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Expert panel – Regulation of artificial intelligence in nuclear - Meeting two

July 2022

As part of our work to support the nuclear industry in embracing innovation, we ran an expert panel meeting on the use of artificial intelligence (AI) in the nuclear sector, along with the Advanced Nuclear Skills and Innovation Campus (ANSIC).

The aim of the expert panel was to establish a roadmap for effective and enabling regulation of AI in the nuclear sector.

Jointly organised by ONR and ANSIC, the seminar panel was hosted by RACE (Remote Applications in Challenging Environments), part of the United Kingdom Atomic Energy Authority (UKAEA).

ANSIC was a Department for Business, Energy and Industrial Strategy (BEIS) funded initiative led by National Nuclear Laboratory.

At the first meeting of the panel, it was agreed to identify potential applications of AI that could be challenging to regulate.

The intention being that these will then be put into the Regulatory Sandbox which ONR are developing.

Sandboxing enables innovators to test and trial new solutions in a safe environment without the pressures of the usual rules applying.

July's meeting of the panel, at the Culham Science Centre, near Oxford, featured representatives from organisations including EDF Energy, Rolls-Royce SMR, Sellafield Ltd, UKAEA and the Universities of Bristol, Manchester and Oxford.

The meeting led to discussion of these candidate proposals, grouped similar proposals together and selected two of them to develop further for entry into ONR’s regulatory sandbox.

Panel experts agreed to work collaboratively to develop the following two AI applications into opportunity/problem statements by licensees and stakeholders:

Use AI to derive information from plant to inform structural integrity claims in a safety case to help demonstrate reliability. It is thought that this will assist in the development of digital twins and probabilistic assessment to demonstrate asset in-service operational life.

Use AI for real-time application to inform operations and understanding stresses and potential environmental constraints to, for example, optimise robotic movements.