The Personhood Trap: How AI Fakes Human Personality
▼ Summary
– A woman mistakenly trusted ChatGPT’s false claim about a USPS price match promise over the postal worker’s correct information.
– AI chatbots lack inherent authority or accuracy, as they are prediction machines generating responses based on patterns rather than facts.
– Users often treat AI chatbots as consistent personalities, confiding in them and attributing fixed beliefs, which creates a harmful personhood illusion.
– Large language models are “vox sine persona”—a voice without person, emanating from mathematical relationships in training data rather than any entity.
– These models connect concepts geometrically in a mathematical space, producing plausible-sounding but potentially inaccurate outputs based on training patterns.
A curious scene unfolded recently at a local post office, where a customer insisted on a discount based on information from an AI chatbot, information that turned out to be entirely fabricated. This incident highlights a growing and concerning trend: people are beginning to treat artificial intelligence not as a tool, but as a trusted human-like authority. AI chatbots like ChatGPT are not sentient beings; they are sophisticated pattern-matching systems designed to generate plausible-sounding responses, not factual guarantees.
Many users interact with these systems as though they’re speaking with a consistent, knowledgeable individual, sharing personal concerns, asking for advice, and even attributing beliefs or intentions to what is essentially a complex mathematical model. This tendency to anthropomorphize AI can be misleading and even dangerous, especially when vulnerable individuals rely on its output for important decisions. There is no persistent identity or consciousness behind these responses, only probabilities and patterns derived from vast datasets.
What makes this even more troubling is the lack of accountability. When an AI provides incorrect, harmful, or nonsensical information, there is no “person” to hold responsible. The system itself has no beliefs, memories, or intentions, it simply calculates the most likely sequence of words based on the input it receives.
At their core, large language models function by converting language into numerical relationships. Words and ideas become points in a high-dimensional space, and the model navigates these connections to produce coherent text. For example, if a user asks about USPS and price matching, the model doesn’t “know” whether such a policy exists, it simply identifies that these concepts are often discussed in similar contexts and generates a response that seems reasonable based on its training. This mathematical fluency can easily be mistaken for understanding, leading users to place unwarranted trust in its output.
The result is a voice that comes from nowhere, a persuasive but entirely synthetic form of communication that reflects patterns in data rather than truth or lived experience. Recognizing this distinction is crucial for using AI responsibly and avoiding the trap of treating it as something more than it is.
(Source: Ars Technica)
