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 connect 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：如今，呼吁制定新的指标、技术标准及治理机制以指导人工智能在机构和公共管理中的采纳已十分普遍。然而，大多数旨在理解人工智能采纳影响的研究和政策努力往往只优先考虑少数观点；它们未能完全连接所有潜在相关的不同视角和主题。在这篇立场论文中，我们认为这种遗漏部分源于我们称之为社会技术话语中的关系问题：基本的本体论问题尚未解决——包括语义模糊性、概念之间缺乏清晰关系以及不同的标准术语。这导致了评估机构人工智能系统时不同推理模式的持续存在，以及研究这些系统的领域（包括机器学习、人因工程、社会科学和政策）中概念孤立的普遍现象。在提出这一批评之后，我们通过提出一个简单的政策和研究设计工具——即一个概念框架来组织跨领域的术语——来提供前进方向。该框架包含三个横向领域，用于分组相关概念和相关方法：操作领域、认知领域和规范领域。我们首先在两个主要学术场所（AIES和FAccT）最近的社会技术话语背景下定位该框架，然后说明如何通过在每个领域中操作化相关概念来帮助制定合适的指标、标准和机制。最后，我们概述了发展这种机构人工智能研究与采纳的关系方法尚未解决的问题。