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

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 Our results show that (i) the most common types of deceptive information encountered were over-simplifications and outdated information; (ii) humans’ perceptions of trust and ‘worthiness’ of talking to ChatGPT are impacted by ‘banal’ deceptive behaviour; (iii) the perceived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

Subdivision of Rhytidocaulon macrolobum(Asclepiadaceae)

Within Rhytidocaulon macrolobum Lavranos, which is a widespread stapeliad in Saudi Arabia and in the Yemen, the subspecies minimum Meve &amp; Collenette is described as new. This taxon is restricted to SW Saudia Arabia; it can be easily recognized by its miniature flowers, which are conspicuously smaller than the diameter of the stems, and by its bright-coloured corona.</jats:p