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University of Miami Law Review

Abstract

Two case studies involving motorcycle-accident negligence and administrative law in the context of drone regulation violations explore the transformative potential of generative AI in legal practice. Four AI engines—DeepSeek, Claude, ChatGPT, and Grok—were asked to analyze transcripts of client interviews by identifying legal claims and assessing their strengths and weaknesses.

The results demonstrate that current AI technology effectively processes natural-language client interviews, identifies viable legal theories, and assesses claim strengths and weaknesses in both tort and administrative law contexts. The AI systems demonstrated proficiency in parsing legal theories and citing relevant statutes and administrative regulations, though case citations were frequently inaccurate due to limited access to comprehensive legal databases.

The technology shows promise for junior attorneys who may lack experience in systematic case analysis, though human oversight remains essential for accuracy and ethical compliance. Best practices include anonymizing client data, cross-validating results across multiple platforms, and maintaining rigorous quality control. The findings support a balanced approach: embracing AI’s analytical capabilities while preserving professional judgment and ethical standards.

Misconceptions about AI, such as exaggerated fears of job displacement, concerns about ill-defined “bias,” and vague demands for regulation, should not obscure AI’s potential as a valuable “wingman” rather than a substitute for legal expertise. While AI is unlikely to replace lawyers or originate novel legal theories, it excels in document summarization, brainstorming, and claim identification. Used prudently and cross-validated across platforms, generative AI emerges not as a threat, but as a powerful, practical tool for augmenting modern legal work.

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