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.

该研究探讨了社会技术话语中的关系问题，即语义模糊、概念之间缺乏明确关系和不同的标准术语这些根本本体论问题。作者提出了一个概念框架来组织概念，包括操作性，认识论和规范方面，以帮助开发适当的指标、标准和机制，促进机构 AI 系统的研究和采用。