Reconciling Explanations in Multi-Model Systems through Probabilistic Argumentation

Abstract

Explainable Artificial Intelligence (XAI) has become critical in enhancing the transparency and trustworthiness of AI systems, especially as these systems are increasingly deployed in high-stakes domains such as healthcare and finance. Despite the progress made in developing explanation generation techniques for individual machine learning (ML) models, significant challenges remain in achieving coherent and comprehensive explanations in multi-model systems. This paper addresses these challenges by focusing on the explanation reconciliation problem (ERP) within multi-model systems. Traditional explanation generation technique often fall short in multi-model systems contexts, where explanations from different models can conflict and fail to form a cohesive narrative. Through the use of probabilistic argumentation and knowledge representation techniques, we propose a framework for generating holistic explanations that align with human cognitive processes. Our approach involves mapping uncertain explanation information to probabilistic arguments and introducing criteria for explanation reconciliation based on user perspectives such as optimism, pessimism, fairness. In addition, we introduce the relative independence assumption to optimise the search space for computational explanations.