A Quantitative Comparison of LIME and SHAP using Stamp-Based Distance Method on Image Data

Dong-Su Song, Jay-Hoon Jung

http://doi.org/10.5626/JOK.2023.50.10.906

XAI(eXplainable AI), 인공신경망, MNIST, 도장 기반의 distance method, LIME, SHAP Abstract XAI, or eXplainable AI, is a technique used to explain artificial neural networks in a way that can be understood by humans. However, it is difficult to compare explanations and heat maps produced by XAI algorithms numerically as it is unclear how humans interpret them. This presents a challenge in determining which XAI algorithm is the most effective and accurate in providing explanations. Therefore, we introduced a stamp-based distance method to compare several XAI algorithms and identify the most accurate algorithm. The proposed method involves evaluating the quality of explanations generated by XAI algorithms applied to a deep learning model trained to detect the presence of stamps in the MNIST dataset. This evaluation was performed using statistical techniques to determine the effectiveness of each XAI algorithm. This paper evaluated performances of LIME and SHAP algorithms using the distance method, which compared explanations produced by each algorithm. Result revealed that LIME with the Felzenszwalb method provided more effective explanations than other LIME and SHAP algorithms.