A debate-trained AI R&D agent's safety case requires debate proficiency to imply honesty, honesty to persist, and deployment to tolerate errors.
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The safety case rests on four key claims: (1) the agent has become good at the debate game, (2) good performance in the debate game implies that the system is mostly honest, (3) the system will not become significantly less honest during deployment, and (4) the deployment context is tolerant of some errors.
Abstract of a paper sketching an alignment safety case for an AI R&D agent trained via debate.
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