Artificial intelligence in government: Concepts, standards, and a
unified framework
- URL: http://arxiv.org/abs/2210.17218v2
- Date: Wed, 25 Oct 2023 18:35:20 GMT
- Title: Artificial intelligence in government: Concepts, standards, and a
unified framework
- Authors: Vincent J. Straub, Deborah Morgan, Jonathan Bright and Helen Margetts
- Abstract summary: Recent advances in artificial intelligence (AI) hold the promise of transforming government.
It is critical that new AI systems behave in alignment with the normative expectations of society.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advances in artificial intelligence (AI), especially in generative
language modelling, hold the promise of transforming government. Given the
advanced capabilities of new AI systems, it is critical that these are embedded
using standard operational procedures, clear epistemic criteria, and behave in
alignment with the normative expectations of society. Scholars in multiple
domains have subsequently begun to conceptualize the different forms that AI
applications may take, highlighting both their potential benefits and pitfalls.
However, the literature remains fragmented, with researchers in social science
disciplines like public administration and political science, and the
fast-moving fields of AI, ML, and robotics, all developing concepts in relative
isolation. Although there are calls to formalize the emerging study of AI in
government, a balanced account that captures the full depth of theoretical
perspectives needed to understand the consequences of embedding AI into a
public sector context is lacking. Here, we unify efforts across social and
technical disciplines by first conducting an integrative literature review to
identify and cluster 69 key terms that frequently co-occur in the
multidisciplinary study of AI. We then build on the results of this
bibliometric analysis to propose three new multifaceted concepts for
understanding and analysing AI-based systems for government (AI-GOV) in a more
unified way: (1) operational fitness, (2) epistemic alignment, and (3)
normative divergence. Finally, we put these concepts to work by using them as
dimensions in a conceptual typology of AI-GOV and connecting each with emerging
AI technical measurement standards to encourage operationalization, foster
cross-disciplinary dialogue, and stimulate debate among those aiming to rethink
government with AI.
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