False Authorship Surges Through Predatory Journals, AI
In a News and Perspectives piece, JMIR correspondent Cliff Dominy reports that fraudulent papers and false authorship are increasing across scientific publishing. The article highlights recent cases involving predatory journals and AI.
What happened
The Journal of Medical Internet Research (JMIR) published a News & Perspectives piece by correspondent Cliff Dominy reporting that fraudulent papers and false authorship are rising across scientific publishing, with generative AI now facilitating fabrication at scale. The article documents cases in which AI-generated articles have appeared in predatory journals under the names of real researchers who never submitted or authored the work.
What the pattern looks like
Advances in generative AI allow scammers to produce plausible-looking manuscripts, craft personalized author invitations that reference a researcher's prior publications and conference history, and mimic the style of legitimate journals. Predatory publishers then issue digital object identifiers (DOIs) and list fabricated author names, making the false papers discoverable in citation databases. A documented case study, published separately in Research Integrity and Peer Review (Springer Nature, 2025), describes a researcher who discovered an AI-generated article published under her name with a valid DOI despite never having submitted the work.
Why it matters for AI and data practitioners
Training datasets built from crawled scientific literature may inadvertently include AI-fabricated papers indexed by predatory outlets. Downstream models fine-tuned on such corpora can absorb synthetic claims presented as peer-reviewed findings, eroding the reliability of knowledge grounding. Practitioners assembling domain corpora - particularly in health, science, and policy - should audit sources for predatory journal indicators before including them in training or retrieval pipelines.
What to watch
Monitor whether major indexing bodies (Scopus, PubMed, Google Scholar) strengthen predatory-journal filters in response to AI-assisted fabrication. Track whether academic institutions and publishers introduce provenance or authorship-verification tooling to make false attribution harder to sustain at scale.
Key Points
- 1WHAT: Fraudulent papers and false authorship are increasing across scientific publishing outlets.
- 2WHY: Predatory journals and AI are implicated in the recent cases reported.
- 3SO WHAT: For practitioners, research integrity, authorship verification, and editorial screening face rising challenges.
Scoring Rationale
A relevant integrity-risk story for AI/DS practitioners building scientific literature corpora: AI-facilitated false authorship in predatory journals poses a training-data contamination risk. The primary JMIR piece is a news/perspectives item rather than a primary research study, and the threat is already well-documented in prior literature. Score reflects practical relevance to practitioners doing corpus curation without overstating novelty.
Sources
Primary source and supporting public references used for this report.
Practice with real Ad Tech data
90 SQL & Python problems · 15 industry datasets
250 free problems · No credit card
See all Ad Tech problems
