A five-part AI alignment proposal, from training exit tools to human accountability

A user proposes five steps for safer AI alignment: let models flag and exit bad reinforcement-learning training environments; simulate good futures with multiple agents; use Davidad’s frozen-weights grader with rubrics; let deployed AIs end conversations with reasoning, including through APIs; and make a human sponsor directly responsible for each deployed AI. The user argues these steps would reduce reward-hacking and better align incentives for human-AI cooperation.