AI Alignment via Incentives and Correction
We study AI alignment through the lens of law-and-economics models of deterrence and enforcement. In these models, misconduct is not treated as an external failure, but as a strategic response to incentives: an actor weighs the gain from violation against the probability of detection and the severity of punishment. We argue that the same logic arises naturally in agentic AI pipelines. A solver may benefit from producing a persuasive but incorrect answer, hiding uncertainty, or exploiting spurious shortcuts, while an auditor or verifier must decide whether costly monitoring is worthwhile. Alignment is therefore a fixed-point problem: stronger penalties may deter solver misbehavior, but the...
| Authors | Rohit Agarwal, Joshua Lin, Mark Braverman et al. |
|---|---|
| Primary category | cs.LG |
| Published | 2 May 2026 |
| Last updated at source | 11 May 2026 |
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