Intersectional Study of the Gender Gap in STEM through the Identification of Missing Datasets about Women: A Multisided Problem

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.

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