How informative is your XAI? Assessing the quality of explanations through information power - Matarese, Marco; Rea, Francesco; Rohlfing, Katharina J.; Sciutti, Alessandra (Frontiers in Computer Science, 2025)
A growing consensus emphasizes the efficacy of user-centered and personalized approaches within the field of explainable artificial intelligence (XAI). The proliferation of diverse explanation strategies in recent years promises to improve the interaction between humans and explainable agents. This poses the challenge of assessing the goodness and efficacy of the proposed explanation, which so far has primarily relied on indirect measures, such as the user's task performance. We introduce an assessment task designed to objectively and quantitatively measure the goodness of XAI systems, specifically in terms of their “information power.” This metric aims to evaluate the amount of information the system provides to non-expert users during the interaction. This work has a three-fold objective: to propose the Information Power assessment task, provide a comparison between our proposal and other XAI goodness measures with respect to eight characteristics, and provide detailed instructions to implement it based on researchers' needs.