Reverse game theory could offer a new approach to AI alignment

On 3 November 2026, MIT Technology Review reported on a new approach to AI alignment called "reverse game theory" (mechanism design): rather than peering inside models or training their values, it designs incentive rules so that following the rules becomes the most rational choice for an AI model. The report cites a preprint by MIT economist Stephen Morris and others that proposes having models predict each other's answers, compete against each other, or pair models with opposing biases, to stop models from going easy during tests. Princeton professor Elad Hazan proposes having models take turns as "solver" and "auditor", with penalties for both cheaters and negligent auditors. The approach remains at the stage of papers and proposals.

Written by AI from the reports · updated 3 h ago