Alan Said AI media incident — Nov 2026
A predatory publishing ring used generative AI to create low-quality papers and falsely attributed them to prominent computer scientists to manufacture.
- Incident date
- Nov 13, 2026
- Source report date
- Nov 14, 2026
- Target
- Alan Said, Fei-Fei Li, Christopher Manning, Michael I. Jordan, and Stuart Russell
On November 13, 2026, investigations revealed a coordinated network of predatory open-access journals publishing AI-generated content falsely attributed to renowned academics. The scheme exploited automated indexing services to attach these fabricated papers to the public bibliographies of unsuspecting researchers, creating a deceptive facade of academic prestige.
What happened
The fraud was uncovered when Alan Said, an associate professor at the University of Gothenburg, received an automated Google Scholar notification for a paper titled Intelligence Systems Supporting Sustainable Global Development. Said had never written or submitted the work, which he described as vacuous, computer-generated prose filled with hallucinated citations. Further investigation identified dozens of similar papers falsely listing prominent computer scientists as authors, including Fei-Fei Li, Christopher Manning, Michael I. Jordan, and Stuart Russell.
The fraudulent papers were hosted by a cluster of journals, including the American International Journal of Research and Innovation (AIJRI), the American Journal of Advanced Multidisciplinary Research and Innovation (AJAMRI), and the American Journal For Multidisciplinary Research (AJFMR). Although these sites claimed to be international peer-reviewed journals, they were newly registered in 2026 and featured fabricated back-archives. The websites shared identical layouts, a common WhatsApp contact number, and overlapping editorial boards. The operation functioned on an author-fee model, charging approximately $200 in article processing charges.
When researchers attempted to report the impersonation, the journal operators often refused to retract the content, instead cycling through different names to credit other scholars for the same AI-generated text. This tactic exploits the speed of automated indexing, which often populates researcher profiles before formal investigations can occur. The network’s use of high-profile names serves to trap early-career researchers and graduate students into submitting genuine work to these predatory venues, resulting in the loss of both publication fees and intellectual property.