TaylorPODA: A New Method for Explaining Opaque AI Models

A novel post-hoc model-agnostic local attribution technique, named TaylorPODA (Taylor exPansion-Originated aDaptive Attribution), has been introduced by researchers to clarify the workings of opaque AI systems. This approach is based on the Taylor expansion framework and establishes a series of principles that define the essential requirements for linking Taylor terms to specific features. The authors evaluate current post-hoc model-agnostic local attribution methods and highlight a core conflict between principled attribution and the adaptation to user-specific utilities. TaylorPODA seeks to resolve this issue by offering more precise and flexible attributions. This research is available on arXiv with the identifier 2507.10643.