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China and the U.S. produce more impactful AI research when collaborating together

Bedoor AlShebli, Shahan Ali Memon, James A. Evans, Talal Rahwan

TL;DR

It is found that since 2000, China and the U.S. have led the field in terms of impact, novelty, productivity, and workforce and a matching experiment reveals that the two countries have always been more impactful when collaborating than when each works without the other.

Abstract

Artificial Intelligence (AI) has become a disruptive technology, promising to grant a significant economic and strategic advantage to nations that harness its power. China, with its recent push towards AI adoption, is challenging the U.S.'s position as the global leader in this field. Given AI's massive potential, as well as the fierce geopolitical tensions between China and the U.S., several recent policies have been put in place to discourage AI scientists from migrating to, or collaborating with, the other nation. Nevertheless, the extent of talent migration and cross-border collaboration are not fully understood. Here, we analyze a dataset of over 350,000 AI scientists and 5,000,000 AI papers. We find that since 2000, China and the U.S. have led the field in terms of impact, novelty, productivity, and workforce. Most AI scientists who move to China come from the U.S., and most who move to the U.S. come from China, highlighting a notable bidirectional talent migration. Moreover, the vast majority of those moving in either direction have Asian ancestry. Upon moving, those scientists continue to collaborate frequently with those in the origin country. Although the number of collaborations between the two countries has increased since the dawn of the millennium, such collaborations continue to be relatively rare. A matching experiment reveals that the two countries have always been more impactful when collaborating than when each works without the other. These findings suggest that instead of suppressing cross-border migration and collaboration between the two nations, the science could benefit from promoting such activities.

China and the U.S. produce more impactful AI research when collaborating together

TL;DR

It is found that since 2000, China and the U.S. have led the field in terms of impact, novelty, productivity, and workforce and a matching experiment reveals that the two countries have always been more impactful when collaborating than when each works without the other.

Abstract

Artificial Intelligence (AI) has become a disruptive technology, promising to grant a significant economic and strategic advantage to nations that harness its power. China, with its recent push towards AI adoption, is challenging the U.S.'s position as the global leader in this field. Given AI's massive potential, as well as the fierce geopolitical tensions between China and the U.S., several recent policies have been put in place to discourage AI scientists from migrating to, or collaborating with, the other nation. Nevertheless, the extent of talent migration and cross-border collaboration are not fully understood. Here, we analyze a dataset of over 350,000 AI scientists and 5,000,000 AI papers. We find that since 2000, China and the U.S. have led the field in terms of impact, novelty, productivity, and workforce. Most AI scientists who move to China come from the U.S., and most who move to the U.S. come from China, highlighting a notable bidirectional talent migration. Moreover, the vast majority of those moving in either direction have Asian ancestry. Upon moving, those scientists continue to collaborate frequently with those in the origin country. Although the number of collaborations between the two countries has increased since the dawn of the millennium, such collaborations continue to be relatively rare. A matching experiment reveals that the two countries have always been more impactful when collaborating than when each works without the other. These findings suggest that instead of suppressing cross-border migration and collaboration between the two nations, the science could benefit from promoting such activities.
Paper Structure (9 sections, 4 figures)

This paper contains 9 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Results
  3. Discussion
  4. Data and Code Availability
  5. Author Contributions
  6. Competing interests

Figures (4)

  • Figure 1: The U.S. and China leading in impact, novelty, productivity, and number of AI scientists.a, Main plot: The 20 countries producing the most AI papers. Inset: The number of AI papers produced by the leading two countries (U.S. and China) over time. b, Main plot: The 20 countries with the largest number of AI scientists. Inset: The number of AI scientists in the leading two countries (U.S. and China) over time. c, Main plot: The 20 countries that garner the highest impact in AI research. Inset: The average impact of AI papers in the leading two countries (U.S. and China) over time. d, Main plot: The 20 countries with the greatest share of hits based on impact. Inset: The share of hit AI papers based on impact in the leading two countries (U.S. and China) over time. e and f, The same as (c) and (d), respectively, but for context novelty instead of impact.
  • Figure 2: The interplay between AI scientists' migration and cross-border collaboration.a, Main plot: The 20 countries from which the largest number of AI scientists migrate to the U.S. Inset: For the top five countries, the number of AI scientists that migrate to the U.S. over time. b, The same as (a) but for China instead of the U.S. c-e, For AI scientists migrating from the U.S. to China, and vice versa, the distributions of career age, impact, and productivity. f, Out of all AI scientists that are based in country $A$, comparing those who migrated from country $B$ to those who did not, in terms of the percentage of their papers that involve coauthors from $B$; the comparison is done using Coarsened Exact Matching, and only considers the years after the migration, while controlling for career age, impact, and productivity. Error bars represent bootstrapped 95% confidence intervals; P values are from t-tests; * $p<.05$; *** $p<.001$.
  • Figure 3: The impact of U.S.-China AI collaborations. Analyzing four types of AI papers: (i) U.S.-based papers produced in collaboration with China, $(\mathit{US}, \mathit{China})$; (ii) U.S.-based papers produced without China, $(\mathit{US}, \neg\mathit{China})$; (iii) China-based papers produced in collaboration with the U.S., $(\mathit{China}, \mathit{US})$; (iv) China-based papers produced without the U.S., $(\mathit{China}, \neg\mathit{US})$. a, Annual number of papers per type (log scale). b, Distribution of team size per type. c, For each type, the percentage of papers of which the last author's affiliation is among the 100 most impactful institutions in AI. d, The infographic illustrates the Coarsened Exact Matching (CEM) experiment, while the plot depicts the percentage increase in impact of $(\mathit{US}, \mathit{China})$ compared to $(\mathit{US}, \neg\mathit{China})$, as well as the percentage increase in impact of $(\mathit{China}, \mathit{US})$ compared to $(\mathit{China}, \neg\mathit{US})$, over time. Error bars represent bootstrapped 95% confidence intervals; P values are calculated using t-tests (a, b, and d) and two-sided Fisher’s exact test (c); * $p<.05$; ** $p<.01$; *** $p<.001$.
  • Figure 4: Publication venue analysis. Comparing four types of AI papers: (i) U.S.-based papers produced in collaboration with China, $(\mathit{US}, \mathit{China})$; (ii) U.S.-based papers produced without China, $(\mathit{US}, \neg\mathit{China})$; (iii) China-based papers produced in collaboration with the U.S., $(\mathit{China}, \mathit{US})$; (iv) China-based papers produced without the U.S., $(\mathit{China}, \neg\mathit{US})$. a, For each type, the percentage of papers published in the top five AI conferences. b, Similar to (a) but over time. c, Similar to (a) but disaggregated across the top five AI conferences.