CAREER: How Collaboration and Competition Influence Research in the Emerging Field of Artificial Intelligence

Artificial Intelligence (AI) is a set of disruptive technologies that automate decision-making, problem-solving, and content generation. AI stands out among scientific fields for its impact on society and is a prescient case to study this tension. Much hope and promise exist for AI to bring about profound societal improvements in education, commerce, and medicine. However, it also poses significant risks to privacy, jurisprudence, security, and even armed conflicts. As the public discourse on large-language models (LLMs) such as ChatGPT has made clear, AI is now on the frontline of both scientific advancement and international politics. This project investigates how international politics shapes the global field of AI academic research. This research compares how nations lead in cutting-edge AI research and invest in AI as the engine for the next industrial revolution to reduce gaps in basic research breakthroughs and high-end product development. This research explores how international politics creates understudied tensions for academic researchers between (1) the global, open, and universalistic aspects of scientific research and (2) the growing competitive distrust and cutting of collaborative ties between nations. The researcher conducts a three-part, large-scale, multi-method, and interconnected study to demonstrate how international tensions influence AI researchers, their science, and the implications to global scientific research. The first project deploys social network analysis and natural language processing (NLP) techniques to measure international influence by asking whether distinct national signatures of research manifest in academic AI publications of other countries using a database called OpenAlex. The project examines the international landscape of AI research, highlighting variations across regions and institutions. It also analyzes the evolving dynamics of international collaboration in AI research and the contributions of different countries and universities to the field's intellectual production. The second project deploys survey experiments of academic AI researchers to determine how overt or understated this national influence is. The project examines potential bias in evaluating research from different countries, specifically in AI and how affiliation influences the adoption and evaluation of research from other countries. The third project applies NLP techniques to the transcripts of hundreds of in-depth semi-structured interviews with researchers around the world to show how national and international influences manifest in individual researchers’ collaborations, work, and careers. Applying NLP techniques to the interview transcripts can uncover dominant themes and topics at scale, leading to a more nuanced understanding of the issues being examined. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.