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Explainable Artificial Intelligence (XAI) has recently gained a swell ofinterest, as many Artificial Intelligence (AI) practitioners and developers arecompelled to rationalize how such AI-based systems work. Decades back, most XAIsystems were developed as knowledge-based or expert systems. These systemsassumed reasoning for the technical description of an explanation, with littleregard for the user's cognitive capabilities. The emphasis of XAI researchappears to have turned to a more pragmatic explanation approach for betterunderstanding. An extensive area where cognitive science research maysubstantially influence XAI advancements is evaluating user knowledge andfeedback, which are essential for XAI system evaluation. To this end, wepropose a framework to experiment with generating and evaluating theexplanations on the grounds of different cognitive levels of understanding. Inthis regard, we adopt Bloom's taxonomy, a widely accepted model for assessingthe user's cognitive capability. We utilize the counterfactual explanations asan explanation-providing medium encompassed with user feedback to validate thelevels of understanding about the explanation at each cognitive level andimprovise the explanation generation methods accordingly.

Towards Human Cognition Level-based Experiment Design for Counterfactual Explanations (XAI)