# Internationalizing AI: Evolution and Impact of Distance Factors

arXiv: https://arxiv.org/abs/2112.01231 · HTML: https://mlsanity.com/paper/2112.01231/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 63 references that are in the map.
- First posted on arXiv 10 Nov 2021.

## Builds on (most cited first, 10 of 10)

- [Mapping the Landscape of Artificial Intelligence Applications against COVID-19](https://mlsanity.com/paper/2003.11336/) (2020)
- [The Pace of Artificial Intelligence Innovations: Speed, Talent, and Trial-and-Error](https://mlsanity.com/paper/2009.01812/) (2020)
- [Team size matters: Collaboration and scientific impact since 1900](https://mlsanity.com/paper/1410.8544/) (2014)
- [International Research Collaboration: Novelty, Conventionality, and Atypicality in Knowledge Recombination](https://mlsanity.com/paper/1804.09070/) (2018)
- [Machine Teaching by Domain Experts: Towards More Humane,Inclusive, and Intelligent Machine Learning Systems](https://mlsanity.com/paper/1908.08931/) (2019)
- [Growth of International Cooperation in Science: Revisiting Six Case Studies](https://mlsanity.com/paper/1612.07208/) (2016)
- [Characterizing Research Leadership on Geographical Weighted Collaboration Network](https://mlsanity.com/paper/2004.07102/) (2020)
- [Mapping the co-evolution of artificial intelligence, robotics, and the internet of things over 20 years (1998-2017)](https://mlsanity.com/paper/2006.02366/) (2020)
- [Understanding Team Collaboration in Artificial Intelligence from the perspective of Geographic Distance](https://mlsanity.com/paper/2012.13560/) (2020)
- [Does the geographic proximity effect on knowledge spillovers vary across research fields?](https://mlsanity.com/paper/2103.14557/) (2021)

## Abstract (arXiv)

International collaboration has become imperative in the field of AI. However, few studies exist concerning how distance factors have affected the international collaboration in AI research. In this study, we investigate this problem by using 1,294,644 AI related collaborative papers harvested from the Microsoft Academic Graph (MAG) dataset. A framework including 13 indicators to quantify the distance factors between countries from 5 perspectives (i.e., geographic distance, economic distance, cultural distance, academic distance, and industrial distance) is proposed. The relationships were conducted by the methods of descriptive analysis and regression analysis. The results show that international collaboration in the field of AI today is not prevalent (only 15.7%). All the separations in international collaborations have increased over years, except for the cultural distance in masculinity/felinity dimension and the industrial distance. The geographic distance, economic distance and academic distances have shown significantly negative relationships with the degree of international collaborations in the field of AI. The industrial distance has a significant positive relationship with the degree of international collaboration in the field of AI. Also, the results demonstrate that the participation of the United States and China have promoted the international collaboration in the field of AI. This study provides a comprehensive understanding of internationalizing AI research in geographic, economic, cultural, academic, and industrial aspects.