Consistency of Explanations in Evolving Supervised Learning Contexts

In many real-world scenarios, explanations are not perceived as isolated entities, but rather as streams accompanying models that operate over extended periods of time. In such contexts, users are exposed not to a single explanation, but to a sequence of explanations whose evolution may influence trust, understanding, and decision-making. In this work, we aim to capture the evolution of explanations that exhibit sudden changes, contradict prior explanations, or demonstrate erratic fluctuations, as these may compromise their practical utility, even when maintaining a high level of predictive performance. We propose a framework aimed at supporting the assessment of the consistency of explanations for evolving supervised learning contexts, taking into account the variability of the input datasets, and the uncertainty of the model. We introduce a model-agnostic and explanation-agnostic index for quantifying the temporal consistency of automated explanations. We empirically demonstrate that the proposed index facilitates the quantification and localization of temporal instability in explanation streams. Experimental results for simulated multivariate time series underscores the ability of the proposed index to capture instantaneous changes and their relative impact in relation to historical patterns.

keywords: Explainable Artificial Intelligence, Online learning, Stability, Classification, Fuzzy Linguistic Summarization