论文提出目标揭示框架，连接信息论赋能与人类能力型权力
Goal Revelation as a Bridge Between Information-Theoretic Empowerment and Capability-Based Human Power
这篇论文把两种衡量 AI 削弱人类权力的度量用不等式串了起来，还给出能从交互日志直接算的监控指标，做 AI 安全的方向可以看看。
arXiv 论文在马尔可夫博弈框架下比较两种权力度量：信息论赋能与基于能力的个体权力。作者用 Fano 不等式从个体权力推出目标揭示下界 B(s)，即潜在目标与实际轨迹之间的互信息，并证明 B(s) ≤ 目标揭示 ≤ 赋能的链式不等式。论文指出每层量取大值时更低层可为零：最大化赋能的助手可能选择让人类无法达成目标的宽影响接口，最大化揭示的助手无法区分正常接口与所有目标都导向错误结果的被篡改接口。作者提出可从交互日志两个统计量计算的满足度下界，用于监控部署系统中用户保留的权力。
Goal Revelation as a Bridge Between Information-Theoretic Empowerment and Capability-Based Human Power
Power matters for AI safety. Optimal agents tend to acquire power across a wide range of objectives (instrumental convergence). As we embed AI agents into socio-technical systems, humans risk gradual disempowerment. Both concerns call for a formal, quantitative measure of power. Two such measures in the context of Markov games are information-theoretic empowerment, the capacity of the channel from an agent's actions to future states, and capability-based individual power, a measure of goal attainment built from the probabilities that a human would attain each goal in a family of candidates were they pursuing it. We show where the two meet. From individual power, Fano's inequality yields a floor $B(s)$. This floor is a lower bound on what we term goal revelation, the mutual information $\mathbb{I}(G;ξ\mid s)$ between the human's latent goal and the realised trajectory. Empowerment caps revelation from above, so that $B(s) \le \mathbb{I}(G;ξ\mid s) \le \mathbb{I}(\mathbf{A}_h \to ξ\mid s) \le \mathfrak{E}_h(s)$. Each quantity can be large while the one below it stays at zero, highlighting the following failure modes. An assistant maximising empowerment can prefer an interface offering broad influence over goal-irrelevant degrees of freedom to one that lets the human attain every goal they might hold. An assistant maximising revelation cannot tell a working interface from one rewired so that every goal yields the wrong outcome, since relabelling goals leaves mutual information unchanged while zeroing every attainment probability. Positive guarantees therefore come only from the attainment end of the chain. A satisfaction floor, computable from two statistics of interaction logs, offers a way to monitor the power users retain in a deployed system, and simulations in random environments show the three quantities are correlated but far from interchangeable.
