When Explanations Compete: Policy-Aware Selection Under Uncertainty

Excerpt

arXiv:2410.05479v2 Announce Type: replace-cross Abstract: Uncertainty-aware explanation methods often produce several alternatives for the same prediction. Selecting among them requires a policy for balancing prediction confidence, uncertainty, and application constraints. This paper presents a framework for applying such policies to a fixed set of generated explanations. Candidates are characterised by uncertainty change, prediction direction, and, when available, interval position relative to