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Calls for new metrics, technical standards and governance mechanisms to guidethe adoption of Artificial Intelligence (AI) in institutions and publicadministration are now commonplace. Yet, most research and policy efforts aimedat understanding the implications of adopting AI tend to prioritize only ahandful of ideas; they do not fully connect all the different perspectives andtopics that are potentially relevant. In this position paper, we contend thatthis omission stems, in part, from what we call the relational problem insocio-technical discourse: fundamental ontological issues have not yet beensettled--including semantic ambiguity, a lack of clear relations betweenconcepts and differing standard terminologies. This contributes to thepersistence of disparate modes of reasoning to assess institutional AI systems,and the prevalence of conceptual isolation in the fields that study themincluding ML, human factors, social science and policy. After developing thiscritique, we offer a way forward by proposing a simple policy and researchdesign tool in the form of a conceptual framework to organize terms acrossfields--consisting of three horizontal domains for grouping relevant conceptsand related methods: Operational, Epistemic, and Normative. We first situatethis framework against the backdrop of recent socio-technical discourse at twopremier academic venues, AIES and FAccT, before illustrating how developingsuitable metrics, standards, and mechanisms can be aided by operationalizingrelevant concepts in each of these domains. Finally, we outline outstandingquestions for developing this relational approach to institutional AI researchand adoption.

A multidomain relational framework to guide institutional AI research and adoption