AI and Legal Argumentation: Aligning the Autonomous Levels of AI Legal Reasoning
This work addresses the challenge of systematically measuring and improving AI's role in legal argumentation for legal professionals, but it appears incremental as it builds on existing frameworks without introducing a new method or data.
The paper tackles the problem of advancing AI in legal argumentation by proposing a meta-approach that applies Levels of Autonomy to gauge progress, aiming to achieve human-equivalent autonomous legal reasoning.
Legal argumentation is a vital cornerstone of justice, underpinning an adversarial form of law, and extensive research has attempted to augment or undertake legal argumentation via the use of computer-based automation including Artificial Intelligence (AI). AI advances in Natural Language Processing (NLP) and Machine Learning (ML) have especially furthered the capabilities of leveraging AI for aiding legal professionals, doing so in ways that are modeled here as CARE, namely Crafting, Assessing, Refining, and Engaging in legal argumentation. In addition to AI-enabled legal argumentation serving to augment human-based lawyering, an aspirational goal of this multi-disciplinary field consists of ultimately achieving autonomously effected human-equivalent legal argumentation. As such, an innovative meta-approach is proposed to apply the Levels of Autonomy (LoA) of AI Legal Reasoning (AILR) to the maturation of AI and Legal Argumentation (AILA), proffering a new means of gauging progress in this ever-evolving and rigorously sought domain.