Moving Beyond Text: Multi-modal Expansion of the Toulmin Model for Enhanced AI Legal Reasoning

In the burgeoning field of artificial intelligence’s application in law, enhancing the precision of legal reasoning is a pressing challenge. The Toulmin model, a fundamental framework in AI for legal problem-solving, primarily focuses on textual data, yet it faces limitations in the context of today’s multi-modal digital environment. This paper aims to expand the scope of the Toulmin model to incorporate multi-modal consideration. Based on the multi-modal argumentation theory, the expansion of the Toulmin model will focus on multi-modal data sources, such as text, images, audio, and video, multi-modal warrant, and multi-modal rebuttals, so that enriching AI’s legal argumentation capabilities. Concluding with its theoretical and practical implications, this paper sets a direction for future research in AI and law, highlighting the critical role of multi-modal concern in advancing AI’s ability to handle complex legal scenarios with enhanced efficacy and accuracy.