Artificial Intelligence (AI) in the Nuclear Power Plants: Who Is Liable When AI Fails to Perform

Karim, Ridoan; Muhammad Sukki, Firdaus

Abstract

The sheer magnitude of process parameters and interactions between the systems in a nuclear power plant’s operation poses complications for operators. The operations became more challenging, especially during emergencies. Any recovery from an emergency relies heavily on modifying the system rapidly based on accessible data analysis. Artificial intelligence (AI) technology, working as expert guidance and quick access to a broad knowledge base, may resolve most of the complexities. Hence, experts believe that AI can be the expert system for operating a complex technology like the nuclear power plant. Nevertheless, AI deployment in the energy sector can also introduce several risks and challenges, including physical damage to the power plant. The existing nuclear liability laws and regulations promises a robust compensation packages for nuclear damages; nevertheless, the damage resulting from AI in nuclear power plant is still yet to be tested. Hence, this chapter particularly analyze whether the existing nuclear liability regimes and the tort law, possibly in combination with insurance and strict liability provisions, can constitute a sufficient nuclear damage claims or not.

Citation

Karim, R., & Muhammad Sukki, F. (2022). Artificial Intelligence (AI) in the Nuclear Power Plants: Who Is Liable When AI Fails to Perform. In F. Taghizadeh-Hesary, & D. Zhang (Eds.), The Handbook of Energy Policy (1-21). Singapore: Springer. https://doi.org/10.1007/978-981-16-9680-0_27-1

• Acceptance Date: Jun 29, 2022
• Online Publication Date: Sep 30, 2022
• Publication Date: Sep 30, 2022
• Deposit Date: Sep 30, 2022
• Publisher: Springer
• Pages: 1-21
• Book Title: The Handbook of Energy Policy
• Chapter Number: 1
• ISBN: 978-981-16-9680-0
• DOI: https://doi.org/10.1007/978-981-16-9680-0_27-1
• Public URL: http://researchrepository.napier.ac.uk/Output/2926298
• Publisher URL: https://link.springer.com/referenceworkentry/10.1007/978-981-16-9680-0_27-1