Network structure, gender diversity, and interdisciplinarity predict the centrality of AI organizations.

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Abstract:

Artificial intelligence (AI) research plays an increasingly important role in society, impacting key aspects of human life. From face recognition algorithms aiding national security in airports, to software that advises judges in criminal cases, and medical staff in healthcare, AI research is shaping critical facets of our experience in the world. But who are the people and institutional bodies behind this influen- tial research? What are the predictors of influence of AI researchers and research organizations? We study this question using social network analysis, in an exploration of the structural characteristics, i.e., network topology, of research organizations that shape modern AI. In a sample of 158 organizations with 16,385 affiliated authors of published papers in prominent AI conferences (e.g., NeurIPS, FAccT, AIES), we find that both industry and academic research organizations with influential authors are more interdisciplinary, more hierarchical, more gender diverse, and less clustered. Here, authors’ betweenness centrality in co-authorship networks was used as a measure of their influence. We also find that gender minorities (e.g., women) have less influence in the AI community, determined as lower betweenness centrality in co-authorship networks. These results suggest that while diversity adds significant value to AI based organizations, the individuals contributing to the increased diversity are marginalized in the AI field. We discuss these results in the context of current events with important societal implications.

Publisher's Version

Last updated on 08/31/2021