  Large language models (LLMs) can provide users with false, inaccurate, or
misleading information, and we consider the output of this type of information
as what Natale (2021) calls `banal' deceptive behaviour. Here, we investigate
peoples' perceptions of ChatGPT-generated deceptive behaviour and how this
affects peoples' own behaviour and trust. To do this, we use a mixed-methods
approach comprising 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over-simplifications and outdated information; (ii) humans' perceptions of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 \textit{Deceptive AI Ecosystems}, and highlight the importance
of user-centric approaches to mitigating the potential harms of deceptive AI
technologies.
