Op-ed: The rocky road of diversifying tech
By Irina Hallinan, MEng ’23 (EECS)
This op-ed is part of a series from E295: Communications for Engineering Leaders. In this course, Master of Engineering students were challenged to communicate a topic they found interesting to a broad audience of technical and non-technical readers. As an opinion piece, the views shared here are neither an expression of nor endorsed by UC Berkeley or the Fung Institute.
Have you ever used Google, talked to Siri, or driven a car? Chances are, the software involved was made by a man. If English is your first language and you are a man, Siri recognizes your speech 92% of the time. For native English speakers who are women, the recognition accuracy is 79%¹ . I noticed gender disparity in tech when I arrived at my first team meeting at Hewlett-Packard. Out of ten software engineers, I was the only woman. Five years later, at NASA Ames Research Center, out of eight engineers, I was the only woman. Currently, I am pursuing a master’s degree in Electrical Engineering and Computer Science (EECS) at UC Berkeley. In the class of 2023 there are 29.7% female-identifying students² . The underrepresentation of women in tech is an ongoing issue³ . Following the data on gender disparity throughout the education system, the gap in Science, Technology, Engineering, and Math (STEM) starts in middle school.
Today, women are as likely as men to own personal electronic devices and to use computer software. From voice assistants to vehicles to medical equipment, we interact with multiple devices that run on software. Although women made 50% of the total workforce in STEM disciplines in 2022⁴ , women held 21% of 5 million software engineering jobs in the United States⁵ . Since Google started releasing company demographics data, the number of women working in tech departments in the United States increased from 21.8% in 2015 to 28.9% in 2021⁶ .
“If the pace of progress stays the same, it’ll take us another 20 years to reach equal representation of men and women in tech departments at Google.”
Since there are fewer women software engineers entering the field, there are fewer lead software engineers, managers, and software architects. Considering there are fewer women who teach computer science at every level of education, some women may not encounter a role model who’s a woman until later in their careers. For example, a look at the UC Berkeley EECS faculty gender statistics reveals an underrepresentation of women: out of 205 faculty members, 34 are women and 171 are men⁷ (Figure 1).
Achieving equal opportunity is not easy. I interviewed friends, family, and acquaintances who currently work or previously worked as software, mechanical, and electrical engineers. Gathering data from nine men and six women, the gender makeup of people I know agrees with the demographics data at Google and the software engineering industry (Figure 2). The sample of people I interviewed is not large enough to be statistically significant; nevertheless, the data showed a similar pattern of underrepresentation of women, transgender, and non-binary gender people.
Gender diversity in tech matters not only for employment but for decision-making as well. Modern-day companies rely on algorithms to make decisions such as who gets invited to a job interview, who gets fired, or who gets a loan on a house. Algorithms rely on data to make predictions. If the data isn’t representing the people who are using the program equally, the program may contain biases. Understanding that algorithms may be biased is the first step. The second step is fighting that bias with action. Creating more diverse data sets and more diverse technical teams will help make software products and their consequences more fair for everyone.
Imagine a classroom with 100 students. In elementary school, 50 girls and 50 boys are in a math or a science class together. In high school, the number of girls taking STEM classes drops. In a high school AP Computer Science class, there are 68 boys and 32 girls⁸. A similar trend endures throughout higher education⁹,¹⁰,¹¹,¹²(Figure 3). As a volunteer judge at a middle school robotics competition, I talked to girls during the event. Many of them asked me about being a software engineer at NASA and about being a woman engineer. One strategy to increase diversity in tech is to support girls in STEM clubs in local middle schools.
The overall trend towards equal representation of men and women in tech is upward, but the pace of progress is lagging. If we want Siri to respond with the same accuracy regardless of who’s speaking, we must advocate for diversity, starting with middle schools. Technology and its users will benefit from having a diverse set of engineers. In order to see gender equality in tech before 2050, we can start now by supporting middle school STEM clubs. Additional strategies include expanding high school computer science education and influencing our places of work to have inclusive hiring practices. Further research is needed to identify methods to overcome systemic gender barriers in the STEM professions.
References
¹ Joan Palmiter Bajorek, “Voice Recognition Still Has Significant Race and Gender Biases”, Harvard Business Review, 2019. https://hbr.org/2019/05/voice-recognition-still-has-significant-race-and-gender-biases
² Fung Institute, “UC Berkeley MEng Class of 2023 Profile”, Medium, 2022. https://medium.com/the-coleman-fung-institute/uc-berkeley-meng-class-of-2023-profile-3ca5c317db7d
³ “What percentage of software engineers are female?”, Celential.ai, 2022. https://www.celential.ai/blog/percentage-of-female-software-engineers/#:~:text=According%20to%20our%20AI%2Dp owered,put%20that%20number%20into%20perspective.
⁴ “Women in the labor force: a databook”, U.S. Bureau of Labor Statistics, 2019. bls.gov/opub/reports/womens-databook/2020/home.htm
⁵ “Software engineering demographics”, Wikipedia, 2022.
https://en.wikipedia.org/wiki/Software_engineering_demographics
⁶ “Google 2021 Diversity Annual Report”, Google, 2021.
https://static.googleusercontent.com/media/diversity.google/en//annual-report/static/pdfs/google_2021_diversity_annu al_report.pdf?cachebust=2e13d07
⁷ “Faculty List”, UC Berkeley Electrical Engineering and Computer Science Department, 2022. https://www2.eecs.berkeley.edu/Faculty/Lists/faculty.html
⁸“Young women set record as CS gender gap continues to shrink”, Code.org, 2020.
https://codeorg.medium.com/female-students-ap-cs-state-of-cs-report-b38ff039eada
⁹ Carly Berwick, “Keeping Girls in STEM: 3 Barriers, 3 Solutions”, Edutopia, 2019. https://www.edutopia.org/article/keeping-girls-stem-3-barriers-3-solutions
¹⁰ Suzanne Blake, “The Rate Of Female Comp Sci Majors Is Still Dispiritingly Low. What Can Be Done?”, GrepBeat, 2022. https://grepbeat.com/2022/04/13/the-rate-of-female-comp-sci-majors-is-still-dispiritingly-low-what-can-be-done/#:~:tex t=Although%20women%20now%20far%20outnumber,degrees%20in%20the%20United%20States.
¹¹ National Center for Science and Engineering Statistics, “Women, Minorities, and Persons with Disabilities in Science and Engineering”, National Science Foundation, 2020. https://ncses.nsf.gov/pubs/nsf21321/report/field-of-degree-women
¹²“Analyzing the Gender Disparity Among Higher Academia in Computer Science / Engineering”, Medium, 2020. https://towardsdatascience.com/analyzing-the-gender-disparity-among-higher-academia-in-computer-science-engine ering-2d8cecefa76e
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Edited by Mary Tran.
