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

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arxiv 2407.18422 v3 pith:JXGGK3BR submitted 2024-07-25 cs.AI cs.LG

classification cs.AIcs.LG
keywords blackeventsswanhumandefinitionenvironmentsrarespatial
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Black swan events are statistically rare occurrences that carry extremely high risks. A typical view of defining black swan events is heavily assumed to originate from an unpredictable time-varying environments; however, the community lacks a comprehensive definition of black swan events. To this end, this paper challenges that the standard view is incomplete and claims that high-risk, statistically rare events can also occur in unchanging environments due to human misperception of their value and likelihood, which we call as spatial black swan event. We first carefully categorize black swan events, focusing on spatial black swan events, and mathematically formalize the definition of black swan events. We hope these definitions can pave the way for the development of algorithms to prevent such events by rationally correcting human perception.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Toward Evaluative Thinking: Meta Policy Optimization with Evolving Reward Models

    cs.CL 2025-04 conditional novelty 6.0 of 10

    MPO uses a meta reward model to continuously rewrite the reward model's evaluation prompt during PPO training, and the resulting models beat static-prompt RLAIF baselines on four tasks.

  2. Evolution and The Knightian Blindspot of Machine Learning

    cs.AI 2025-01 conditional novelty 6.0 of 10

    ML's formalisms, particularly RL's, exclude Knightian uncertainty, and evolution's diversify-and-filter mechanisms point toward a direct remedy.

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