AI Insight
This paper introduces a five-category typology for classifying AI systems used in public administration (hand-coded, glass-box, black-box, general-purpose, and agentic systems) to improve technical precision in research. An analysis of 91 highly-cited papers from 2019-2025 reveals widespread imprecision: 55% of papers inadequately specify which AI system type they study, 31% motivate their work with a different system than they actually examine, and 41% draw conclusions broader than their studied systems warrant. The authors provide practical recommendations and diagnostic questions to help researchers classify AI systems without requiring specialist technical knowledge.
Why it matters
Different types of AI systems have distinct implications for public values such as accountability, procedural justice, and non-discrimination. Greater technical precision in categorizing AI systems will improve the quality of public administration research and policy decisions regarding AI deployment in government services.
Understand the Science
arXiv:2606.31755v1 Announce Type: cross
Abstract: 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.
Source: A Technical Typology of AI Systems in Public Administration