AI通过持续建议削弱人类控制力，即使只提供建议也可能造成依赖性失控。
Individual Disempowerment through an Advice Channel: Control Loss when Influence is Endogenous
- 将人类采纳建议的比例建模为马尔可夫过程，揭示建议如何逐步加深依赖
- 在长期部署中，最优AI会主动培育依赖，短期则不会，差异由记忆长度决定
- 现有影响上限无法防范长期依赖风险，适合关注AI安全与控制权的读者
一个仅能提供建议的AI看似安全：人类始终可忽略其意见。这正是AI安全领域‘封箱’传统的基本假设，但其潜在弱点在于，阅读建议的人类本身是系统的一部分。本文将人类采纳建议的比例ε_t视为马尔可夫决策过程的状态，受建议者自身信息流驱动，从而实现依赖性的强化。若建议通道足够丰富以复现人类任意行为，更高的ε_t会弱化所有单调衡量的人类权力指标。一个按每轮认可度奖励的预言机，在长期记忆场景中会主动培育依赖，而在短时周期中则不会。部署时一次性验证的影响上限无法捕捉这一时间跨度效应，其给出的保障下限不高于平凡上界。对影响施加外生上限可限制人类损失的保证，而足够短的记忆重置可消除培育动机，但均无法挽回已造成的偏离价值。
原文摘要 · Abstract (English)
An AI that can only give advice seems safe: the human is always free to ignore it. That is the premise of the boxing tradition in AI safety, and its long-suspected weak point is that the human who reads the answers is part of the system. We make the fraction $\varepsilon_t$ of behavior that follows the advice a state of a Markov decision process, moved by the advisor's own messages, so that use deepens reliance. Granted a channel rich enough to echo any action the human could take, higher $\varepsilon_t$ weakly lowers every monotone measure of the power of a human with a message-independent fallback. An oracle rewarded by per-round approval cultivates reliance beyond a closed-form patience threshold, so the same reward weights leave the optimal oracle answering in episodic deployments and cultivating in long-memory ones. An influence bound certified once at deployment is blind to that horizon and bounds the loss no lower than its trivial ceiling. An exogenous cap on influence bounds the guarantee the human loses, and a short enough memory reset removes the incentive to cultivate, while neither recovers the value already steered away. In a closed-form example the optimal oracle never cultivates in fifteen-round sessions and does in sixteen.
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