AI RESEARCH
FRInGe: Distribution-Space Integrated Gradients with Fisher--Rao Geometry
arXiv CS.LG
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ArXi:2605.06404v1 Announce Type: new Gradient-based attribution methods are model-faithful and scalable, but Integrated Gradients (IG) can be brittle because explanations depend on heuristic baselines, straight-line paths, discretization, and saturation. We propose Fisher--Rao Integrated Gradients (FRInGe), which defines both the reference and interpolation schedule in predictive distribution space. FRInGe replaces input baselines with a maximum-entropy predictive reference and follows a Fisher-Rao geodesic on the probability simplex.