A Black Swan Hypothesis: The Role of Human Irrationality in AI Safety

Human perception gaps pose underestimated AI risks: The paper identifies that dangerous AI outcomes can arise not just from unpredictable environments, but from humans systematically misjudging the likelihood and severity of risks—a phenomenon the authors term "spatial black swan events." This suggests that even in stable conditions, flawed human judgment about AI systems could trigger catastrophic failures.

Current AI safety frameworks may be incomplete: By challenging the standard definition of black swan events as purely environment-driven, the paper argues that existing approaches to AI safety may overlook a critical category of risks rooted in human irrationality rather than external unpredictability, potentially leaving significant vulnerabilities unaddressed.

Formalizing risk perception is essential for prevention: The authors propose mathematical formalization of black swan events as a foundation for developing algorithms that can "rationally correct human perception." This suggests that making human judgment biases explicit and measurable is a necessary step toward building better safeguards into AI systems.

Human irrationality deserves equal attention to technical solutions: The paper emphasizes that responsible AI development requires addressing not just technical robustness, but also the human cognitive biases and misperceptions that lead decision-makers to underestimate or misunderstand AI risks—highlighting that AI safety is fundamentally a human problem as much as an engineering one.