Explainable artificial intelligence (XAI) is used to elucidate how predictions from machine learning (ML) models are generated . These explanations are reported by assigning relative importance to the input variables. Notably, different XAI methods adopt different approximations to determine this importance in relation to the ML model . These distinct techniques come with their individual variances, underpinned by a foundation of internal model uncertainty that forms the basis for their explanations. As these XAI methods yield outcomes with varying degrees of uncertainty , it raises concerns about potential distrust among end-users. Thus, in this study, we delve into how the internal model representations affect the stability of these explanations, notably in the field of toxicity prediction.