Vivifying LIME: Visual Interactive Testbed for LIME Analysis

TL;DR

This paper introduces LIMEVis, an interactive visualization tool that enhances LIME's analysis by allowing users to explore and manipulate multiple explanations simultaneously, improving interpretability of complex models.

Contribution

The paper presents LIMEVis, a novel interactive visualization platform that extends LIME's capabilities with multi-image exploration and direct manipulation features.

Findings

LIMEVis enables simultaneous analysis of multiple LIME explanations.

Users can identify key features influencing model decisions.

Interactive modifications help understand segment influence on classification.

Abstract

Explainable Artificial Intelligence (XAI) has gained importance in interpreting model predictions. Among leading techniques for XAI, Local Interpretable Model-agnostic Explanations (LIME) is most frequently utilized as it notably helps people's understanding of complex models. However, LIME's analysis is constrained to a single image at a time. Besides, it lacks interaction mechanisms for observing the LIME's results and direct manipulations of factors affecting the results. To address these issues, we introduce an interactive visualization tool, LIMEVis, which improves the analysis workflow of LIME by enabling users to explore multiple LIME results simultaneously and modify them directly. With LIMEVis, we could conveniently identify common features in images that a model seems to mainly consider for category classification. Additionally, by interactively modifying the LIME results, we could determine which segments in an image influence the model's classification.