GRALIS: A Unified Canonical Framework for Linear Attribution Methods via Riesz Representation
The episode discusses "GRALIS: A Unified Canonical Framework for Linear Attribution Methods via Riesz Representation," a paper that formalizes how attribution works using linear methods. Hosts discuss how this framework provides a common, standardized mathematical language to explain *how* AI features combine to reach conclusions, improving trust and enabling rigorous benchmarking of explainability.