Voices of Her: Analyzing Gender Differences in the AI Publication World (2025)

Abstract: 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.

## introduction motivated by the spirit of the acl year-round mentorship program 2 to support junior researchers to understand how a career path in nlp is, we want to answer this question technically, namely, what are the causal factors for academic success. although nearly half of the world population is female @xcite , the proportion of female researchers in science fields is often disproportionately smaller @xcite . specifically, in the research community of ai, we find that female researchers constitute only 17.99% of all the scholars in the field of ai with more than 100 citations, as collected in the ai scholar dataset @xcite . this fraction is even smaller in some subdomains of ai such as @xcite starting year to publish computer vision (cv), where only 15.64% of the researchers are females. despite this strong gender gap among ai researchers, comprehensive research on the state of the field is yet to be conducted. most existing research work has been done on certain subdomains of ai, such as the nlp community @xcite @xcite , or has addressed research aspects such as the values listed in top-cited papers @xcite . to the best of our knowledge, this study is the first to conduct an up-to-date ai community-wise comprehensive analysis since the study of @xcite . in this paper, we look into distinct features of the female subgroup in the ai community, and conduct comprehensive statistical analyses from a diverse range of perspectives: basic scholar profile statistics, citation trends, coauthorship, and linguistic styles of papers. the main findings from our study are as follows: our findings contribute suggestions and supporting evidence to future ai community organizers or individuals who want to push for informed community changes. empty abstracts and keep 91k papers, which we denote as "ai papers" in our analysis. ## analysis of scholar profiles we first analyze the basic scholar profiles to compare the general statistics with those of the female subgroup. our analyses answer the following questions: (1) what percentage of female scholars are there in ai and in each subdomain? (2) what are the scholarly indices of the female researchers doing in ai? and (3) how do the analyses differ if we consider the scholarly trends before and after 2012 -a year that corresponds approximately to the time when deep learning started to become widely used (among others, it is the publication year of alexnet @xcite ) -w.r.t. different academic age groups, and in academia vs. industry? note that for the scope of this paper, we focus on overall trends, and we encourage future work to dive into causal analysis. ## analysis of citation time series to take the analysis one step further, we perform a more fine-grained analysis of the scholar statistics. time series clustering. we are interested in patterns in the scholar citation time series. inspired by the time series construction by @xcite , we take the citations-by-year data of all the 78k scholars, normalize them by the average citation number, and linearly interpolate the citation time span to the largest number of active years, so that we can focus on the shape of the citation times series and stay agnostic with respect to different academic ages. we apply k-means clustering on time series @xcite and introduce our implementation details in appendix d.2. we further manually group the multiple clusters generated by the algorithm into four main types according to human-interpretable shape patterns. for notation convenience, we manually assign some easy-to-remember names to the four cluster types: the exponential cluster ( ), linear cluster (↗), stumbling cluster (↷), and struggling cluster (⇝). for each type, we visualize a representative cluster in figure 3 , and plot all machine-identified clusters in appendix f.1. cluster statistics. in the smallest cluster is the struggling cluster, where the scholars experience fluctuations in citations but no clear pattern of overall increase. this cluster is also the one that correlates with the highest ratio of scholars that stop publishing, with a percentage of 44+% for both average scholars and female scholars, followed by the stumbling cluster with 22+% percentage of people who stop publishing. all these percentages are substantially higher than the stop-publishing percentage in the exponential and linear clusters. female subgroups in the clusters. for each cluster that we identify, we also show the female ratio of each feature for the clusters in as an additional note, we also conduct additional analysis to focus on female subgroups in nlp, and find that female researchers in nlp have higher citations than average female scholars, which is a trend across all clusters. we include detailed results in the appendix ## analysis of co-authorship patterns we also address co-authorship patterns, and conduct analyses to answer the following questions: ## analysis of female-authored papers since our study features a comprehensive bottom-up analysis of the female subgroup, we have covered statistics related to individual scholars and coauthorship, and, finally, in this section, we analyze statistics of female-authored papers. ## conclusion in this work, we investigated the gender differences in the ai publication world from a comprehensive range of perspectives: basic scholar profile statistics, citation trends, coauthorship, and linguistic styles of papers. we identified that the female subgroup overall still shows underrepresentation and disadvantages in the ai community. however, there are also distinct characteristics of the female subgroup that makes it unique from the general population. our analysis provides a window to look at the current trends in our ai community, and encourages more gender equality and diversity in the future.