A growing number of AI tools can detect fraudulent elements in papers, but they can be expensive to use. Such tools are probably better deployed by journal publishers rather than individual reviewers, says Elisabeth Bik, a science-integrity consultant in San Francisco, California, especially because feeding unpublished content into AI tools can compromise confidentiality and is generally frowned on during peer review.
The study was inspired by anecdotes from authors who cited articles only because reviewers asked them to, says study author Adrian Barnett, who researches peer review and metaresearch at Queensland University of Technology in Brisbane, Australia. Sometimes, these requests are fine, he says. But if reviewers ask for too many citations or the reason to cite their work is not justified, the peer-review process can become transactional, says Barnett. Citations increase a researcher's h-index, a metric reflecting the impact of their publications.
