How to Validate XAI in Longitudinal Studies?

We propose a study design for validating explainable AI (XAI) tools in longitudinal contexts, focusing on ubiquitous and wearable computing. We aim to address unique challenges such as evolving user behavior and temporal dependencies inherent in longitudinal data. The study design includes both general and longitudinal explanation validation, assessing five XAI constructs: trust, satisfaction, actionability, understanding, and reliance through end-user studies. We demonstrate the proposed study design through a working example utilizing two XAI techniques -SHAP-based feature importance and Bayesian counterfactual explanations- on the LAUREATE dataset, which includes machine learning models for estimating weekly affective states of students based on physiological and behavioral data. To the best of our knowledge, this is the first study design that aims to investigate the longitudinal aspects of XAI, by proposing to evaluate the impact of the longitudinal order in which instances and their corresponding explanations are presented to end-users.