary concept
interdisciplinarity
Responsible AI
trust
trustworthy AI
DDC Class
005: Computer Programming, Programs, Data and Security
Publication version
publishedVersion