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. 2022 Sep 12:11:1037.
doi: 10.12688/f1000research.123599.1. eCollection 2022.

Female Models in AI and the Fight Against COVID-19

Claudia Guerrero  1 , Santiago Mazuelas  2
Affiliations

Female Models in AI and the Fight Against COVID-19

Claudia Guerrero et al. F1000Res. .

Abstract

Gender imbalance has persisted over time and is well documented in the fields of science, technology, engineering and mathematics (STEM) and singularly in artificial intelligence (AI). In this article we emphasize the importance of increasing the visibility and recognition of women researchers to attract and retain women in the AI field. We review the ratio of women in STEM and AI, its evolution through time, and the differences among disciplines. Then, we discuss the main sources of this gender imbalance highlighting the lack of female role models and the problems which may arise; such as the so called Marie Curie complex, suvivorship bias, and impostor syndrome. We also emphasize the importance of active participation of women researchers in conferences, providing statistics corresponding with the leading conferences. Finally, to support these views, we give examples of several prestigious female researchers in the field and we review their research work related to COVID-19 displayed in the workshop "Artificial Intelligence for the Fight Against COVID-19" (AI4FA COVID-19), which is an example of a more balanced participation between genders.

Keywords: AI; AI4FA COVID-19; COVID-19; STEM; Women.

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Conflict of interest statement

Competing interests: This work has been funded in part by AXA Research Fund.

Figures

Figure 1.
Figure 1.. The ratio of women in data analytics industry in Europe.
Figure 2.
Figure 2.. Women bestowed European Research Council Grants in Physical Sciences and Engeneering and elevated to IEEE Fellow. –
Figure 3.
Figure 3.. Keynote speakers at AI4FA COVID-19 workshop.
From left to right: Prof. Mihaela van der Schaar, Prof. Emilie Chouzenoux, Prof. Laure Wynants and Prof. Concha Bielza.
See this image and copyright information in PMC

References

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    1. Eurostat: Graduates by education level, programme orientation, sex and field of education. Eurostat Database;2020. Reference Source
    1. Stathoulopoulos K: How gender diverse is the workforce of AI research? 2019. Reference Source

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