Voices of Her: Analyzing Gender Differences in the AI Publication World
Yiwen Ding Jiarui Liu Zhiheng Lyu Kun Zhang Bernhard Schoelkopf Zhijing Jin Rada Mihalcea
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.