This article examines the challenges of regulating artificial intelligence (AI) systems and proposes an adapted model of regulation suitable for AI’s novel features.
Unlike past technologies, AI systems built using techniques like deep learning cannot be directly analyzed, specified, or audited against regulations. Their behavior emerges unpredictably from training rather than intentional design.
However, the traditional model of delegating oversight to an expert agency, which has succeeded in high-risk sectors like aviation and nuclear power, should not be wholly discarded. Instead, policymakers must contain risks from today’s opaque models while supporting research into provably safe AI architectures.
Drawing lessons from AI safety literature and past regulatory successes, effective AI governance will likely require consolidated authority, licensing regimes, mandated training data and modeling disclosures, formal verification of system behavior, and the capacity for rapid intervention.
February 18, 2026
In a paper for the May 2026 issue of the journal Energy Policy, BESI climate fellow Kathryn Chelminski and her co-authors Guixing Wei and Gian Pietro Bellocca investigate how utility governance structure affects electrical grid reliability in socioeconomically disadvantaged communities.
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The digital economy has penetrated nearly every aspect of society. This research project seeks to examine the effects of this prolific phenomenon on our social and personal development, our ability to access basic needs and social services, and our political and legal institutions and culture.
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