Why AI Hallucinations Are a Distinct Kind of Misinformation

The Harvard Kennedy School Misinformation Review publishes peer-reviewed commentary on how false information spreads, and this piece by Anqi Shao of the University of Wisconsin-Madison tackles a question that keeps coming up wherever people argue about whether AI is "lying": are hallucinations even the same kind of problem as ordinary misinformation?

Shao opens with a concrete case: in February 2025, Google's AI Overview presented an April Fool's satire about "microscopic bees powering computers" as verified fact. No one at Google intended to mislead anyone, yet the system produced a confident falsehood anyway. From there, the piece argues that hallucinations from large language models deserve their own conceptual treatment, since they come from probabilistic next-token prediction rather than from a person's mistake or motive to deceive.

The framework it proposes splits the problem into supply and demand. On the supply side, it traces hallucinations to four vulnerabilities: the boundaries of a model's training knowledge, how data is logistically assembled, the opacity of the generation process, and shortfalls in the checks meant to catch bad output before it reaches a user. On the demand side, it works through a model of institutional credibility, group-level spread, and individual trust and literacy.

This is a conceptual commentary, not an empirical study — Shao is proposing a framework for future research to test, not reporting new data. It's a useful read for anyone trying to think more precisely about what a hallucination actually is before jumping to "the AI lied."