Study measures benefits of AI in research, yet highlights potential disparities

Students at the Hong Kong University of Science and Technology use virtual reality headsets in class – Copyright AFP Peter PARKS

Among approximately 75 million publications analysed, those employing AI are more likely to be a ‘hit paper’. Examples range fromdesigning new drug candidates in medicine to drafting new taxation policies in social sciences. Yet, many researchers still lack a systematic understanding of how AI may benefit scientific research, highlighting a substantial AI use–AI training gap. Several researchers lack a systematic understanding of how AI may benefit their research, and scepticism remains about whether AI is capable of advancing science in every field.

This comes following two scientists known for their pioneering AI research earning the Nobel Prize in Physics and a trio of scientists gaining the Nobel Prize in Chemistry, which recognized the use of advanced technology, including AI, to predict the shape of proteins.

In general, the use of AI is widespread across the sciences, dramatically increasing since 2015. Within this overall finding, the Northwestern University researchers found demographic disparities regarding the benefits of AI.

In particular, the study also highlights the unequal effects on women and minority researchers that the steadfast rise of AI use in scientific research may bring.

Data was drawn from 74.6 million publications, 7.1 million patents and 4.2 million university course syllabi to derive at the conclusion that AI exhibit a “citation impact premium.”

There has been a growing use of AI in disciplinary research since 2015, proxied by the mention of AI-related terms (such as “artificial intelligence,” “deep learning” and “convolutional neural network”) in the title or abstract of publications.

The main disciplines are computer science (37%), engineering (24%), physics (24%), biology (22%), psychology (24%), economics (14%), sociology (30%) and political science (27%).Each has shown notably sharp increases in direct AI use scores due to the development of new AI capabilities.

Regardless of discipline, disciplinary papers that mention AI-related terms in their title or abstract receive more citations, being more likely to be a hit, and receive a higher fraction of citations from other disciplines.

Lead scientists Dashun Wang and Jian Gao developed a measurement framework to estimate the direct use and potential benefits of AI in scientific research by applying natural language processing (NLP) techniques to these vast datasets.

The study, “Quantifying the Use and Potential Benefits of Artificial Intelligence in Scientific Research,” appears in the journal Nature Human Behaviour.