Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints
The episode discusses the paper "Where Not to Learn," which addresses how AI models rely on spurious correlations instead of meaningful evidence. The method uses attribution scores and subset constraints to identify and penalize reliance on misleading evidence that violates known domain knowledge. This approach aims to make AI systems more trustworthy and verifiable, moving them beyond a "black box."