Philippe Prince Tritto

Large Language Models (LLMs) are not merely tools to assist legal professionals—they represent a deeper epistemic and normative challenge to the foundations of legal authority. While LLMs allow humans to produce outputs that convincingly simulate legal reasoning, they lack the embodied judgment, ethical intentionality, and contextual awareness that define legitimate legal decision-making. This paper argues that the social legitimacy of the legal profession relies on capacities that are not reproducible through computational systems. We first examine the epistemological limitations of LLMs, drawing on Kantian philosophy and complexity theory to show that their outputs are simulations, not acts of understanding. We then analyze how this technological shift risks reducing legal professionals to jurists of the gaps – filling in only where machines fall short – thereby hollowing out the humanistic mission of law. Against this backdrop, we call for a renewed professional ethic centered on interpretation, creativity, and normative judgment, rather than technical supplementation. The automation of law is not the end of the profession, but it could be the end of its authority – unless its practitioners reclaim what cannot be outsourced.

This article explores the convergence between traditional legal education and emerging technological skills, addressing the educational metamorphosis facing law in the digital age. Through a comparative assessment of curricula and interviews of practicing jurists, it examines the transformations needed in the academic curriculum to prepare 21st century jurists. It highlights the tensions between tradition and innovation, highlighting the need to integrate advanced technological skills into legal education to ensure competitiveness and efficiency in professional practice. The study reveals a significant dichotomy among educational institutions: while some cling to traditional paradigms, others are moving towards an interdisciplinary education that combines law and technology. Resistance and benefits of the use of digital tools in legal practice are evaluated, underlining the importance of training jurists with a deep understanding of emerging technologies and their impact on the legal field.

Recent advances in legal language processing have highlighted limitations in correlation-based artificial intelligence approaches, prompting exploration of Causal Artificial Intelligence (AI) techniques for improved legal reasoning. This systematic review examines the challenges, limitations, and potential impact of Causal AI in legal language processing compared to traditional correlation-based methods. Following the Joanna Briggs Institute methodology, we analyzed 47 papers from 2017 to 2024 across academic databases, private sector publications, and policy documents, evaluating their contributions through a rigorous scoring framework assessing Causal AI implementation, legal relevance, interpretation capabilities, and methodological quality. Our findings reveal that while Causal AI frameworks demonstrate superior capability in capturing legal reasoning compared to correlation-based methods, significant challenges remain in handling legal uncertainty, computational scalability, and potential algorithmic bias. The scarcity of comprehensive real-world implementations and overemphasis on transformer architectures without causal reasoning capabilities represent critical gaps in current research. Future development requires balanced integration of AI innovation with law’s narrative functions, particularly focusing on scalable architectures for maintaining causal coherence while preserving interpretability in legal analysis.