Recent advances in artificial intelligence (AI), especially in generative language modelling, hold the promise of transforming government. Given the advanced capabilities of new AI systems, 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 applications 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 depth 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 first conducting an integrative literature review to identify and cluster 69 key terms that frequently co-occur in the multidisciplinary study of AI. We then build on the results of this bibliometric analysis to propose three new multifaceted concepts for understanding and analysing AI-based systems for government (AI-GOV) in a more unified way: (1) operational fitness, (2) epistemic alignment, and (3) normative divergence. 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 rethink government with AI.

翻译：人工智能（AI）的最新进展，尤其是生成式语言模型领域的发展，为政府转型带来了巨大希望。鉴于新型AI系统的先进能力，必须通过标准化操作流程、清晰的认知标准将其嵌入，并使其行为符合社会的规范性期望。随后，多领域学者开始概念化AI应用可能呈现的不同形式，强调其潜在优势与陷阱。然而，相关文献仍呈碎片化状态——公共管理与政治学等社会科学领域的研究者，与AI、机器学习、机器人学等快速发展领域的研究者，几乎都在孤立地发展概念。尽管已有呼声要求将新兴的政府AI研究正式化，但缺乏能全面捕捉理解AI嵌入公共部门后果所需理论视角深度的平衡论述。为此，我们通过整合性文献综述，首先识别并聚类了多学科AI研究中频繁共现的69个关键术语，以此统一社会与技术学科的努力。继而基于文献计量分析结果，提出三个用于更统一地理解与分析政府AI系统的新多维概念：（1）操作适应性，（2）认知对齐，以及（3）规范性分歧。最终，我们将这些概念作为概念类型学的维度投入实践，将其与新兴的AI技术测量标准相联结，以促进可操作性、推动跨学科对话，并激发那些旨在用AI重构政府的研究者之间的辩论。