[2505.15516] Explainable embeddings with Distance Explainer

Computer Science > Machine Learning arXiv:2505.15516 (cs) [Submitted on 21 May 2025 (v1), last revised 24 Mar 2026 (this version, v2)] Title:Explainable embeddings with Distance Explainer Authors:Christiaan Meijer, E. G. Patrick Bos View a PDF of the paper titled Explainable embeddings with Distance Explainer, by Christiaan Meijer and E. G. Patrick Bos View PDF HTML (experimental) Abstract:While eXplainable AI (XAI) has advanced significantly, few methods address interpretability in embedded vector spaces where dimensions represent complex abstractions. We introduce Distance Explainer, a novel method for generating local, post-hoc explanations of embedded spaces in machine learning models. Our approach adapts saliency-based techniques from RISE to explain the distance between two embedded data points by assigning attribution values through selective masking and distance-ranked mask filtering. We evaluate Distance Explainer on cross-modal embeddings (image-image and image-caption pairs) using established XAI metrics including Faithfulness, Sensitivity/Robustness, and Randomization. Experiments with ImageNet and CLIP models demonstrate that our method effectively identifies features contributing to similarity or dissimilarity between embedded data points while maintaining high robustness and consistency. We also explore how parameter tuning, particularly mask quantity and selection strategy, affects explanation quality. This work addresses a critical gap in XAI research and ...