(Re)Conceptualizing trustworthy AI: A foundation for change

Wirz, C. D., Demuth, J. L., Bostrom, A., Cains, M., Ebert-Uphoff, I., et al. (2025). (Re)Conceptualizing trustworthy AI: A foundation for change. Artificial Intelligence, doi:https://doi.org/10.1016/j.artint.2025.104309

Title (Re)Conceptualizing trustworthy AI: A foundation for change
Genre Article
Author(s) Christopher D. Wirz, Julie L. Demuth, A. Bostrom, Mariana Cains, I. Ebert-Uphoff, David John Gagne, Andrea Schumacher, A. McGovern, D. Madlambayan
Abstract Developers and academics have grown increasingly interested in developing “trustworthy” artificial intelligence (AI). However, this aim is difficult to achieve in practice, especially given trust and trustworthiness are complex, multifaceted concepts that cannot be completely guaranteed nor built entirely into an AI system. We have drawn on the breadth of trust-related literature across multiple disciplines and fields to synthesize knowledge pertaining to interpersonal trust, trust in automation, and risk and trust. Based on this review we have (re)conceptualized trustworthiness in practice as being both (a) perceptual, meaning that a user assesses whether, when, and to what extent AI model output is trustworthy, even if it has been developed in adherence to AI trustworthiness standards, and (b) context-dependent, meaning that a user's perceived trustworthiness and use of an AI model can vary based on the specifics of their situation (e.g., time-pressures for decision-making, high-stakes decisions). We provide our reconceptualization to nuance how trustworthiness is thought about, studied, and evaluated by the AI community in ways that are more aligned with past theoretical research.
Publication Title Artificial Intelligence
Publication Date May 1, 2025
Publisher's Version of Record https://doi.org/10.1016/j.artint.2025.104309
OpenSky Citable URL https://n2t.net/ark:/85065/d7m3315j
OpenSky Listing View on OpenSky
CISL Affiliations MILES

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