Autonomy and Machine Learning as Risk Factors at the Interface of Nuclear Weapons, Computers and People

In Vincent Boulanin (ed.), The Impact of Artificial Intelligence on Strategic Stability and Nuclear Risk: Euro-Atlantic Perspectives. Stockholm, Sweden: pp. 105-118 (2019)
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Abstract

This article assesses how autonomy and machine learning impact the existential risk of nuclear war. It situates the problem of cyber security, which proceeds by stealth, within the larger context of nuclear deterrence, which is effective when it functions with transparency and credibility. Cyber vulnerabilities poses new weaknesses to the strategic stability provided by nuclear deterrence. This article offers best practices for the use of computer and information technologies integrated into nuclear weapons systems. Focusing on nuclear command and control, avoiding autonomy and machine learning is recommended as one means to reduce the existential risk of unintended nuclear conflict.

Author's Profile

S. M. Amadae
Massachusetts Institute of Technology

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