Computing is one of the only scientific fields which was once female-dominated. In the 30s and 40s, women made up the bulk of the workforce doing complex, tedious calculations in the fields including ballistics, astrophysics, aeronautics (think Hidden Figures) and code-breaking. Engineers themselves found that the female computers were far more reliable than themselves in doing such calculations [9]. As computing machines became available, there was no precedent set for the gender of a computer operator, and so the women previously doing the computing became the computer operators [10].
However, this was not to last. As computing became commercialised in the 50s, the skill required for computing work was starting to be recognised. As written in [1]:
“Software company System Development Corp. (SDC) contracted psychologists William Cannon and Dallis Perry to create an aptitude assessment for optimal programmers. Cannon and Perry interviewed 1,400 engineers — 1,200 of them men — and developed a “vocational interest scale,” a personality profile to predict the best potential programmers. Unsurprisingly given their male-dominated test group, Cannon and Perry’s assessment disproportionately identified men as the ideal candidates for engineering jobs. In particular, the test tended to eliminate extroverts and people who have empathy for others. Cannon and Perry’s paper concluded that typical programmers “don’t like people,” forming today’s now pervasive stereotype of a nerdy, anti-social coder.”
As more men entered the field of computing, women were less likely to be promoted and were paid significantly less than their male counterparts. By 1973, entry-level computer science classes used a nude image of Playboy centerfold model Lena Soderberg to teach engineers how to turn physical photographs into digital bits (the original jpegs). This became standard practice for computer science departments worldwide [11].
Fast-forward to today. While the representation of women in computing has improved since its low point, there is still a long way to go. As of 2021, A-level computer science was made up of 80% male students and 20% female students. Representation of women in undergraduate computer science degrees in the UK is even lower, at just 16%, according to a 2020 article [2]. When it comes to AI-related careers worldwide, 22% of professionals are women, and just 2% (!) of capital was directed towards startups founded by women in 2019 [3]. When we compare average salaries in STEM in the US categorised by gender, race and ethnicity, it becomes obvious that Black and Hispanic women earn significantly less than both their male counterparts and than White and Asian women [4]. These numbers would be further impacted if we were to further categorise by sexuality, gender identity, disability and socio-economic status.
In order to understand where bias is most prevalent within STEM, Smyth and Nosek [5] analysed results of 176,935 college-educated participants of the Harvard gender-science implicit associations test (IAT) [6]. It was found that implicit bias towards ‘science-is-male’ was the norm among all groups, and was most strong in women within non-scientific fields, and men within scientific fields. However, men within fields where women are strongly represented, such as the life sciences, have similar levels of bias as those within fields where women remain distinct minorities.
The observation that even where women are strongly represented, the bias of the male scientists remains strong, is indicative that more needs to be done to level the playing field than purely increasing representation. Additionally, recent research has shown that the perception of increased female participation in a STEM discipline makes it more likely to be labelled a ‘soft’ science, which causes the field to be “devalued, deemed less rigorous and less worthy of federal funding” [7].
So if we want all walks of women to be not only equally represented, but also their work to be valued equally within computing and wider STEM fields, what do we need to do?
There are plenty of resources out there for assessing our own biases and understand how they manifest in the workplace and wider world. If everyone engaged with such ideas, the statistics may quickly look quite different. For this to happen, we need to invest in encouraging every person to understand the issues and that by engaging, we can improve conditions for everyone. A key manifestation of unconscious bias is that the marginalised group will be not listened to: a man explaining feminism makes a larger impact than a woman saying the exact same words. However, there are significant obstacles to this happening, such as lack of awareness, threat of hostility from other men, or the fear that men will ‘take over’ women’s organisations and spaces [8]. If we can figure out how get men properly involved in the conversation, we will both take the burden off the small number of women in positions of power, and we will be able to reach groups of people that thus far have not engaged. By getting everyone involved, we will hugely improve the chances of having true equality in STEM, sooner.
REFERENCES
[3] https://www.unesco.org/reports/science/2021/en/women-digital-revolution
[4] Pew research centre, 2017-2019 IPUMS
[5] https://www.frontiersin.org/articles/10.3389/fpsyg.2015.00415/full
[6] https://implicit.harvard.edu/implicit/takeatest.html
[7] https://www.sciencedirect.com/science/article/pii/S0022103121001372?via%3Dihub
[8] https://www.cidse.org/2020/03/04/what-is-the-role-of-men-in-feminism/
[9] Light, Jennifer S. (1999). “When Computers Were Women”. Technology and Culture. 40 (3): 455–483. doi:10.1353/tech.1999.0128. JSTOR 25147356. S2CID 108407884.
[10] Hicks, Marie (2017). Programmed Inequality: How Britain Discarded Women Technologists and Lost Its Edge in Computing. MIT Press
[11] Evans, Claire L. (2018). Broad Band: The Untold Story of the Women Who Made the Internet. New York: Portfolio/Penguin
