2022/01/06 by Peter Xenopoulos, Gromit Chan, Xenopoulos, Peter +10 · 1 citation
Computer Science · Medicine · #Advanced Neuroimaging Techniques and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Medical Imaging Techniques and Applications #Topological and Geometric Data Analysis #cs.LG
paper · pdf · doi:10.48550/arxiv.2201.02155
arxiv created 2022/01/06 · openalex publication_date 2022/01/06 · arxiv updated 2022/01/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Local explainability methods -- those which seek to generate an explanation for each prediction -- are becoming increasingly prevalent due to the need for practitioners to rationalize their model outputs. However, comparing local explainability methods is difficult since they each generate outputs in various scales and dimensions. Furthermore, due to the stochastic nature of some explainability methods, it is possible for different runs of a method to produce contradictory explanations for a given observation. In this paper, we propose a topology-based framework to extract a simplified representation from a set of local explanations. We do so by first modeling the relationship between the explanation space and the model predictions as a scalar function. Then, we compute the topological skeleton of this function. This topological skeleton acts as a signature for such functions, which we use to compare different explanation methods. We demonstrate that our framework can not only reliably identify differences between explainability techniques but also provides stable representations. Then, we show how our framework can be used to identify appropriate parameters for local explainability methods. Our framework is simple, does not require complex optimizations, and can be broadly applied to most local explanation methods. We believe the practicality and versatility of our approach will help promote topology-based approaches as a tool for understanding and comparing explanation methods.