Feature-Guided Neighbor Selection for Non-Expert Evaluation of Model Predictions

Courtney Ford, Mark T. Keane

Keywords: Explainable AI, Expertise, Example-Based Explanations

TL;DR: We introduce Feature-Guided Neighbor Selection (FGNS), a post hoc XAI method that selects class-representative examples using local and global feature importance, improving non-experts' ability to detect model errors.

Abstract: Explainable AI (XAI) methods often struggle to generate clear, interpretable outputs for users without domain expertise. We introduce Feature-Guided Neighbor Selection (FGNS), a post hoc method that enhances interpretability by selecting class-representative examples using both local and global feature importance. In a user study (N = 98) evaluating Kannada script classifications, FGNS significantly improved non-experts' ability to identify model errors while maintaining appropriate agreement with correct predictions. Participants made faster and more accurate decisions compared to those given traditional k-NN explanations. Quantitative analysis shows that FGNS selects neighbors that better reflect class characteristics rather than merely minimizing feature-space distance, leading to more consistent selection and tighter clustering around class prototypes. These results support FGNS as a step toward more human-aligned model assessment, although further work is needed to address the gap between explanation quality and perceived trust.

Paper Type: New Full Paper

Submission Number: 1