A Technical Typology of AI Systems in Public Administration

Source

Source name: arXiv
Source kind: paper
Authors / organization: Jonathan Rystrøm, Chris Schmitz, Nathan Davies, Gerhard Hammerschmid, Albert Meijer, Chris Russell
Venue: arXiv: cs.CY
Published: 2026-06-30
Collected: 2026-07-01
URL: http://arxiv.org/abs/2606.31755v1
PDF: https://arxiv.org/pdf/2606.31755v1
DOI: UNKNOWN

Plain-Language Brief

The paper argues that research on AI in public administration should be more precise about different types of AI systems. The authors present a typology of five categories of AI systems, evaluate the precision of recent studies, and provide recommendations for future research.

What the Document Says

The paper discusses the lack of technical precision in research on AI in public administration.
It introduces a typology of five categories of AI systems: hand-coded, glass-box, black-box, general-purpose, and agentic systems.
The authors evaluate the technical precision of 91 highly-cited papers (2019-2025) using their typology and find widespread imprecision.
They provide practical recommendations for future research, including avoiding common pitfalls and providing enough technical detail to locate the studied system in their typology.

Key Concepts Mentioned

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

Claims Made by the Document

Public administration research would benefit from more technical precision on 'AI'
The authors' typology can help identify distinct implications for public values of different AI systems.
Most papers in the evaluated sample lack technical precision.
The authors suggest that future research should provide enough technical detail to locate the studied system in their typology.

Evidence Used

The paper presents a detailed typology of AI systems.
The authors evaluate 91 highly-cited papers using their typology and present findings on the level of precision in these papers.
The authors provide practical recommendations for future research based on their evaluation.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

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.