2020/10/23 by Dina Mardaoui, Damien Garreau, Mardaoui, Dina +1 · 2 citations
Computer Science · #Explainable Artificial Intelligence (XAI) #Bayesian Modeling and Causal Inference #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2010.12487
Text data are increasingly handled in an automated fashion by machine learning algorithms. But the models handling these data are not always well-understood due to their complexity and are more and more often referred to as "black-boxes." Interpretability methods aim to explain how these models operate. Among them, LIME has become one of the most popular in recent years. However, it comes without theoretical guarantees: even for simple models, we are not sure that LIME behaves accurately. In this paper, we provide a first theoretical analysis of LIME for text data. As a consequence of our theoretical findings, we show that LIME indeed provides meaningful explanations for simple models, namely decision trees and linear models.