mlsanityData to 7 Oct 2026

It Cannot Be Right If It Was Written by AI: On Lawyers' Preferences of Documents Perceived as Authored by an LLM vs a Human

arXiv 2407.06798 ↗First posted 9 Jul 2024cs.HC cs.AI cs.CYcited by 5

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Abstract (arXiv)

Large Language Models (LLMs) enable a future in which certain types of legal documents may be generated automatically. This has a great potential to streamline legal processes, lower the cost of legal services, and dramatically increase access to justice. While many researchers focus on proposing and evaluating LLM-based applications supporting tasks in the legal domain, there is a notable lack of investigations into how legal professionals perceive content if they believe an LLM has generated it. Yet, this is a critical point as over-reliance or unfounded scepticism may influence whether such documents bring about appropriate legal consequences. This study is the necessary analysis of the ongoing transition towards mature generative AI systems. Specifically, we examined whether the perception of legal documents' by lawyers and law students (n=75) varies based on their assumed origin (human-crafted vs AI-generated). The participants evaluated the documents, focusing on their correctness and language quality. Our analysis revealed a clear preference for documents perceived as crafted by a human over those believed to be generated by AI. At the same time, most participants expect the future in which documents will be generated automatically. These findings could be leveraged by legal practitioners, policymakers, and legislators to implement and adopt legal document generation technology responsibly and to fuel the necessary discussions on how legal processes should be updated to reflect recent technological developments.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 52 of its 112 references that are in the map. First posted on arXiv 9 Jul 2024.

Builds on

52 of 112 references.

References · 52

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 1.

5 papers.

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  1. 2026Ordinary, Reasonable Chatbots: Do AI Models Track Human Legal Judgments?
  2. 2026Reliability by design: quantifying and eliminating fabrication risk in LLMs. From generative to consultative AI: a comparative analysis in the legal domain and lessons for high-stakes knowledge bases
  3. 2026Bridging Legal Interpretation and Formal Logic: Faithfulness, Assumption, and the Future of AI Legal Reasoning
  4. 2026LLM Agents in Law: Taxonomy, Applications, and Challenges
  5. 2025Artificial Intelligence and Legal Analysis: Implications for Legal Education and the Profession
  6. 2026Statutory AI: Aligning Large Language Models With Legal Norms
  7. 2023Exploring the psychology of LLMs' Moral and Legal Reasoning
  8. 2023Modeling Legal Reasoning: LM Annotation at the Edge of Human Agreement