An Empirical Exploration of Trust Dynamics in LLM Supply Chains

TL;DR

This paper empirically investigates trust dynamics across the entire supply chain of large language models, revealing diverse trust actors and factors influencing trust, with implications for developing calibrated trust and reliable AI deployment.

Contribution

It broadens the scope of trust studies in AI by empirically exploring trust relationships in real-world LLM supply chains, beyond laboratory settings.

Findings

Identified new types of trustors and trustees in LLM supply chains.

Revealed factors impacting trust relationships among diverse stakeholders.

Highlighted risks of uncalibrated trust and reliance on untrustworthy LLMs.

Abstract

With the widespread proliferation of AI systems, trust in AI is an important and timely topic to navigate. Researchers so far have largely employed a myopic view of this relationship. In particular, a limited number of relevant trustors (e.g., end-users) and trustees (i.e., AI systems) have been considered, and empirical explorations have remained in laboratory settings, potentially overlooking factors that impact human-AI relationships in the real world. In this paper, we argue for broadening the scope of studies addressing `trust in AI' by accounting for the complex and dynamic supply chains that AI systems result from. AI supply chains entail various technical artifacts that diverse individuals, organizations, and stakeholders interact with, in a variety of ways. We present insights from an in-situ, empirical study of LLM supply chains. Our work reveals additional types of trustors and trustees and new factors impacting their trust relationships. These relationships were found to be central to the development and adoption of LLMs, but they can also be the terrain for uncalibrated trust and reliance on untrustworthy LLMs. Based on these findings, we discuss the implications for research on `trust in AI'. We highlight new research opportunities and challenges concerning the appropriate study of inter-actor relationships across the supply chain and the development of calibrated trust and meaningful reliance behaviors. We also question the meaning of building trust in the LLM supply chain.