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AI models are using material from retracted scientific papers

AiNEWS2025 by AiNEWS2025
2025-09-23
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AI models are using material from retracted scientific papers
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“If [a tool is] facing the general public, then using retraction as a kind of quality indicator is very important,” says Yuanxi Fu, an information science researcher at the University of Illinois Urbana-Champaign. There’s “kind of an agreement that retracted papers have been struck off the record of science,” she says, “and the people who are outside of science—they should be warned that these are retracted papers.” OpenAI did not provide a response to a request for comment about the paper results.

The problem is not limited to ChatGPT. In June, MIT Technology Review tested AI tools specifically advertised for research work, such as Elicit, Ai2 ScholarQA (now part of the Allen Institute for Artificial Intelligence’s Asta tool), Perplexity, and Consensus, using questions based on the 21 retracted papers in Gu’s study. Elicit referenced five of the retracted papers in its answers, while Ai2 ScholarQA referenced 17, Perplexity 11, and Consensus 18—all without noting the retractions.

Some companies have since made moves to correct the issue. “Until recently, we didn’t have great retraction data in our search engine,” says Christian Salem, cofounder of Consensus. His company has now started using retraction data from a combination of sources, including publishers and data aggregators, independent web crawling, and Retraction Watch, which manually curates and maintains a database of retractions. In a test of the same papers in August, Consensus cited only five retracted papers. 

Elicit told MIT Technology Review that it removes retracted papers flagged by the scholarly research catalogue OpenAlex from its database and is “still working on aggregating sources of retractions.” Ai2 told us that its tool does not automatically detect or remove retracted papers currently. Perplexity said that it “[does] not ever claim to be 100% accurate.” 

However, relying on retraction databases may not be enough. Ivan Oransky, the cofounder of Retraction Watch, is careful not to describe it as a comprehensive database, saying that creating one would require more resources than anyone has: “The reason it’s resource intensive is because someone has to do it all by hand if you want it to be accurate.”

Further complicating the matter is that publishers don’t share a uniform approach to retraction notices. “Where things are retracted, they can be marked as such in very different ways,” says Caitlin Bakker from University of Regina, Canada, an expert in research and discovery tools. “Correction,” “expression of concern,” “erratum,” and “retracted” are among some labels publishers may add to research papers—and these labels can be added for many reasons, including concerns about the content, methodology, and data or the presence of conflicts of interest. 

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#models #material #retracted #scientific #papers

Tags: AIAI modelsAI scienceAi2 ScholarQAchatgptConsensusElicitPerplexityscientific research
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