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.

翻译：随着人工智能系统的广泛普及，对AI的信任已成为当前亟待探讨的重要议题。迄今为止，研究者多采用局限性的视角来审视这一关系。具体而言，现有研究仅考虑了有限的相关信任方（如终端用户）与被信任方（即AI系统），且实证探索多局限于实验室环境，可能忽视了现实世界中影响人机关系的诸多因素。本文主张通过考量AI系统所源自的复杂动态供应链，来拓宽"AI信任"研究的范畴。AI供应链涉及多样化的技术构件，不同个体、组织及利益相关方以多种方式与之交互。我们通过一项针对LLM供应链的现场实证研究，揭示了更多类型的信任方与被信任方，以及影响其信任关系的新因素。研究发现这些关系对LLM的开发与采用具有核心影响，但也可能成为未校准信任及依赖不可靠LLM的滋生场域。基于这些发现，我们探讨了其对"AI信任"研究的意义，着重指出了关于供应链跨主体关系恰当研究、校准信任建立及有效依赖行为发展的新研究机遇与挑战。同时，我们对构建LLM供应链信任的根本意义提出了反思。