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Evaluation of artificial intelligence-enhanced critical infrastructure systems: A conceptual framework

Steven Pudney et al.

Evaluation2025https://doi.org/10.1177/13563890251350677article
AJG 2ABDC B
Weight
0.46

Abstract

The use of artificial intelligence in Critical Infrastructure Systems has increased substantially, having evolved to become both technically possible and financially beneficial. Yet there is an emerging consensus that the consideration and management of artificial intelligence-related risks in Critical Infrastructure Systems have not been commensurate with its rapid growth. Our surveys have identified that generalised artificial intelligence principles such as those promoted by the Organisation for Economic Co-operation and Development are alone not fit for purpose in guiding use of artificial intelligence in Critical Infrastructure Systems. Evaluation is an important aspect of that, and we argue for the development of a foundational approach suited to evaluation of artificial intelligence-enhanced Critical Infrastructure Systems as a base to further research and improve practice. This study develops a novel conceptual framework for evaluation of artificial intelligence-enhanced Critical Infrastructure Systems, based on theory adaptation of Value-Focused Thinking. The framework offers simplicity and additional functionality over the default principles-based framework.

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https://doi.org/https://doi.org/10.1177/13563890251350677

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@article{steven2025,
  title        = {{Evaluation of artificial intelligence-enhanced critical infrastructure systems: A conceptual framework}},
  author       = {Steven Pudney et al.},
  journal      = {Evaluation},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/13563890251350677},
}

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Evaluation of artificial intelligence-enhanced critical infrastructure systems: A conceptual framework

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Evidence weight

0.46

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.37 × 0.4 = 0.15
M · momentum0.60 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.