Friends,
This is an excellent history of women in computer science that should cast all doubts away that women are not equipped to be computer scientists. In fact, women, more than men, were initially involved not just with computers, but contributing significantly to space missions during the 1960s.
Though not addressed herein, there must be a sidebar conversation on racial/ethnic equity and STEM. The piece does touch a bit on class.
Of concern to policymakers should be a lack of exposure to STEM subjects for Black and Brown children and youth as this is a significant factor contributing to their low enrollment in STEM fields and professions (Adelman, 2006; Anderson, 2014; Wang, Hong, Ravitz, & Hejazi Moghadam, 2016), and their under-representation as degree recipients at the bachelor’s level (National Center for Science and Engineering Statistics, 2023; also see Pew, 2021).
These kinds of histories urgently need to be revived and taught in every STEM classroom throughout K-12 education lest these inequities persist. All of our youth and their parents need to know this precious knowledge, as well.
This foray into women and minorities in STEM has been illuminating.
-Angela Valenzuela
References
Adelman, C. (2006). The toolbox revisited: Paths to degree completion from high school through college. U.S. Department of Education. http://hdl.voced.edu.au/10707/224025
Anderson, K. A. (2014). Equity in opportunities to learn mathematics: Policy and practice implications for high-achieving Black students. In Y. Sealey-Ruiz C. W. Lewis, and I. Toldson (Eds.), Teacher education and Black communities: Implications for access, equity, and achievement. Information Age Publishing Inc.
National Center for Science and Engineering Statistics. (2023, January 30). Diversity and STEM: Women, minorities, and persons with disabilities (NSF 23-315). Directorate for Social, Behavioral and Economic Sciences, National Science Foundation. Alexandria, VA. https://ncses.nsf.gov/pubs/nsf23315/report
Pew Research Center (2021). STEM jobs see uneven progress in increasing gender, racial, and ethnic diversity. [Data Set]. https://www.pewresearch.org/science/2021/04/01/stem-jobs-see-uneven-progress-in-increasing-gender-racial-and-ethnic-diversity/
Wang, J., Hong, H., Ravitz, J., & Hejazi Moghadam, S. (2016, February). Landscape of K-12 computer science education in the US: Perceptions, access, and barriers. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education (pp. 645-650).
Where are the Women? A Detailed History of Women in Computer Science and How it Impacts the Modern Day Industry
Written by Mary Kate Nowak May 22, 2024
Introduction
In the United States, computer science is considered an extremely lucrative career. In 2019, STEM-educated workers in computer occupations had the highest median annual earnings among STEM occupations. [1] Yet, computer science has one of the largest gender gaps in STEM. As of 2022, 91% of software developers were men. [2]
This gender inequality did not always exist. Women were among the first programmers in the early 20th century and contributed substantially to the industry.[3] By the early 1940s, the majority of people employed as computers were women.[4] However, as software development rose in popularity and exclusivity, women were slowly pushed out of the industry. In the late half of the 20th century, a cultural stigma arose where programming was seen as a job better suited for men than women. Despite their historic contributions to the industry, the number of women in computing has never recovered to the levels reached in the late 70s and early 80s.
This gender gap has implications not only for the tech industry but for society as a whole. Artificial Intelligence (AI) technology is gaining popularity in countries across the globe. Yet only 22% of AI professionals are female. [5] To many, AI may come across as an objective, or “neutral,” learning model compared to humans, whose analyses may be clouded by their own personal biases. [6] However, humans are ultimately responsible for creating algorithms. [7] As a result, these programmers can unknowingly insert their own social biases into the programs. As society increasingly relies on AI, every user becomes susceptible to these biases. [8] If this bias goes unaddressed, many marginalized groups will be prevented from fully participating in the economy and society. [9] Therefore, these systems must be designed and created by a diverse representation of leaders. If AI is a tool used by the people, then it needs to better reflect the diverse communities it serves.
A Brief History of Women in Computer Science
Women have been present in computer science since its very beginnings. Ada Lovelace is often credited as the first computer programmer. [10] During World War II, many women entered the programming field to free men up for “skilled” labor. [11] After the war ended, women continued to work in programming because they were so good at it. [12] The Apollo missions of the 1960s were only successful because of the many women working behind the scenes. [13]
Historically, computer programming was not considered a lucrative field. When computers were first being built, many people believed that building the computer was the most “important thing.” [14] Accordingly, hardware engineering was considered an elite job reserved primarily for men.[15] By contrast, computer programming was considered a “menial” and unskilled job, similar to typists. [16] As the demand for programmers , many women were hired to fill those roles. [17]
As we know now, software is anything but an “easy” field. Programmers – or “computers” as they were often called – were responsible for computing long and tedious mathematical calculations by hand. [18] An ideal coder was someone patient, persistent, and detail-oriented.[19] Accordingly, women proved to be natural as computers and were often sought out for these jobs because of their reputation for detail-oriented work. [20] Many male engineers, including those at the National Advisory Committee for Aeronautics (NACA), admitted that “[women] computers do the work more rapidly and accurately than [men] could.”[21] By the 1940s, almost all human computers were women. [22]
Women also contributed significantly to the space missions of the 1960s. In the 1960s, Margaret Hamilton studied software reliability while working at the US SAGE air defense system.[23] She was responsible for programming the onboard flight software for the Apollo mission computers.[24] After Hamilton had completed the software program, the code was sent to Raytheon where expert seamstresses hardwired the code by threading copper wire through magnetic rings. [25] These seamstresses were often Navajo women and were highly skilled in the traditional Navajo craft of weaving. [26] Their weaving skills is what allowed them to excel at the delicate work of assembling and testing integrated circuits.
While women’s role in the industry may have been minimized, computer science would not be as it is today without the contributions of women.
What Changed?
If the Apollo missions taught us anything, it was that computer programming was incredibly important. By the end of the 1960s, the United States began recognizing the value of software development and the significant expense that came along with it. Computing jobs thus became higher-status and better-paid positions.
However, as computer programming became more lucrative, women were slowly pushed out of the industry. Following the Apollo missions, the media minimized women’s contributions and presented their roles as requiring “no thinking and no skill” and as a “feminine craft” not to be confused with engineering. [27] The software boom also required employers to hire more computer programmers. Because programming was now considered a “skilled” position, employers began relying on aptitude tests and personality profiles to hire the best candidates. However, these hiring processes prioritized stereotypically masculine traits and ultimately favored men. [28] By the end of the decade, the general demographics of programmers began to shift away from being predominantly women.
The 1980s also marked a significant cultural change in the attitude towards computers. Before the 70s and 80s, almost nobody had access to personal computers. As a result, most college students were on equal footing in terms of computing skills. But, as computers became a more common household item, they were often marketed towards boys. “[B]oys were more than twice as likely to have been given one as a gift by their parents. And if parents bought a computer for the family, they most often put it in a son’s room, not a daughter’s.” [29] As a result, boys received much more exposure to computers at an early age than girls which may have given men a significant head start in college classrooms.
There were also many differences inside the classroom as well. Women in college computing programs faced significant harassment from male professors and peers. A 1983 study involving MIT students found that women who raised their hands in class were often ignored by professors and talked over by other students. [30] The classrooms also openly embraced sexist practices that ostracized female classmates. At the University of Southern California, 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. [31]
As a result, a stereotype cultivated in the United States that computer programming was a job better suited for men than women. [32] By the 1990s, the computer science industry was completely dominated by men. A cultural shift had taken hold, and the interest among women in computing has never recovered to the levels reached in the late 70s and early 80s.
Bringing Women Back Into Computer Science
Diversity is incredibly important across all professions, including STEM. A diverse workforce is not only important for employee satisfaction and retention, but it has also been shown to result in better financial performance. [33] However, the lack of women in computer science has implications that extend beyond the computer science industry.
Artificial Intelligence (AI) is a technology that allows computers to simulate human intelligence and can ultimately assist with problem-solving and computing. AI is often seen as an “objective” program. [34] However, it is anything but. Human programmers are the ones ultimately responsible for creating the algorithms, and they can often impute their own social biases. Similarly, AI models draw their material from information that has already been published, which can contain its own set of biases. [35] In other words, if the inputs into the program are biased, then the resulting outputs will also be biased.
For instance, Joy Buolamwini is a computer scientist who has researched facial recognition across gender and race. In her studies, she found that darker-skinned women’s faces had up to 34.7% error rates, while the maximum error rate for white men was 0.8%. [36] This was largely due to the fact that the face-analysis algorithms were trained this way – there was a significant lack of darker skin tones in the training data used. Ultimately, the people most affected by biases in AI and technology are the ones who are rarely in a position to fix it. If biases like this continue to go unaddressed, marginalized groups may be prevented from fully participating in the economy and society. [37]
The use of artificial intelligence models in different industries significantly impacts women’s lives. Therefore, as society becomes more dependent upon AI systems, these systems must be designed and created by a diverse representation of leaders with different gender, cultural, educational, and experiential backgrounds. This diversity of thought is crucial for AI to continue to push the boundaries of what technology can achieve. [38]
Conclusion
This article focuses on the history of women in computer science. While history is important, it is also crucial that we remain focused on the future. Bringing women back into computer science is more than just a solution to correct past mistakes. As society becomes more dependent upon AI and AI systems, we need a diverse representation of leaders to design algorithms and eliminate existing biases.
There are several ways to get women back into computer science. Education and early exposure programs are great ways to introduce young women to computer science at a young age. This can often be done through coding clubs, mentorship programs, and workshops. However, the lack of women is not limited to just a recruitment problem. Many women may leave the STEM workforce because of various work environments. Companies should be promoting a culture of diversity and inclusion to create a work environment and supports and encourages women.
Encouraging women to enter and stay in the tech industry is not an easy task. However, by taking initiatives such as these, we can start to work towards a more diverse and inclusive world.
[1] US Census Bureau, STEM Majors Earned More Than Other STEM Workers, Census.gov, https://www.census.gov/library/stories/2021/06/does-majoring-in-stem-lead-to-stem-job-after-graduation.html#:~:text=STEM%2Deducated%20workers%20in%20computer [https://perma.cc/LF96-AU4V] (last visited Feb 25, 2024).
[2] Lionel Sujay Vailshery, Software developers: distribution by gender 2020, Statista (2023), https://www.statista.com/statistics/1126823/worldwide-developer-gender/ [https://perma.cc/VR2P-DCKS].
[3] Clive Thompson, The Secret History of Women in Coding, The New York Times, Feb. 13, 2019, https://www.nytimes.com/2019/02/13/magazine/women-coding-computer-programming.html [https://perma.cc/45F4-ZSXM] [hereinafter The Secret History of Women in Coding].
[4] Programming: when did “women’s work” become a “man’s world”?, Digital Future Society (2021), https://digitalfuturesociety.com/programming-when-did-womens-work-become-a-mans-world/ [https://perma.cc/86KX-GLB7].
[5] Carmen Niethammer, AI Bias Could Put Women’s Lives At Risk – A Challenge For Regulators, Forbes (2020), https://www.forbes.com/sites/carmenniethammer/2020/03/02/ai-bias-could-put-womens-lives-at-riska-challenge-for-regulators/?sh=1dd159fd534f [https://perma.cc/6SXU-H5GB] (last visited Feb 25, 2024).
[6] Matthew Putman, Artificial Objectivity, Medium (2016), https://medium.com/@MatthewPutman/artificial-objectivity-c5233a8c453b [https://perma.cc/M4U2-RFXV].
[7] Id.
[8] Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on Fairness, Accountability and Transparency. 77–91.
[9] IBM Data and AI Team, Shedding light on AI bias with real world examples, IBM Blog (2023), https://www.ibm.com/blog/shedding-light-on-ai-bias-with-real-world-examples/ [https://perma.cc/DE2R-EQMQ.]
[10] The Editors of Encyclopedia Britannica, Ada Lovelace | Biography & Facts, Encyclopædia Britannica (2018), https://www.britannica.com/biography/Ada-Lovelace [https://perma.cc/MQA3-LNBD].
[11] Women’s Work” and the Hidden History of Computer Science and Engineering – News – SparkFun Electronics, www.sparkfun.com (2023), https://www.sparkfun.com/news/6411 [https://perma.cc/29CN-NR9D] [hereinafter Women’s Work].
[12] Id.
[13] A Woman on the Moon and Equality on Earth, Council on Foreign Relations (2019), https://www.cfr.org/blog/woman-moon-and-equality-earth [https://perma.cc/YCE7-7WSE].
[14] Becky Little, When Computer Coding Was a ‘Woman’s’ Job, History (2018), https://www.history.com/news/coding-used-to-be-a-womans-job-so-it-was-paid-less-and-undervalued [https://perma.cc/9WP3-77MT] [hereinafter When Computer Coding Was a ‘Woman’s’ Job].
[15] Jennifer S. Light, When Computers Were Women, 40(3) Technology and Culture 455, 469-70 (1999).
[16] Emma Goldberg, Women built the tech industry. Then they were pushed out., The Washington Post, Feb. 19, 2019, https://www.washingtonpost.com/outlook/2019/02/19/women-built-tech-industry-then-they-were-pushed-out/ [https://perma.cc/4DVR-34VZ] [hereinafter Women built the tech industry].
[17] The Secret History of Women in Coding, supra note 3.
[18] When Computers Were Women, supra note 15.
[19] Id.
[20] Id.
[21] Atkinson, Joe, From Computers to Leaders: Women at NASA Langley, NASA (August 24, 2015).
[22] Programming: when did “women’s work” become a “man’s world”?, Digital Future Society (2021), https://digitalfuturesociety.com/programming-when-did-womens-work-become-a-mans-world/ [https://perma.cc/7BJ9-Q2P6].
[23] Margaret Hamilton, Computer History Museum, https://computerhistory.org/profile/margaret-hamilton/?alias=bio&person=margaret-hamilton [https://perma.cc/M8R7-TDZ9] (last visited Mar 6, 2024).
[24] Harvey IV, Harry Gould, Her Code Got Humans on the Moon—And Invented Software Itself, WIRED, https://www.wired.com/2015/10/margaret-hamilton-nasa-apollo/ [https://perma.cc/Y596-SXVY] (October 13, 2015).
[25] Women’s Work, supra note 10.
[26] Id.
[27] Id.
[28] Women built the tech industry, supra note 15.
[29] The Secret History of Women in Coding, supra note 3.
[30] Id.
[31] Women built the tech industry, supra note 10.
[32] When Computer Coding Was a ‘Woman’s’ Job, supra note 9.
[33] Dame Vivian Hunt, Dennis Layton & Sara Prince, Why diversity matters, McKinsey & Company (2015), https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/why-diversity-matters [https://perma.cc/YY4F-96XN].
[34] Matthew Putman, Artificial Objectivity, Medium (2016), https://medium.com/@MatthewPutman/artificial-objectivity-c5233a8c453b [https://perma.cc/M4U2-RFXV].
[35] HeForShe summit discusses gender bias in AI and how to encourage male feminist allies, UN Women – Headquarters (2023), https://www.unwomen.org/en/news-stories/feature-story/2023/09/heforshe-summit-discusses-gender-bias-in-ai-and-how-to-encourage-male-feminist-allies [https://perma.cc/ZNE6-3ALU].
[36] P. Gender shades: Intersectional accuracy disparities in commercial gender classification, supra note 8.
[37] Shedding light on AI bias with real world examples, supra note 9.
[38] Pete Trainor, The Imperative of Diversity and Inclusion in the Ai Industry, and why I’ve said “no” today., Medium (2023), https://medium.com/@petetrainor/the-imperative-of-diversity-and-inclusion-in-the-ai-industry-and-why-ive-said-no-today-8ba37df9a17f [https://perma.cc/5W2H-LK4Q].
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