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Published (Version of record)CC BY-NC V4.0, Open Access
Published (Version of record)CC BY-NC V4.0, Open
Journal article
Evaluation of artificial intelligence-enhanced critical infrastructure systems: A conceptual framework
Evaluation, Vol.31(3), pp.412-443
07/2025
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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.
Details
- Title
- Evaluation of artificial intelligence-enhanced critical infrastructure systems: A conceptual framework
- Creators
- Steven Pudney - Southern Cross UniversityDavid Mills - Queensland University of TechnologyAli Reza Alaei - Southern Cross UniversitySam Sellers - Southern Cross UniversityJaroslav Dvorak - Klaipėda UniversityOto Potluka - University of Basel
- Publication Details
- Evaluation, Vol.31(3), pp.412-443
- Publisher
- SAGE Publications; THOUSAND OAKS
- Number of pages
- 32
- Grant note
- This work was supported by the European Cooperation in Science and Technology [grant number CA20112].
- Identifiers
- 991013304628702368
- Copyright
- © The Author(s) 2025.
- Academic Unit
- Information Technology; Faculty of Science and Engineering; Engineering
- Language
- English
- Resource Type
- Journal article