Objection Overruled! Lay People can Distinguish Large Language Models from Lawyers, but still Favour Advice from an LLM

Large Language Models (LLMs) are seemingly infiltrating every domain, and the legal context is no exception. In this paper, we present the results of three experiments (total N=288) that investigated lay people's willingness to act upon, and their ability to discriminate between, LLM- and lawyer-generated legal advice. In Experiment 1, participants judged their willingness to act on legal advice when the source of the advice was either known or unknown. When the advice source was unknown, participants indicated that they were significantly more willing to act on the LLM-generated advice. This result was replicated in Experiment 2. Intriguingly, despite participants indicating higher willingness to act on LLM-generated advice in Experiments 1 and 2, participants discriminated between the LLM- and lawyer-generated texts significantly above chance-level in Experiment 3. Lastly, we discuss potential explanations and risks of our findings, limitations and future work, and the importance of language complexity and real-world comparability.

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Publication Details

Published
2025-04-24
DOI
https://doi.org/10.1145/3706598.3713470
Citations
8
Primary Topic
Artificial Intelligence in Law
Type
article
Field-Weighted Citation Impact
68.18

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article

Objection Overruled! Lay People can Distinguish Large Language Models from Lawyers, but still Favour Advice from an LLM

Eike Schneiders, Jérémie Clos, Natalie Leesakul, Richard Hyde et al.
8 citations
Artificial Intelligence in Law
68.18
article

Objection Overruled! Lay People can Distinguish Large Language Models from Lawyers, but still Favour Advice from an LLM

Eike Schneiders, Jérémie Clos, Natalie Leesakul, Richard Hyde, Tina Seabrooke, Joshua Krook, Joel E. Fischer
article en
8 citations

Abstract

Large Language Models (LLMs) are seemingly infiltrating every domain, and the legal context is no exception. In this paper, we present the results of three experiments (total N=288) that investigated lay people's willingness to act upon, and their ability to discriminate between, LLM- and lawyer-generated legal advice. In Experiment 1, participants judged their willingness to act on legal advice when the source of the advice was either known or unknown. When the advice source was unknown, participants indicated that they were significantly more willing to act on the LLM-generated advice. This result was replicated in Experiment 2. Intriguingly, despite participants indicating higher willingness to act on LLM-generated advice in Experiments 1 and 2, participants discriminated between the LLM- and lawyer-generated texts significantly above chance-level in Experiment 3. Lastly, we discuss potential explanations and risks of our findings, limitations and future work, and the importance of language complexity and real-world comparability.

University of Nottingham (GB), University of Antwerp (BE), University of Southampton (GB)
UK Research and Innovation, Engineering and Physical Sciences Research Council
Openalex Percentile: Top 0%
Artificial Intelligence in Law
68.18
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