Unpacking Trust Dynamics in the LLM Supply Chain: An Empirical Exploration to Foster Trustworthy LLM Production & Use

要旨

Research on trust in AI is limited to several trustors (e.g., end-users) and trustees (especially AI systems), and empirical explorations remain in laboratory settings, overlooking factors that impact trust relations in the real world. Here, we broaden the scope of research by accounting for the supply chains that AI systems are part of. To this end, we present insights from an in-situ, empirical, study of LLM supply chains. We conducted interviews with 71 practitioners, and analyzed their (collaborative) practices using the lens of trust drawing from literature in organizational psychology. Our work reveals complex trust dynamics at the junctions of the chains, with interactions between diverse technical artifacts, individuals, or organizations. These junctions might constitute terrain for uncalibrated reliance when trustors lack supply chain knowledge or power dynamics are at play. Our findings bear implications for AI researchers and policymakers to promote AI governance that fosters calibrated trust.

受賞
Honorable Mention
著者
Agathe Balayn
Delft University of Technology, Delft, Netherlands
Mireia Yurrita
Delft University of Technology, Delft, Netherlands
Fanny Rancourt
ServiceNow, Montreal, Quebec, Canada
Fabio Casati
University of Trento, Trento, Italy
Ujwal Gadiraju
Delft University of Technology, Delft, Netherlands
DOI

10.1145/3706598.3713787

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713787

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Trust and Responsibility in AI

G302
7 件の発表
2025-04-30 20:10:00
2025-04-30 21:40:00
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