# Women, artificial intelligence, and key positions in collaboration networks: Towards a more equal scientific ecosystem

arXiv: https://arxiv.org/abs/2205.12339 · HTML: https://mlsanity.com/paper/2205.12339/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 63 references that are in the map.
- First posted on arXiv 19 May 2022.

## Builds on (most cited first, 10 of 10)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Historical comparison of gender inequality in scientific careers across countries and disciplines](https://mlsanity.com/paper/1907.04103/) (2019)
- [Gender Disparities in Science? Dropout, Productivity, Collaborations and Success of Male and Female Computer Scientists](https://mlsanity.com/paper/1704.05801/) (2017)
- [Quantifying the impact of weak, strong, and super ties in scientific careers](https://mlsanity.com/paper/1509.01804/) (2015)
- [Gender-Specific Patterns in the Artificial Intelligence Scientific Ecosystem](https://mlsanity.com/paper/2106.01446/) (2021)
- [Analyzing the network structure and gender differences among the members of the Networked Knowledge Organization Systems (NKOS) community](https://mlsanity.com/paper/1803.04225/) (2018)
- [Gender differences in research collaboration](https://mlsanity.com/paper/1810.13355/) (2018)
- [Are researchers that collaborate more at the international level top performers? An investigation on the Italian university system](https://mlsanity.com/paper/1811.01796/) (2018)
- [The collaboration behavior of top scientists](https://mlsanity.com/paper/1909.08445/) (2019)

## Abstract (arXiv)

Scientific collaboration in almost every discipline is mainly driven by the need of sharing knowledge, expertise, and pooled resources. Science is becoming more complex which has encouraged scientists to involve more in collaborative research projects in order to better address the challenges. As a highly interdisciplinary field with a rapidly evolving scientific landscape, artificial intelligence calls for researchers with special profiles covering a diverse set of skills and expertise. Understanding gender aspects of scientific collaboration is of paramount importance, especially in a field such as artificial intelligence that has been attracting large investments. Using social network analysis, natural language processing, and machine learning and focusing on artificial intelligence publications for the period from 2000 to 2019, in this work, we comprehensively investigated the effects of several driving factors on acquiring key positions in scientific collaboration networks through a gender lens. It was found that, regardless of gender, scientific performance in terms of quantity and impact plays a crucial in possessing the "social researcher" in the network. However, subtle differences were observed between female and male researchers in acquiring the "local influencer" role.