Banal Deception Human-AI Ecosystems: A Study of People's Perceptions of LLM-generated Deceptive Behaviour

Zhan, X, Xu, Y, Abdi, N ORCID: 0000-0002-4613-6443, Collenette, J and Sarkadi, S Banal Deception Human-AI Ecosystems: A Study of People's Perceptions of LLM-generated Deceptive Behaviour. Journal of Artificial Intelligence research, 84. ISSN 1076 - 9757 (Accepted)

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Abstract

Large language models (LLMs) can provide users with false, inaccurate, or mis leading information, and we consider the output of this type information as what Natale (2021) calls ‘banal’ deceptive behaviour. Here, we investigate peoples’ per ceptions of ChatGPT-generated deceptive behaviour and how this affects peoples’ own behaviour and trust. To do this, we use a mixed-methods approach compris ing of (i) an online survey with 220 participants and (ii) semi-structured interviews with 12 participants. Our results show that (i) the most common types of deceptive information encountered were over-simplifications and outdated information; (ii) humans’ perceptions of trust and ‘worthiness’ of talking to ChatGPT are impacted by ‘banal’ deceptive behaviour; (iii) the perceived responsibility for deception is influenced by education level and the frequency of deceptive information; and (iv) users become more cautious after encountering deceptive information, but they come to trust the technology more when they identify advantages of using it. Our findings contribute to the understanding of human-AI interaction dynamics in the context of Deceptive AI Ecosystems, and highlight the importance of user-centric approaches to mitigating the potential harms of deceptive AI technologies.

Item Type:	Article
Uncontrolled Keywords:	0102 Applied Mathematics; 0801 Artificial Intelligence and Image Processing; 1702 Cognitive Sciences; Artificial Intelligence & Image Processing; 4602 Artificial intelligence; 4603 Computer vision and multimedia computation; 4611 Machine learning
Subjects:	B Philosophy. Psychology. Religion > BF Psychology Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Computer Science and Mathematics
Publisher:	AI Access Foundation
Date of acceptance:	3 July 2025
Date of first compliant Open Access:	7 October 2025
Date Deposited:	07 Oct 2025 13:27
Last Modified:	07 Oct 2025 13:30
Editors:	Walsh, T
URI:	https://researchonline.ljmu.ac.uk/id/eprint/27288

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