Voices of Her: Analyzing Gender Differences in the AI Publication World
This research addresses gender inequality in the AI community by providing data-driven insights to encourage more diversity and equality, though it is incremental as it builds on prior studies of gender bias in research.
The study analyzed gender differences in the AI publication world using a dataset of 78K researchers, finding that female researchers have fewer overall citations but this varies by academic age, there is high gender homophily in co-authorship, and female first-authored papers exhibit distinct linguistic styles like longer text and more positive emotion words.
While several previous studies have analyzed gender bias in research, we are still missing a comprehensive analysis of gender differences in the AI community, covering diverse topics and different development trends. Using the AI Scholar dataset of 78K researchers in the field of AI, we identify several gender differences: (1) Although female researchers tend to have fewer overall citations than males, this citation difference does not hold for all academic-age groups; (2) There exist large gender homophily in co-authorship on AI papers; (3) Female first-authored papers show distinct linguistic styles, such as longer text, more positive emotion words, and more catchy titles than male first-authored papers. Our analysis provides a window into the current demographic trends in our AI community, and encourages more gender equality and diversity in the future. Our code and data are at https://github.com/causalNLP/ai-scholar-gender.