In March 2022, an Indian BrahMos missile launched from Sirsa landed in a sparsely populated region of Mian Channu in Pakistan. The episode, which India described as an accident, fortunately, caused no casualties but led to fears that Pakistan might respond in kind, fueling escalation. A few months later, similar uncertainty surrounding the origin of a missile that exploded in Przewodów, Poland during a particularly acute period of Russian bombardment of Ukrainian energy facilities triggered fears that NATO might find itself at war. And perhaps the most famous close call was the nuclear false alarm in 1983 when a Soviet early warning system detected the launch of five U.S. intercontinental ballistic missiles. Stanislav Petrov’s decision to ignore that warning is credited with avoiding nuclear war. The integration of AI tools for targeting and engagement, as well as early warning and decision support functions, might make these close calls more common. In light of this increased risk, efforts to increase confidence that AI-enabled systems are acting as designed become increasingly important.
Assurance for AI systems presents unique challenges.
While the challenges facing policymakers in the realm of AI governance are significant, they are not insurmountable.
Over the last several years, analyses of potential future conflicts have stressed the growing role that autonomous systems will play across domains and functions. As these systems are integrated into global militaries, it is increasingly likely that accidents could lead to escalation (inadvertent or advertent) in the face of improperly tested and evaluated platforms. Collaboration with both allies and adversaries on testing and evaluation has the potential to reduce these accidents and the consequent escalation of conflicts, driving compliance with international law. Establishing international standards and norms about the employment of AI in safety-critical contexts is the prudent way forward for this collaboration.
Read the full article from Lawfare.
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