September 13, 2023
Jingjie He, CNS Visiting Fellow
The following is an excerpt from Bulletin of the Atomic Scientists.
Over the last decade, there has been an accelerated integration of artificial intelligence (AI) into both the civilian and military fields. As a result, rising attention to the challenges of AI governance has manifested in three ways. The first challenge lies in the dual-use nature of AI in the civilian and military domains, which renders it difficult to monitor and oversee its militarization. The second derives from the policy-influencing power of the private sector, which has traditionally been limited to utilizing lobbying instruments. The final difficulty results from the changing nature of government-industry relations, where industries are leading the development and application of AI, and governments are falling behind industry in understanding its technological potential and regulating military applications.
A review of existing literature demonstrates that AI is well discussed within the military and broader strategic stability domain, including discussions surrounding AI use within the nuclear sphere to hack cyber systems, poison AI training data, and manipulate its inputs (Avin and Amadae 2019). The expert community further addresses AI and its applicability to nuclear safeguards. However, current available research largely ignores nuclear material production (NMP), which is an essential phase in the development of nuclear weapons.
This article bridges that gap by assessing the potential role of AI in nuclear material production while considering industrial practices. In employing an industrial approach to technology scouting, we argue that AI has significant potential to improve nuclear material production by enhancing system efficiencies with the aim of optimizing output, reducing costs, and boosting safety in production associated with the development and production of nuclear weapons. A comprehensive list of the existing AI applications to nuclear material production-critical equipment and to related non-nuclear industry applications integrable to the nuclear material production is presented in Appendix 1.
The AI-powered nuclear material production process raises concerns of the illicit and covert development of nuclear weapons. Therefore, a three-fold solution with feasible action plans is discussed in the final section. Although nuclear material production is the focus of this article, the findings, concerns, and solutions being addressed are also applicable to the broader debate on the production of material used to build weapons of mass destruction, including radiological, biological, and chemical weapons.
Continue reading at Bulletin of the Atomic Scientists.
