Improving Local Interpretable Model-agnostic Explanations Stability

Local Interpretable Model-Agnostic Explanations (LIME) is a widely used explainable artificial intelligence (XAI) technique for tabular data.LIME explains how classifiers or regressors make decisions.However, randomization during synthetic data production makes LIME unstable, leading to distrust in its explanations.The objective of this paper is to present STable LIME (ST-LIME).ST-LIME is an extension of LIME that addresses its instability by using fixed noise to generate consistent synthetic data.Additionally, ST-LIME enhances fidelity by employing the standard deviation to create the original data distribution rather than relying on randomization.For each training data feature, calculate standard deviation values with fixed noise to provide unique synthetic data.The paper evaluates the stability of explanations using the Feature Sequence Stability Index (FSSI) and compares ST-LIME against five state-of-the-art techniques on several popular tabular datasets.On average, ST-LIME outperforms these techniques in stability (100%) and fidelity (98.72%), with a low execution time.