This study analyzes how ChatGPT portrays and describes itself, revealing misleading myths about AI technologies, specifically conversational agents based on large language models. This analysis allows for critical reflection on the potential harm these misconceptions may pose for public understanding of AI and related technologies. While previous research has explored AI discourses and representations more generally, few studies focus specifically on AI chatbots. To narrow this research gap, an experimental-qualitative investigation into auto-generated AI representations based on prompting was conducted. Over (...) the course of a month, ChatGPT (both in its GPT-4 and GPT-4o models) was prompted to “Draw an image of yourself,” “Represent yourself visually,” and “Envision yourself visually.” The resulting data (n = 50 images and 58 texts) was subjected to a critical exploratory visual semiotic analysis to identify recurring themes and tendencies in how ChatGPT is represented and characterized. Three themes emerged from the analysis: anthropomorphism, futuristic/futurism and (social)intelligence. Importantly, compared to broader AI imaginations, the findings emphasize ChatGPT as a friendly AI assistant. These results raise critical questions about trust in these systems, not only in terms of their capability to produce reliable information and handle personal data, but also in terms of human–computer relations. (shrink)

Modern AI systems based on deep learning are neither traditional tools nor full-blown agents. Rather, they are characterised by idiosyncratic agential profiles, i.e., combinations of agency-relevant properties. Modern AI systems lack superficial features which enable people to recognise agents but possess sophisticated information processing capabilities which can undermine human goals. I argue that systems fitting this description, when they are adversarial with respect to human users, pose particular risks to those users. To explicate my argument, I provide conditions under which (...) agential profiles are explanatorily relevant to harms caused. I then contend that the role of recommender systems in producing harmful outcomes like digital addiction satisfies these conditions. (shrink)