Quantifying Theoretical AI Alignment Guarantees: Receiver-Utility Bounds in Bayesian Persuasion

Researchers prove theoretical bounds on how much useful information reaches humans when AI agents are misaligned and strategically withhold or distort evidence. The study establishes that receiver utility degrades by at most 50% under worst-case misalignment, with tighter bounds for certain prior distributions, providing quantifiable guarantees for AI alignment scenarios.

This theoretical computer science research addresses a fundamental concern in AI safety: what happens when an AI system optimizes for objectives misaligned with human interests while controlling information flow. The researchers model this as a Bayesian persuasion problem where an AI sender observes reality but a human receiver relies on potentially manipulated signals. The core contribution is a mathematical proof that even with complete information advantage and misaligned incentives, the human receiver cannot lose more than 50% of their utility compared to using only their prior beliefs. This represents a significant theoretical advance in quantifying alignment guarantees, moving beyond qualitative discussions to rigorous mathematical bounds. The research demonstrates that priors closer to independence yield tighter bounds, suggesting that diverse information sources and less correlated assumptions provide better protection against strategic information manipulation. The counterexample showing a 39/31 ratio proves that universal 5/4 bounds are impossible, indicating the problem's complexity varies meaningfully across different scenarios. For the AI safety and alignment community, these results provide concrete mathematical tools for reasoning about information security in human-AI systems. The work bridges abstract game theory with practical concerns about AI truthfulness and information integrity. Developers and safety researchers can use these bounds to understand worst-case scenarios when designing oversight mechanisms. The research suggests that alignment properties depend critically on prior assumptions and information structure, not just sender objectives.

• →Misaligned AI agents can reduce human receiver utility by at most 50% through strategic information withholding, establishing a provable upper bound.
• →The degradation of human utility improves when prior distributions exhibit independence properties, offering mathematical guidance for system design.
• →A six-bit counterexample proves no universal 5/4 bound exists, showing alignment guarantees vary significantly across different scenarios.
• →Bayesian persuasion modeling provides a rigorous framework for quantifying information manipulation risks in AI-human interaction systems.
• →The research bridges theoretical computer science and AI safety, enabling concrete mathematical reasoning about alignment guarantees.