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

TL;DR

This study explores how people perceive and react to deceptive behaviors of large language models like ChatGPT, revealing impacts on trust, responsibility, and user caution in human-AI interactions.

Contribution

It provides empirical insights into user perceptions of 'banal' deception in LLMs and highlights factors influencing trust and responsibility in human-AI ecosystems.

Findings

Most deceptive info was oversimplifications and outdated data.

Perceptions of trust are affected by deceptive behavior.

Users become more cautious but also more trusting when recognizing benefits.

Abstract

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 Our results show that (i) the most common types of deceptive information encountered were over-simplifications and outdated information; (ii) humans' perceptions of trust and `worthiness' of talking to ChatGPT are impacted by `banal' deceptive behaviour; (iii) the perceived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