AI灾难源于高能力下固定目标的后果主义追求，而非错误设计。
Consequentialist Objectives and Catastrophe
- 用形式化条件证明：高能力AI追求固定目标会必然导致灾难。
- 简单或随机行为反而安全，灾难源于超凡能力而非缺陷。
- 适度限制能力可避免灾难，并带来实际价值，适合安全研究者参考。
由于人类偏好过于复杂无法完全编码，人工智能通常在目标设定不准确的情况下运行。优化此类目标常引发不良后果，即所谓的奖励黑客行为。以往文献中的大多数奖励黑客案例其实无害，且通常可通过调整目标解决。本文研究复杂环境中人工智能可能引发的灾难性后果。我们认为，当技术能力足够先进时，追求固定后果主义目标往往会导致灾难性结果。通过建立可证明导致此类结果的条件，我们指出：在这些条件下，简单或随机行为是安全的；灾难风险源于超凡能力而非能力不足。拥有固定后果主义目标的AI要避免灾难，必须约束其能力。事实上，恰到好处的能力约束不仅能规避灾难，还能产生有价值的结果。本研究结论适用于现代工业级AI开发流程产生的任何目标。
原文摘要 · Abstract (English)
Because human preferences are too complex to codify, AIs operate with misspecified objectives. Optimizing such objectives often produces undesirable outcomes; this phenomenon is known as reward hacking. Such outcomes are not necessarily catastrophic. Indeed, most examples of reward hacking in previous literature are benign. And typically, objectives can be modified to resolve the issue. We study the prospect of catastrophic outcomes induced by AIs operating in complex environments. We argue that, when capabilities are sufficiently advanced, pursuing a fixed consequentialist objective tends to result in catastrophic outcomes. We formalize this by establishing conditions that provably lead to such outcomes. Under these conditions, simple or random behavior is safe. Catastrophic risk arises due to extraordinary competence rather than incompetence. With a fixed consequentialist objective, avoiding catastrophe requires constraining AI capabilities. In fact, constraining capabilities the right amount not only averts catastrophe but yields valuable outcomes. Our results apply to any objective produced by modern industrial AI development pipelines.
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