Investigating the causal effects of affiliation diversity on the disruption of papers in Artificial Intelligence

Abstract

Growing multiple-affiliation collaboration in Artificial Intelligence (AI) can help solve complex integrated problems, but will it trigger the disruption in AI? Scholars have discussed the related topics in other fields. However, these studies did not specifically target the field of AI and primarily relied on correlation methods, which may not lead to a causal conclusion. Analyzing around 0.6 million AI collaborative papers between 1950 and 2019 with 872,727 authors and 9,258 affiliations, this study tests the causal effect of multiple-affiliation collaboration on the disruption in AI by using descriptive analysis and a causal inference method, i.e., the Propensity Score Matching (PSM). We propose an improved affiliation diversity indicator to measure the distribution of affiliation differences in multiple-affiliation collaboration by taking disparity into account. Our results show that affiliation diversity exerts a negative causal effect on the disruption of papers in AI: (a) The average level of AI papers with diverse affiliation types or affiliation countries of authors is less disruptive than those with a single type or country. (b) Affiliation diversity will causally reduce the disruption of papers in AI by 2.006%∼5.891%. That indicates that AI papers with high affiliation diversity are significantly less disruptive, ranging from 2.006% to 5.891%, compared to those without. We cross-validate the findings by using five comparison experiments and five other matching methods. This study provides a comprehensive understanding of multiple-affiliation collaboration on AI disruption.