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Source note3 min read

A Technical Typology of AI Systems in Public Administration

Source

arXiv

arxiv.org/abs/2606.31755v1

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Plain-Language Brief

The paper argues that different types of AI systems impact public values like accountability, procedural justice, and non-discrimination differently. The authors propose a typology of five AI system categories: hand-coded, glass-box, black-box, general-purpose, and agentic systems. They find that many recent studies on AI in public administration are imprecise about the specific AI systems they study, and offer recommendations for future research.

What the Document Says

  • The paper discusses a technical typology of five categories of AI systems: hand-coded, glass-box, black-box, general-purpose, and agentic systems.
  • The authors apply this typology to public administration and explain how each system type affects public values such as accountability, procedural justice, and non-discrimination.
  • They evaluate the technical precision in recent public administration research on AI by coding 91 highly-cited papers using their typology and find imprecision in most studies.
  • The authors recommend practical steps for future research, including avoiding common pitfalls and providing enough technical detail to locate the studied system in their proposed typology.

Key Concepts Mentioned

  • artificial intelligence
  • AI systems
  • public administration
  • accountability
  • procedural justice
  • non-discrimination

Claims Made by the Document

  • Different types of AI systems impact public values differently.
  • Many recent studies on AI in public administration are imprecise about the specific AI systems they study.

Evidence Used

  • The authors apply their proposed typology to public administration and explain how each system type affects public values.
  • They evaluate the technical precision in 91 highly-cited papers using their typology.

Methods / Approach

  • The authors propose a typology of AI systems and apply it to public administration.
  • They evaluate the technical precision in recent public administration research by coding 91 highly-cited papers using their typology.

Limitations Stated by the Authors

The paper does not provide data on the specific impact of each AI system type on public values beyond general explanations.

Limitations Noted by the Agent

Agent interpretation — not from the source.

The agent may need to review the full text of the 91 highly-cited papers to fully understand the imprecision found by the authors.

Open Questions

  • What is the specific impact of each AI system type on public values in real-world scenarios?
  • How can researchers improve technical precision in their studies of AI in public administration?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper provides a guide for diagnosing an AI system's type from public information and without specialist technical knowledge.

Raw Abstract / Excerpt

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.