A narrative emerged around an artificial intelligence chatbot, ChatGPT, claiming to recount an incident involving American law professor Jonathan Turley. The tale circulated through a Washington Post edition, presenting a scenario in which the professor allegedly made inappropriate remarks toward a student during a class trip and attempted physical contact. The account cited by the neural network referenced a Washington Post article supposedly published in March of 2018 as its source.
According to the story, Turley learned from a colleague, a lawyer based in California, that ChatGPT had fabricated such an episode involving him. In the course of a controlled experiment, the professor’s associate asked the AI to list lawyers who had faced harassment allegations, and Turley appeared among the names the system produced.
“The whole thing was unsettling,” remarked Turley. “Fabrications of this kind can seriously damage a person’s reputation.” The professor did not simply react to the rumor; he directly contacted The Washington Post and stated that the allegations were untrue, noting that he had never traveled to Alaska and certainly had not harassed any student. The publication, after review, maintained its stance that the cited article did not exist, and the story tied to the AI input did not originate from a published piece.
Additional commentary in the coverage noted that a widely used Android keyboard, SwiftKey, had, at one point, been mentioned in connection with ChatGPT in various discussions. This reference appeared to be secondary in nature, linked to broader conversations about AI tools and their integration with mobile devices, rather than offering independent substantiation of the central claim about Turley.
What this sequence underscores is the growing complexity of AI-generated content in which automated narratives can blend speculative material with real-world references. The incident highlights the importance of source verification when dealing with machine-produced statements that touch on reputational matters and professional conduct. It also raises questions about how AI systems parse and synthesize public records, journalistic reports, and rumor, and how readers should approach such outputs with a critical eye.
Experts emphasize that AI models, though powerful in generating plausible text, do not inherently possess discernment about truth and accuracy. They rely on patterns learned from vast amounts of data, including headlines, summaries, and articles, which means they can echo misinformation if not properly constrained by verified sources. This case illustrates the necessity for corroboration from primary sources and authoritative outlets before accepting AI-generated narratives as fact.
From a media ethics standpoint, the episode invites reflection on how journalism and technology intersect. Newsrooms may need clearer guidelines for distinguishing between human-authored reporting and machine-generated content that cites or mimics credible outlets. Readers are reminded to consider the provenance of the claims, look for corroborating documentation, and be cautious about shared summaries that may omit important context. The eventual takeaway is not to distrust AI outright but to demand rigorous verification when AI outputs touch upon professional reputations or legal matters.