A Technical Typology of AI Systems in Public Administration

2026-06-30

AI Summary

Public administration research often treats artificial intelligence (AI) as a monolithic technology, overlooking the heterogeneous impacts of its distinct technical forms on core public values such as accountability, procedural justice, and non-discrimination. Addressing this gap, this study proposes the first typology of AI systems—comprising hand-coded, transparent-box, black-box, general-purpose, and agent-based systems—specifically designed to assess differential effects on public values. Through a systematic review of 91 highly cited studies and a diagnostic question framework, the analysis reveals that 55% of existing works fail to specify the AI system type, 31% exhibit a mismatch between research motivation and system under study, and 41% overgeneralize their findings. The paper concludes with practical guidelines enabling researchers to precisely identify system types and enhance the clarity and applicability of their conclusions—without requiring specialized AI expertise.

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.

Problem

• artificial intelligence
• public administration
• technical typology
• public values
• AI systems

Innovation

• AI typology
• public administration
• technical precision
• algorithmic accountability
• AI classification