AI Alignment Modeled as Law-and-Economics Deterrence Problem

A new paper on arXiv (2605.01643) proposes modeling AI alignment using law-and-economics frameworks of deterrence and enforcement. The authors treat misconduct not as an external failure but as a strategic response to incentives: an AI agent weighs gain from violation against detection probability and punishment severity. They argue this logic applies naturally to agentic AI pipelines, where a solver may benefit from producing persuasive but incorrect answers, hiding uncertainty, or exploiting spurious shortcuts, while an auditor must decide whether costly monitoring is worthwhile. Alignment becomes a fixed-point problem: stronger penalties deter solver misbehavior but can reduce the auditor's incentive to inspect, since auditing mainly incurs cost on a seemingly aligned population. This perspective also redefines what counts as a post-training signal, challenging standard feedback approaches.