Machine-learning tool needs to be more accurate before it can replace or aid human assessment in the UK Research Excellence Framework.
of journal articles submitted to the United Kingdom’s Research Excellence Framework say the system is not yet accurate enough to aid human assessment, and recommend further testing in a large-scale pilot scheme., show that the AI system generated identical scores to human peer reviewers up to 72% of the time.
Thelwall says that the tool needs to reach 95% accuracy to be viable. He and his colleagues therefore recommend that the algorithms be tested on a wider scale, so that they can obtain feedback from the university sector. And that limited access is not just a problem for the AI tool. “From a research-on-research perspective, it’s a tragedy that we put in all this effort and then we just delete [the data],” says James Wilsdon, a research-policy scholar and director of the Research on Research Institute in London. “The fear has always been that a university will raise a legal challenge, as there’s a lot of money at stake,” he adds.
Other inputs into the AI system include the productivity of the team generating the articles, how big the team is, how diverse it is in terms of the number of institutions and countries represented, and key words in article abstracts and titles.
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