Abstract

Artificial Intelligence constitutes a powerful means when deployed for assisting people in making well-informed decisions. However, this comes with a price. It is indeed the urge to overcome ethical issues involving AI-based systems, along with distrust from their users, that denotes the reason for the recent interest in the Explainable AI (XAI) research field. The idea is that the trustworthiness of the AIs can be improved by building more transparent and interpretable tools capable of explaining what the system has done while disclosing salient information during these processes. Several studies advocate an account of explanations that is primarily argumentative and point toward specific templates that may be employed to clarify the rationale behind the AIs' decisions. Such templates can then be instantiated and fed to a chatbot that will deliver the requested information by interacting with the interested user. The talk will be structured in three parts. Starting from a general introduction of XAI and its well-established relation with argumentation theory, I will then proceed, in the second part, with an analysis of (the most relevant) Douglas Walton’s Argument Schemes. This will include how they can be adapted to provide suited explanation templates and an overall presentation of the formal dialogue protocols employed to convey such (instantiated) templates. In the final part of the talk, I will focus on argumentation-based chatbots and how these can deliver exhaustive explanations by prompting the interacting user to question for information until satisfied. Chatbots are designed to converse with humans, thus representing media intrinsically suited to provide explanations.