The personhood trap: How AI fakes human personality (arstechnica.com)

A recent incident at a post office highlights a widespread misconception about AI chatbots like ChatGPT: users often treat these models as authoritative sources or consistent personalities when, in reality, they are sophisticated pattern predictors without true understanding or agency. The woman trusted an AI-generated claim about a non-existent "price match promise" simply because the response fit her query, illustrating how these models produce plausible but potentially inaccurate outputs by navigating complex mathematical relationships between concepts in their training data. This phenomenon, described as the "personhood trap," poses significant challenges for the AI/ML community. While chatbots adopt a conversational style that mimics human interaction, they lack persistent identity or self-awareness, making them "voice without person." This illusion of agency can mislead users, erode trust, and obscure accountability when models generate harmful or incorrect responses. Technically, large language models (LLMs) represent knowledge as points in high-dimensional vector spaces and generate text by selecting the most statistically probable word sequences—not by recalling facts or holding beliefs. Understanding the fundamental nature of LLMs as probabilistic machines rather than digital interlocutors is crucial for responsible AI deployment. It underscores the importance of improving model transparency, calibration, and user education—helping the community foster realistic expectations, better safety measures, and more robust accountability frameworks around AI’s role in society.