False authorship: an explorative case study around an AI-generated article published under my name

BACKGROUND: The proliferation of generative artificial intelligence (AI) has facilitated the creation and publication of fraudulent scientific articles, often in predatory journals. This study investigates the extent of AI-generated content in the Global International Journal of Innovative Research (GIJIR), where a fabricated article was falsely attributed to me. METHODS: The entire GIJIR website was crawled to collect article PDFs and metadata. Automated scripts were used to extract the number of probable in-text citations, DOIs, affiliations, and contact emails. A heuristic based on the number of in-text citations was employed to identify the probability of AI-generated content. A subset of articles was manually reviewed for AI indicators such as formulaic writing and missing empirical data. Turnitin's AI detection tool was used as an additional indicator. The extracted data were compiled into a structured dataset, which was analyzed to examine human-authored and AI-generated articles. RESULTS: Of the 53 examined articles with the fewest in-text citations, at least 48 appeared to be AI-generated, while five showed signs of human involvement. Turnitin's AI detection scores confirmed high probabilities of AI-generated content in most cases, with scores reaching 100% for multiple papers. The analysis also revealed fraudulent authorship attribution, with AI-generated articles falsely assigned to researchers from prestigious institutions. The journal appears to use AI-generated content both to inflate its standing through misattributed papers and to attract authors aiming to inflate their publication record. CONCLUSIONS: The findings highlight the risks posed by AI-generated and misattributed research articles, which threaten the credibility of academic publishing. Ways to mitigate these issues include strengthening identity verification mechanisms for DOIs and ORCIDs, enhancing AI detection methods, and reforming research assessment practices. Without effective countermeasures, the unchecked growth of AI-generated content in scientific literature could severely undermine trust in scholarly communication.