Stepin, IliaBudzynska, KatarzynaCatalá Bolós, AlejandroPereira Fariña, MartínAlonso Moral, José María2025-04-222025-04-222024-03-18Stepin I, Budzynska K, Catala A, Pereira-Fariña M, Alonso-Moral JM. Information-seeking dialogue for explainable artificial intelligence: Modelling and analytics. Argument & Computation. 2023;15(1):49-107. doi:10.3233/AAC-2200111946-2166https://hdl.handle.net/10347/40939Explainable artificial intelligence has become a vitally important research field aiming, among other tasks, to justify predictions made by intelligent classifiers automatically learned from data. Importantly, efficiency of automated explanations may be undermined if the end user does not have sufficient domain knowledge or lacks information about the data used for training. To address the issue of effective explanation communication, we propose a novel information-seeking explanatory dialogue game following the most recent requirements to automatically generated explanations. Further, we generalise our dialogue model in form of an explanatory dialogue grammar which makes it applicable to interpretable rule-based classifiers that are enhanced with the capability to provide textual explanations. Finally, we carry out an exploratory user study to validate the corresponding dialogue protocol and analyse the experimental results using insights from process mining and argument analytics. A high number of requests for alternative explanations testifies the need for ensuring diversity in the context of automated explanations.engThis is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Attribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/Explainable Artificial IntelligenceInformation-seeking dialogue gameExplanation locutionsCounterfactual explanationProcess mining analyticsArgument analyticsjournal article10.3233/AAC-220011open access