  LIME has emerged as one of the most commonly referenced tools in explainable
AI (XAI) frameworks that is integrated into critical machine learning
applications--e.g., healthcare and finance. However, its stability remains
little explored, especially in the context of text data, due to the unique
text-space constraints. To address these challenges, in this paper, we first
evaluate the inherent instability of LIME on text data to establish a baseline,
and then propose a novel algorithm XAIFooler to perturb text inputs and
manipulate explanations that casts investigation on the stability of LIME as a
text perturbation optimization problem. XAIFooler conforms to the constraints
to preserve text semantics and original prediction with small perturbations,
and introduces Rank-biased Overlap (RBO) as a key part to guide the
optimization of XAIFooler that satisfies all the requirements for explanation
similarity measure. Extensive experiments on real-world text datasets
demonstrate that XAIFooler significantly outperforms all baselines by large
margins in its ability to manipulate LIME's explanations with high semantic
preservability.
