给公共部门的AI系统分类，让研究更精准。
A Technical Typology of AI Systems in Public Administration
- 提出五类AI系统：手工编码、透明、黑箱、通用和自主型。
- 分析91篇论文发现超半数未说明技术细节，结论常超出实际研究范围。
- 提供无需专业知识的问答指南，帮助研究者明确定位所用AI类型。
公共管理领域对人工智能的研究常将AI视为单一类别，忽视其技术差异，而这些差异直接影响问责、程序正义和非歧视等核心公共价值。本文主张公共管理研究需提升对AI的技术精确性，并作出三项贡献：首先，提出五类AI系统分类——手写代码、透明模型、黑箱模型、通用模型与自主型系统，并根据其对公共价值的影响进行归类；其次，基于该分类对2019至2025年91篇高被引论文进行编码分析，发现普遍存在技术描述不清问题：55%论文未明确研究系统类型，31%研究动机与实际研究系统不符，41%得出的结论超出所研究系统的支持范围；最后，给出实践建议，强调研究者至少应提供足够信息以定位到本分类体系中，并附上一套无需专业背景即可回答的诊断问题清单。
原文摘要 · Abstract (English)
Research on artificial intelligence (AI) in the public sector often treats "AI" as a single category, neglecting technical distinctions between different AI systems. But these distinctions affect how different systems impact core public values like accountability, procedural justice, and non-discrimination. This paper argues that public administration research would benefit from more technical precision on "AI" and makes three contributions to this end. First, we introduce a typology of five categories of AI systems: hand-coded, glass-box, black-box, general-purpose, and agentic systems. We calibrate the typology to public administration by grouping system types by their distinct implications for public values. Second, we evaluate technical precision in recent public administration research about AI by coding 91 highly-cited papers (2019-2025) using our typology. We find widespread imprecision: most papers (55\%) leave the studied system underspecified, 31\% motivate their work with a different system than they study, and 41\% make more general conclusions than the studied system supports. Finally, we give practical recommendations for future research. We highlight common pitfalls to avoid, and suggest that researchers should, at a minimum, provide enough technical detail to locate the studied system in our typology. To this end, we provide a practical guide -- a short set of diagnostic questions answerable from public information and without specialist technical knowledge.
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