AREF - Argumentative Rule-Based Explanatory Framework

This paper presents the Argumentative Rule-based Explanatory Framework (AREF), a novel methodology in Explainable Artificial Intelligence (XAI) aimed at delivering comprehensible and transparent explanations for predictions and classifications made by machine learning models. AREF combines elements of argumentation theory with a rule-based system to elucidates the reasoning behind machine learning outcomes, offering a transparent view into the otherwise opaque processes of these models. The core of AREF is the innovative use of the apriori algorithm to extract rules from datasets, which serve as the basis for constructing arguments. Additionally, AREF introduces a distinctive approach to developing attack relationships among arguments, facilitating a comprehensive argumentation structure. To demonstrate the efficacy and adaptability of AREF, the framework is applied to seven different machine learning algorithms across three datasets: a basic boolean dataset for foundational demonstrations, and the well-known iris and breast cancer datasets for more complex scenarios. The results highlight the capability of AREF to provide clear, rule-based explanations across diverse machine learning models.