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Training data, transparency, and teaching law. AI literacy, professional ethics and legal education
Lambert, Steve
Lambert, Steve
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2026-05-26
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- Embargoed until 2226-05-31
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Abstract
This chapter examines how ongoing generative-AI copyright litigation reframes legal education’s obligations around AI literacy, professional responsibility, and access to justice. Using court filings and rulings as a live case study, the chapter maps issues (copyright subsistence, fair dealing/fair use analogues, derivative works, database rights) to curricular choices in research, writing, clinics, and assessment. It proposes a practice-ready model for “human-in-the-loop” pedagogy for any AI-assisted student work and aligns with emerging judicial/administrative expectations for explainability and accountability. The analysis is comparative: a short cross-reference to The New York Times Company v. Microsoft/OpenAI illustrates how differing procedural postures and remedies travel back into the classroom, shaping student competencies in prompt design, provenance verification, and ethical supervision. The chapter concludes with a governance checklist for law schools that sustains equity and inclusion while meeting rapidly evolving professional norms.
Citation
Lambert, S. (2026 - forthcoming). Training data, transparency, and teaching law. AI literacy, professional ethics and legal education. In V. Wang (Ed.), AI, Ethics, and the Future of Legal Education: Critical Perspectives and Global Reforms. IGI Global Scientific Publishing.
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IGI Global Scientific Publishing
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Book chapter
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This chapter/paper appears in <AI, Ethics, and the Future of Legal Education: Critical Perspectives and Global Reforms> edited/authored by <Viktor Wang> Copyright [2026], IGI Global, www.igi-global.com. Posted by permission of the publisher.
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9798337368436