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From designing new drug candidates in medicine to drafting new taxation policies in social sciences, the benefits of artificial intelligence (AI) in scientific research are all around.

Just this week, two scientists known for their pioneering AI research earned the Nobel Prize in Physics, and a trio of scientists earned the Nobel Prize in Chemistry, which recognized the use of advanced technology, including AI, to predict the shape of proteins. Despite its rapid progress and broad applications, however, many researchers lack a systematic understanding of how AI may benefit their research, and skepticism remains about whether AI is capable of advancing science in every field.

A new Northwestern University study analyzing 74.6 million publications, 7.1 million patents and 4.2 million university course syllabi finds papers that employ AI exhibit a "citation impact premium." However, the benefits of AI do not extend equitably to women and minority researchers, and, as AI plays more important roles in accelerating science, it may exacerbate existing disparities in science, with implications for building a diverse, equitable and inclusive research workforce.

The research team, led by the Kellogg School of Management's 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.

Wang is a professor of management and organizations at Kellogg and of industrial engineering and management sciences at McCormick, director of Kellogg's Center for Science of Science and Innovation (CSSI) and co-director of Kellogg's Ryan Institute on Complexity. Gao is a research assistant professor at Kellogg CSSI.

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