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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
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
9da80af0-1e27-4454-90e2-eb1abf7108bd
Seabrooke, Tina
bf0d9ea5-8cf7-494b-9707-891762fce6c3
Krook, Joshua
d6faf163-a9d3-40b1-b02f-b2913859da81
Hyde, Richard
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Leesakul, Natalie
953c9cf3-ac2f-4d8c-9ed7-da47f356626b
Clos, Jeremie
398fea21-4dc6-42ee-a2f1-cad9a789b378
Fischer, Joel E
ef3a4021-58e4-4df2-95c0-a145278c4746

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
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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
ORCID for Eike Schneiders: ORCID iD orcid.org/0000-0002-8372-1684
ORCID for Tina Seabrooke: ORCID iD orcid.org/0000-0002-4119-8389

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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 ORCID iD
Author: Tina Seabrooke ORCID iD
Author: Joshua Krook
Author: Richard Hyde
Author: Natalie Leesakul
Author: Jeremie Clos
Author: Joel E Fischer

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