Will Affiliation Diversity Promote the Disruptiveness of Papers in Artificial Intelligence?

This study investigates the causal relationship between affiliation diversity and the disruptiveness of papers in the field of Artificial Intelligence (AI). We obtained 646,100 AI-related papers with complete affiliation information between 1950 and 2019 from the Microsoft Academic Graph. Descriptive analysis and Propensity Score Matching (PSM) methods are employed in this study. The results show that homophily (over 70%) has still been prevalent over the past 70 years among multi-affiliation collaborations in AI, despite the average affiliation diversity exhibiting startling upward trends when AI steps into the deep learning stage. Affiliation diversity cannot promote the disruptiveness of AI papers. On the contrary, AI papers with affiliation diversity can be 1.75% less disruptive compared to AI papers that collaborated by similar affiliations.