Thinking Machines, Thinking Lawyers
As AI’s capabilities enhance and it gradually seeps into the legal field, what happens to how lawyers think and write? This symposium aims to initiate a conversation around many such unanswered questions and explores epistemic justice, research integrity, institutional and regulatory responses, and the comparative and archival dimensions of knowledge production in the age of AI.
One of the first things a bleary-eyed first-year law student has to hear ad nauseam in their first week of law school is that, over the next three years, they will be taught to “think” like a lawyer. What does that mean exactly? And how is that likely to change in the face of rapid adoption and spread of Large Language Models (LLMs) across law schools, law firms, and in-house legal departments? What do we lose when lawyers let LLMs do their writing for them?
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Computational methods may facilitate a form of estrangement valuable to qualitative and, in particular, ethnographic inquiry: they can help make the familiar strange. Machines may be most useful precisely when they fail to reproduce our categories; they may be less informative when they appear simply to confirm them. Despite their prima facie opposite inclinations, the two approaches can therefore produce considerable synergies.
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There are many stakeholders in the information economy. For decades, creators, workers, market intermediaries, media companies, and digital platforms have tussled over how information, articulated in different forms such as “works”, “content”, or “knowledge”, can be monetised and who should benefit. More recently, AI firms have entered this contest over how to define and justify the forms of profit that can be drawn off information as it flows from creators to consumers via intermediaries, platforms, and AI models.
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The impact of AI on research has already led to much reflection in general and, more recently, on legal scholarship in particular, including first attempts to measure how far the practice has spread, and to examine what happens when generative systems interpose themselves between readers and law review articles. On a similar note, we have recently argued that the way researchers build their scholarship is itself under threat, and that the response required is institutional. In this blogpost, we extend that argument to legal research, where it acquires a further consequence.
Continue reading >>AI is being widely integrated into legal practice and academic research. Early concerns about such tools were focused on the outputs: hallucinated citations, fabricated case law, overall sloppiness among attorneys and researchers submitting AI-generated work without review. These problems have not disappeared but have received enough attention to warrant continuous improvement of predictive and generative tools for accuracy. However, the fabricated footnotes distract from the fact that we are substituting the use of these tools for the manual processes of conducting our own research, exercising critical thinking, and engaging in reasoned judgment processes.
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The legal academy, and perhaps many other fields, is at a curious juncture. We have largely accepted AI as a tool for refinement—for polishing language, organising footnotes, checking citations, and formatting documents to meet journal specifications. Yet we have drawn a bright line. AI must not be used for ideation, argument development, and initial writing. One can test arguments with AI-based platforms, probe for weaknesses, and refine logic through dialogue with these systems. However, the generation of core ideas and legal reasoning must remain human, and writing must remain distinctly personal. But is this the right standard to adopt?
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Our results suggest that LLMs’ usefulness for comparative constitutional data collection depends on what researchers ask them to code. They may be able to produce datasets that simply code constitutional facts, but current versions of LLMs do not perform well when asked to create datasets that require interpreting abstract constitutional concepts or reasoning about law. However, since the models make errors in systematic ways, researchers should always be cautious about treating LLM-generated data as interchangeable with expert-coded data.
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The spread of AI systems appears increasingly central to the evolution of work and professions, and lawyers are not exempt. However, what remains rare, almost everywhere, is a broader strategy taken at the institutional level about the mission of academic institutions in the age of AI. Particularly, despite some exceptions, universities have acknowledged the relevance of AI, but legal education, particularly in Europe, is largely public, framed by national curricular requirements, and followed by a separate stage of professional training through bar schools and traineeships
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The legal traditions of Indigenous Peoples and of much of the non-Western world are characteristically oral, relational, etched in carvings, seasonal, performative, and place-based. Their normative content lives in narrative, genealogy, ceremony, and the deliberations of communities; their authority is constituted through relationships between people, land, ancestors, and the non-human world. An AI system built to learn from text does not merely fail to locate such law. It is architecturally incapable of recognising legal knowledge in the forms in which that law exists.
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Responding to the influx of AI into scholarly writing, in the legal domain and elsewhere, poses real challenges. It will be neither possible nor appropriate to develop a set of determinative rules covering all possible uses. Ethical frameworks do not operate in this way. Instead, they should be principles-based, allowing them to accommodate both the evolution of the technology and developments in scholarly practice taking place in the light of shared commitments to research integrity.
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This symposium explores the relationship between AI and the production and delivery of legal knowledge. It also interrogates how emerging AI-based capabilities are shaping the way lawyers think, research, and act. It does not claim to present perfect answers, but is an attempt to initiate conversations on the multifaceted changes posed by AI-assisted capabilities to our traditional ways of doing legal research and teaching.
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An invitation.
732 regular supporters. We need 1,000 by mid-January. Since 19 October: €6,831 of €120,000.
