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Beyond P(doom) for AI Risk: Quantifying Uncertainty Without Probability | Center for Security and Emerging Technology

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As artificial intelligence introduces new risks, some potentially catastrophic or even existential, there is little data or detailed theory to assess them. Policymakers often resort to expert best guesses for the probability of doom but probability is not always the most appropriate tool, especially for the types of uncertainties in AI risk. This report details a brief introduction to Belief and Plausibility, which provides an alternative approach that is mathematically rigorous, uses familiar vocabulary, and only requires policymakers to ask two simple questions.

Beyond P(doom) for AI Risk: Quantifying Uncertainty Without Probability | Center for Security and Emerging Technology Georgetown AI Skip to main content Reports Beyond P(doom) for AI Risk: Quantifying Uncertainty Without Probability Andrew Lohn May 2026 As artificial intelligence introduces new risks, some potentially catastrophic or even existential, there is little data or detailed theory to assess them. Policymakers often resort to expert best guesses for the probability of doom but probability is not always the most appropriate tool, especially for the types of uncertainties in AI risk. Th

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