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, blac
The proliferation of AI in public sectors necessitates a more granular understanding of its diverse technical forms to address governance and societal impacts effectively.
This typology provides a critical framework for policymakers and public administrators to differentiate AI systems, enabling more targeted and effective regulation and ethical oversight.
The debate around AI governance in public administration can transition from a monolithic view of 'AI' to one that recognizes and addresses the specific characteristics and impacts of different AI architectures.
- · AI ethicists
- · Public administration researchers
- · Governments implementing AI oversight
- · One-size-fits-all AI regulatory approaches
- · AI developers ignoring ethical frameworks
Public sector entities will gain a clearer understanding of the various AI systems they deploy or regulate.
This clarity will lead to the development of more nuanced and effective policies for AI accountability and fairness in government services.
These policies could influence global standards for public sector AI, promoting responsible innovation and reducing negative societal externalities.
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Read at arXiv cs.AI