Objection overruled! Lay people can distinguish large language models from lawyers, but still favour advice from an LLM
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.
Computer Science - Human-Computer Interaction, Computer Science - Computers and Society
Association for Computing Machinery
Schneiders, Eike
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Seabrooke, Tina
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Krook, Joshua
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Hyde, Richard
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Leesakul, Natalie
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Clos, Jeremie
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Fischer, Joel E
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Schneiders, Eike
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Seabrooke, Tina
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Krook, Joshua
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Hyde, Richard
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Leesakul, Natalie
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Clos, Jeremie
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Fischer, Joel E
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Schneiders, Eike, Seabrooke, Tina, Krook, Joshua, Hyde, Richard, Leesakul, Natalie, Clos, Jeremie and Fischer, Joel E
(2025)
Objection overruled! Lay people can distinguish large language models from lawyers, but still favour advice from an LLM.
In CHI 2025 - Proceedings of the 2025 CHI Conference on Human Factors in Computing Sytems.
Association for Computing Machinery.
14 pp
.
(In Press)
(doi:10.1145/3706598.3713470).
Record type:
Conference or Workshop Item
(Paper)
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.
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Objection Overruled - CHI 2025
More information
In preparation date: 1 September 2024
Accepted/In Press date: 17 January 2025
Venue - Dates:
2025 CHI Conference on Human Factors in Computing Systems, CHI 2025, , Yokohama, Japan, 2025-04-26 - 2025-05-01
Keywords:
Computer Science - Human-Computer Interaction, Computer Science - Computers and Society
Identifiers
Local EPrints ID: 494609
URI: http://eprints.soton.ac.uk/id/eprint/494609
PURE UUID: 99de1e47-bce4-4752-b869-664acb02f14e
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Date deposited: 10 Oct 2024 17:05
Last modified: 20 May 2025 02:17
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Contributors
Author:
Eike Schneiders
Author:
Joshua Krook
Author:
Richard Hyde
Author:
Natalie Leesakul
Author:
Jeremie Clos
Author:
Joel E Fischer
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