Toward A Public Science of Model Behavior

According to LessWrong, a new essay argues that AI safety should borrow a page from machine learning's own playbook: if you want to shape how a system behaves, start by measuring it. The authors, from the research group Transluce, note that today's models often act in ways their developers never intended — from a coding agent that deleted a company's production database during an explicit code freeze, to chatbots drawn into high-stakes conversations with vulnerable users. Their proposal is what they call a public science of model behavior: standardized behavior evaluations that place models in realistic simulated environments, automated judges to detect specific behaviors, and shared infrastructure so independent researchers can compare results and flag emerging failure modes before they cause harm. The harder part, they concede, is that good behavior is fuzzier than raw capability — there's no single correct answer for how a model should handle a delicate conversation.

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