Calls for new metrics, technical standards and governance mechanisms to guide the adoption of Artificial Intelligence (AI) in institutions and public administration are now commonplace. Yet, most research and policy efforts aimed at understanding the implications of adopting AI tend to prioritize only a handful of ideas; they do not fully account for all the different perspectives and topics that are potentially relevant. In this position paper, we contend that this omission stems, in part, from what we call the relational problem in socio-technical discourse: fundamental ontological issues have not yet been settled-including semantic ambiguity, a lack of clear relations between concepts and differing standard terminologies. This contributes to the persistence of disparate modes of reasoning to assess institutional AI systems, and the prevalence of conceptual isolation in the fields that study them including ML, human factors, social science and policy. After developing this critique, we offer a way forward by proposing a simple policy and research design tool in the form of a conceptual framework to organize terms across fields-consisting of three horizontal domains for grouping relevant concepts and related methods: Operational, epistemic, and normative. We first situate this framework against the backdrop of recent socio-technical discourse at two premier academic venues, AIES and FAccT, before illustrating how developing suitable metrics, standards, and mechanisms can be aided by operationalizing relevant concepts in each of these domains. Finally, we outline outstanding questions for developing this relational approach to institutional AI research and adoption.

翻译：关于在机构与公共管理中采用人工智能（AI）的新指标、技术标准及治理机制的呼声如今已屡见不鲜。然而，大多数旨在理解AI应用影响的研究与政策工作往往只优先关注少数观点，未能全面考量所有潜在相关的不同视角与主题。在本立场论文中，我们认为这种遗漏部分源于我们所谓的"社会技术话语中的关系问题"：基本本体论问题尚未得到解决——包括语义歧义、概念间缺乏明确关系以及标准术语的差异。这导致评估机构人工智能系统时仍存在多种不连贯的推理模式，并在研究这些系统的机器学习、人因工程、社会科学与政策等领域中普遍存在概念孤立现象。在提出上述批评后，我们通过构建一个概念框架形式的简易政策与设计研究工具来指明前进方向，该框架包含三个横向领域以组织跨领域术语及关联概念与方法：操作域、认知域与规范域。我们首先将这一框架置于AIES与FAccT两个顶级学术场所近期社会技术话语的背景下进行定位，随后阐述如何通过在各领域中对相关概念进行操作化来促进适当指标、标准与机制的开发。最后，我们概述了发展这种面向机构人工智能研究与应用的"关系方法"所需解决的突出问题。