Abstract
This paper discusses the problem of missing datasets for analysing and exhibiting the role of women in STEM with a particular focus on computer science (CS), artificial intelligence (AI) and data science (DS). It discusses the problem in a concrete case of a global south country (i.e., Mexico). Our study aims to point out missing datasets to identify invisible information regarding women and the implications when studying the gender gap in different STEM disciplines. Missing datasets about women in STEM show that the first step to understanding gender imbalance in STEM is building women’s history by “completing” existing datasets.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference53 articles.
1. Why So Few? Women in Science, Technology, Engineering, and Mathematics;Hill,2010
2. Reflections on Gender and Science;Keller,1987
3. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor;Eubanks,2018
4. Doing gender in engineering workplace cultures. II. Gender in/authenticity and the in/visibility paradox
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献