Recent advances in artificial intelligence (AI) and machine learning (ML)
hold the promise of improving government. Given the advanced capabilities of AI
applications, it is critical that these are embedded using standard operational
procedures, clear epistemic criteria, and behave in alignment with the
normative expectations of society. Scholars in multiple domains have
subsequently begun to conceptualize the different forms that AI systems may
take, highlighting both their potential benefits and pitfalls. However, the
literature remains fragmented, with researchers in social science disciplines
like public administration and political science, and the fast-moving fields of
AI, ML, and robotics, all developing concepts in relative isolation. Although
there are calls to formalize the emerging study of AI in government, a balanced
account that captures the full breadth of theoretical perspectives needed to
understand the consequences of embedding AI into a public sector context is
lacking. Here, we unify efforts across social and technical disciplines by
using concept mapping to identify 107 different terms used in the
multidisciplinary study of AI. We inductively sort these into three distinct
semantic groups, which we label the (a) operational, (b) epistemic, and (c)
normative domains. We then build on the results of this mapping exercise by
proposing three new multifaceted concepts to study AI-based systems for
government (AI-GOV) in an integrated, forward-looking way, which we call (1)
operational fitness, (2) epistemic completeness, and (3) normative salience.
Finally, we put these concepts to work by using them as dimensions in a
conceptual typology of AI-GOV and connecting each with emerging AI technical
measurement standards to encourage operationalization, foster
cross-disciplinary dialogue, and stimulate debate among those aiming to reshape
public administration with AI.

本文在社会科学与技术学科之间使用概念映射方法建立了三种不同的语义域 (a) 操作域，(b) 认识域，(c) 规范域，并引入了研究 AI 系统的三个新概念：操作适应性、认识完整性和规范重要性来研究 AI 在政府中的应用，并将其作为政府 AI 概念分类法的维度之一，为推动跨学科对话和促进公共行政重塑提供帮助。