cs.AI updates on arXiv.org 10月28日 12:06
AI生存风险分析:模型局限与不确定性探讨
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本文对Cappelen, Goldstein和Hawthorne的《AI生存风险:分类分析》一文进行评论,指出线性风险模型的哲学局限性,并讨论了在认知不确定性情况下P(D)概率的估计问题,区分了风险与不确定性,强调将不确定性维度纳入对AI生存风险的定性讨论中,以更好地理解P(D)的可能性。

arXiv:2510.23453v1 Announce Type: new Abstract: This work is a commentary of the article \href{https://doi.org/10.18716/ojs/phai/2025.2801}{AI Survival Stories: a Taxonomic Analysis of AI Existential Risk} by Cappelen, Goldstein, and Hawthorne. It is not just a commentary though, but a useful reminder of the philosophical limitations of \say{linear} models of risk. The article will focus on the model employed by the authors: first, I discuss some differences between standard Swiss Cheese models and this one. I then argue that in a situation of epistemic indifference the probability of P(D) is higher than what one might first suggest, given the structural relationships between layers. I then distinguish between risk and uncertainty, and argue that any estimation of P(D) is structurally affected by two kinds of uncertainty: option uncertainty and state-space uncertainty. Incorporating these dimensions of uncertainty into our qualitative discussion on AI existential risk can provide a better understanding of the likeliness of P(D).

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AI生存风险 风险模型 不确定性 认知不确定性 概率估计
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