AI research papers are getting better, and it’s a big problem for scientists
AI-generated research papers are now sophisticated enough to evade detection systems, overwhelming peer review processes and scientific publishing with low-quality but plausible studies that exploit public datasets. This crisis threatens the integrity of the research system and academic credibility metrics, creating significant operational and strategic risks for organizations that rely on published research for decision-making and competitive advantage. CIOs and technology leaders must address the dual challenge of how their own organizations validate research inputs and how enterprise systems that consume academic data will maintain reliability in an era of scientific fraud at scale.
Last summer, Peter Degen's postdoctoral supervisor came to him with an unusual problem: One of his papers was being cited too much. Citations are the currency of academia, but there was something unusual about these. Published in 2017, the paper had assessed the accuracy of a particular type of statistical analysis on epidemiological data and had received a respectable few dozen citations in other research papers over the years, but now it was being referenced every few days, hundreds of times, placing it among the most cited papers of his career. Another professor might be thrilled. Degen's adviser asked him to investigate. Degen, a postd … Read the full story at The Verge.