MCMC-LIME: enhancing stability of LIME using Markov chain Monte Carlo approach

This paper deeply investigates the stability issues of the visual explainability technique, called the local interpretable model-agnostic explanations (LIME)1 and presents an enhanced technique and our findings. Explainability is one of the important tasks in explainable artificial intelligence (XAI) systems where LIME tool has been widely used as an explainer model. However, LIME’s perturbation-based sampling introduces randomness, negatively affecting the stability of LIME’s explanations. In this paper, we utilize the unique sampling mechanism of the well-known Markov Chain Monte Carlo (MCMC) model to address this issue. However, a question arises: Will MCMC alone provide a semantically meaningful segmentation for visual explainability? To ensure this, we modify 40% of perturbation using MCMC while preserving 60% of the original perturbation of LIME and introduce additional stability-enhancing mechanisms. Adaptive Segment Merge (ASM) is employed to refine segmentation boundaries before applying MCMC, ensuring that segments align with meaningful visual structures. Additionally, Sign Entropy-Based Feature Elimination (SEFE) suppresses the effects of the remaining unstable segments by filtering them based on their sign entropy, further improving the robustness of the explanations. To evaluate the performance of the proposed technique, we adopt the average rank similarity (ARS), which quantifies the consistency of feature rankings across multiple runs. Our simulations, conducted on images of five dog breeds (Samoyed, Pomeranian, Newfoundland, Pug, and Cocker Spaniel) along with a Coyote, and a Robin image, demonstrate that MCMC-LIME achieves ARS scores of 0.893, 0.887, 0.851, 0.874, 0.906, 0.850, and 0.903 compared to 0.803, 0.862, 0.596, 0.676, 0.718, 0.771, and 0.846 for LIME. These results indicate that MCMC-LIME significantly enhances the stability and reliability of visual explanations, making it a more robust alternative to the original LIME.