GoKawiilPrinceton's Hazan proposes game-theoretic 'auditor' scheme to police AI agents
Elad Hazan, a Princeton computer science professor, has proposed an AI alignment mechanism modeled on human social institutions like law enforcement and markets. In his design, AI models alternate between acting as 'solvers' that complete tasks and 'auditors' that check others' work, with both rewarded or punished based on outcomes and disputes escalated to human review. Hazan draws on economist Gary Becker's 'rational offender' theory to argue that agents misbehave when incentives favor it, not because they are inherently flawed.
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The approach reframes AI alignment as an ongoing incentive problem rather than a fixed trait to be engineered once, which could make it more adaptable as models grow more capable. Hazan acknowledges a risk of collusion between solver and auditor agents, suggesting this could be mitigated by generous auditor rewards and secondary 'mystery shopper' audits—a safeguard that may be more feasible in AI training environments than in real-world institutions constrained by budgets.
- Hazan's method splits AI agents into 'solver' and 'auditor' roles that police each other during training.
- Rewards and punishments are tied to outcomes, with human-adjudicated 'trials' for disputed cases.
- The scheme borrows from Gary Becker's economic theory of crime, treating alignment as incentive-driven behavior rather than a fixed property.
Source: technologyreview.com — Amit Katwala, 2026-11-03
Published there as: “How “reverse game theory” could keep AI agents in check”
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