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Organized scientific fraud is growing at an alarming rate, new study shows

From fabricated research to paid authorships and citations, organized scientific fraud is on the rise, according to a new study.

By combining large-scale data analysis of scientific literature with case studies, the researchers led a deep investigation into scientific fraud. Although concerns around scientific misconduct typically focus on lone individuals, this study instead uncovered sophisticated global networks of individuals and entities, which systematically work together to undermine the integrity of academic publishing.

The problem is so widespread that the publication of fraudulent science is outpacing the growth rate of legitimate scientific publications. The authors of the study argue these findings should serve as a wake-up call to the scientific community, which needs to act before the public loses confidence in the scientific process.

The study, "The entities enabling scientific fraud at scale are large, resilient and growing rapidly," was published in the Proceedings of the National Academy of Sciences.

Science must police itself better in order to preserve its integrity. If we do not create awareness around this problem, worse and worse behaviour will become normalized. At some point, it will be too late, and scientific literature will become completely poisoned. Some people worry that talking about this issue is attacking science. But this is really defending science from bad actors. We need to be aware of the seriousness of this problem and take measures to address it, say the researchers.

When people think about scientific fraud, they might remember news reports of retracted papers, falsified data or plagiarism. These reports typically center around the isolated actions of one individual, who takes shortcuts to get ahead in an increasingly competitive industry. But these researchers uncovered a widespread underground network operating within the shadows and outside of the public's awareness.

These networks are essentially criminal organizations, acting together to fake the process of science, the researchers reveal. Millions of dollars are involved in these processes.

To conduct the study, the researchers analyzed extensive datasets of retracted publications, editorial records and instances of image duplication.

Most of the data came from major aggregators of scientific literature, including Web of Science (WoS), Elsevier's Scopus, National Library of Medicine's PubMed/MEDLINE and OpenAlex, which includes data from Microsoft Academic Graph, Crossref, ORCID, Unpaywall and other institutional repositories.

The researchers also collected lists of de-indexed journals, which are scholarly journals that have been removed from databases for failing to meet certain quality or ethical standards.

The researchers also included data on retracted articles from Retraction Watch, article comments from PubPeer and metadata—such as editor names, submission dates and acceptance dates—from articles published in specific journals.

After analyzing the data, the team uncovered coordinated efforts involving "paper mills," brokers and infiltrated journals. Functioning much like factories, paper mills churn out large numbers of manuscripts, which they then sell to academics who want to quickly publish new work.

These manuscripts are mostly low quality—featuring fabricated data, manipulated or even stolen images, plagiarized content and sometimes nonsensical or physically impossible claims.

More and more people in the scientific world are being caught up in paper mills. Not only can they buy papers, but they can buy citations. Then, they can appear like well-reputed scientists when they have barely conducted their own research at all.

Paper mills operate by a variety of different models. So, we have only just been able to scratch the surface of how they operate. But they sell basically anything that can be used to launder a reputation. They often sell authorship slots for hundreds or even thousands of dollars. A person might pay more money for the first author position or less money for a fourth author position. People can also pay to get papers they have written automatically accepted in a journal through a sham peer-review process.

To identify more articles originating from paper mills, the research group launched a parallel project that automatically scans published materials science and engineering papers. The team specifically looked for authors who misidentified instruments they used in their research. A paper with those results was accepted by the journal PLOS ONE.

They found fraudulent networks use several key strategies:

Groups of researchers collude to publish papers across multiple journals. When their activities are discovered, the papers are subsequently retracted;
Brokers serve as intermediaries to enable mass publication of fraudulent papers in compromised journals;
Fraudulent activities are concentrated in specific, vulnerable subfields;
Organized entities evade quality-control measures, such as journal de-indexing.

Brokers connect all the different people behind the scenes, the researchers say. You need to find someone to write the paper. You need to find people willing to pay to be the authors. You need to find a journal where you can get it all published. And you need editors in that journal who will accept that paper.

Sometimes these organizations go around established journals altogether, searching instead for defunct journals to hijack. When a legitimate journal stops publishing, for example, bad actors can take over its name or website. These actors surreptitiously assume the journal's identity, lending credibility to its fraudulent publications, despite the actual publication being defunct.

This happened to the journal HIV Nursing, according to the authors of this paper. It was formerly the journal of a professional nursing organization in the U.K., then it stopped publishing, and its online domain lapsed. An organization bought the domain name and started publishing thousands of papers on subjects completely unrelated to nursing, all indexed in Scopus.

To combat this growing threat to legitimate scientific publishing, the authors emphasize the need for a multi-prong approach. This approach includes enhanced scrutiny of editorial processes, improved methods for detecting fabricated research, a greater understanding of the networks facilitating this misconduct and a radical restructuring of the system of incentives in science.

They also underscore the importance of addressing these issues before artificial intelligence (AI) infiltrates scientific literature more than it already has.

If we're not prepared to deal with the fraud that's already occurring, then we're certainly not prepared to deal with what generative AI can do to scientific literature, they said. We have no clue what's going to end up in the literature, what's going to be regarded as scientific fact and what's going to be used to train future AI models, which then will be used to write more papers.

It's distressing to see others engage in fraud and in misleading others. But if you think that science is useful and important for humanity, then you have to fight for it, the authors conclude.

The entities enabling scientific fraud at scale are large, resilient and growing rapidly, Proceedings of the National Academy of Sciences (2025). DOI: 10.1073/pnas.2420092122

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Replies to This Discussion

Permalink Reply by Dr. Krishna Kumari Challa on August 5, 2025 at 10:11am

AI-Enabled Paper Mills Flood Journals, Threaten Scientific Integrity

Amid growing concerns over academic misconduct, Northwestern University researchers uncovered sophisticated global networks collaborating to undermine scientific publishing, published in PNAS.
Amid rapid publication demands, researchers identified coordinated efforts by paper mills and brokers fueling fraud, outpacing legitimate scientific publications.
At PNAS, the study found 45 editors handled only 1.3% of articles but accounted for more than 30% of 702 retractions from 276,956 articles analyzed.
Preserving research integrity requires stronger oversight, and the authors emphasize enhanced scrutiny of editorial processes and improved detection methods as a wake-up call to the scientific community.
Richardson said, 'If we're not prepared to deal with the fraud that's already occurring, then we're certainly not prepared to deal with what generative AI can do to scientific literature.

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Permalink Reply by Dr. Krishna Kumari Challa on August 6, 2025 at 2:09pm

More scientific papers being written with help of ChatGPT—especially in computer science

Since its release in November of 2022, the use of ChatGPT and other large language models (LLMs) has proliferated throughout many disciplines, providing writing assistance for everything from speeches to contracts. So, it may not be surprising that some scientists might utilize ChatGPT to quicken the pace at which they publish their research.

There is little known about how the adoption of AI-generated content might affect the diversity, quality and reliability of research papers. And because these technologies are still new and constantly evolving, there is not yet a sure-fire way to detect the use of LLMs, and many institutions are still developing policies to curb their use.

To get a better grasp of how ChatGPT has been used in scientific writing over the last few years, a group of researchers recently conducted a study analyzing 1,121,912 scientific papers and preprints from arXiv, bioRxiv and Nature portfolio journals. The study, published in Nature Human Behaviour, used a new population-level framework based on word frequency shifts to estimate the increase of LLM-modified content between January 2020 and September 2024.

The study found that abstracts and introductions were most commonly affected, while methods and experiment sections showed less AI use, likely due to the summarization abilities of LLMs. A steady increase in the likely use of ChatGPT was observed across multiple topics of study, with the most dramatic being computer science—a notably AI-adjacent discipline.

The analysis showed likely LLM use in 22.5% of computer science abstracts and 19.5% of computer science introductions by September 2024. In November 2022, these numbers were only around 2.4% and similar across all article types at the time. LLM use was also relatively high in electrical engineering and systems science by 2024, at 18.0% for abstracts and 18.4% for introductions.

LLM usage was found to be much lower in areas like mathematics, with LLM usage at 7.7% for abstracts and 4.1% for introductions. The Nature portfolio of journals also showed a lower increase in AI use, with 8.9% for abstracts and 9.4% for introductions.

In addition to the field of study, the analysis was further stratified by author preprint frequency, paper length, and geographical region, in which the researchers found LLM modification to be more common in a few different cases. Authors who posted preprints more frequently were associated with more LLM usage in their papers, possibly due to increased pressure to put out more papers at a faster pace. Shorter papers—those less than 5,000 words—were also associated with more help from LLMs, along with those in more competitive research areas, like computer science.

Detecting AI-generated text in non-English speaking geographical regions is trickier, and some bias has been pointed out in previous methods of AI detection against non-native English writers in scientific papers. This study did show higher LLM usage in papers from China and Continental Europe, compared to North America and the UK, but much of this is likely for English-language assistance.

As the AI landscape rapidly evolves in the coming years, it has the potential to change how science is written and communicated, which in turn raises questions about transparency, originality and the future of scientific publishing.

The study authors point out many of the questions that should be answered as science continues to incorporate these technologies: "Our observations of the rise of generated or modified papers open many questions for future research. How do such papers compare in terms of accuracy, creativity or diversity? How do readers react to LLM-generated abstracts and introductions? How do citation patterns of LLM-generated papers compare with other papers in similar fields? How might the dominance of a limited number of for-profit organizations in the LLM industry affect the independence of scientific output?

Weixin Liang et al, Quantifying large language model usage in scientific papers, Nature Human Behaviour (2025). DOI: 10.1038/s41562-025-02273-8