Almost daily are news articles about women in tech. Among these on the day I wrote this post, for example, were an article in Marie Claire, the women’s magazine, called “How Much Have Things Really Changed for Women in Technology?” and another in India’s business newspaper Mint titled “Two kinds of pay gap in the IT industry: NetApp’s Mark Bregman.”
Both articles touch on several issues about women in tech, and STEM fields more generally; the cornerstone in each, however, is simply the number of women in the tech world—or the lack thereof, compared with men. This is a problem that has been explored since at least the mid-1970s in computer science (e.g., Montanelli Jr. and Mamrak 1976), longer for some other STEM fields. More recently this issue was highlighted last year, particularly in the media and public attention, when large tech companies like Google, Apple, Twitter, and Facebook released “diversity data” showing the dismal number of women and minorities among their employees. The articles also point to several issues seen as contributing to the disparities, including pay and hiring gaps for women, so-called “brogrammer” culture (involving frat-house-like sociality and performances of technical heroism, generally among men), and implicit biases shaping how women (and men) are perceived and judged.
As a former woman in tech—I pursued an undergraduate degree in computer science—I appreciate how this surge in public awareness and interest is helpful to many, particularly in relation to discussions about sexism and tech cultures. Through social media, blogs, and news articles people are sharing and discussing personal experiences and working to further raise awareness of, and gain support for, challenges women as a group face in tech. Tech companies and governments have also pledged a great deal of money towards “fixing” this problem.
It’s easy to understand this focus on the number of women, particularly among governments and tech companies. It provides a straightforward way to measure the problem and evaluate the success of solutions. Money and policies can be devoted to issues like pay gaps, companies and governments can promote and finance their tech and diversity programs (like this recent failed promotion by IBM), and bias training can be instituted to make everyone aware of their unconscious assumptions. An increase in the number of women suggests a solution must be working. This is clear in articles that discuss companies as having “solved” the “woman problem.”
Many of these solutions are desirable, researched and implemented with the best of intentions, and some bring at least qualitative changes, leading to things like an increased awareness of gender issues. Overall, though, these solutions are clearly not working. By their own standard of measurement, over thirty years of research and efforts have not led to any significant improvement for women in tech. In the US the number of women has actually declined (Ashcraft and Blithe 2010:14).
So why do they continue?
In his book The Anti-Politics Machine, anthropologist James Ferguson (1990) looked at how and why development projects in Lesotho were continually implemented despite also continually failing to achieve their goals. He outlines how political and economic issues are continually reconstructed and presented through development discourse as problems with technical solutions. And although the projects recurrently fail, they do work to increase governmental control in areas of development and hide the inherent politics of the changes that are made: the workings of what he calls an anti-politics machine. Focusing on women in tech works precisely the same way as the development discourse in Ferguson’s account.
Anti-politics and women in tech
The quantitative focus on women in STEM constructs a technical problem, requiring technical solutions and masking the politics involved: of who these solutions are for and what kinds of solutions are possible.
When talking about women in tech, we need to ask which women in tech? Many women of color working in tech have argued that they face different and greater challenges than white women (see, for example, here and here), which are often not addressed by the various solutions discussed above. This also says nothing about the challenges faced by men of color and other minorities.
And what about the categories of men and women themselves? These are taken as a clear-cut and measurable binary division. But this separation is as much a product of technological fields (and STEM more broadly) as it is a part of the process of evaluation, measurement, and improvement for women in tech.
As a doctoral student in anthropology, I’ve been conducting ethnographic and policy-based research on computer science education in Singapore, exploring the “making” of computer scientists. One of the things I’m looking at is how the focus on gender difference dovetails with a strong focus on categorization in computer science. A significant part of computer science knowledge deals with how to organize information. Within these organizational practices, men and women are continuously partitioned as opposing categories: in teaching examples; in program design; and even the conceptualization of mathematical problems. By repeatedly making these divisions, computing (among other STEM fields) hides and suppresses the many gendered identities that do not fall within these binary categories, never mind the many ways people’s identities change throughout their lives or even throughout their days. It also produces a reality where the divisions between men and women are made fact, materialized in infrastructures, technologies, and modes of thought.
Focusing on women in tech bypasses these questions. But what it also does is ensure that women (and men) are continuously counted, measured, and judged according to how they are contributing and performing (or leaning-in?). They are even judged on whether they are technical enough to be counted. This discourse operates as a machine that cleanses the messy “wicked” problems (Rittel and Webber 1973) of power, politics, and knowledge production, but also always finds new (and old) problems to fund, research, and improve. At the same time, the promotion of anti-political diversity and inclusion initiatives hides the ways these initiatives also maintain the status-quo in relation to gender and race in tech. Ultimately, who benefits? In most cases, it is not women or minorities . Research on gender and tech (and STEM fields) continues to be important, but what is needed are approaches that do not feed into this anti-politics machine.
 Particularly if things like pay equity were actually instituted and achieved.
 Tania Murray Li (2007) outlines similar processes in relation to her work on environmental NGOs in Indonesia, showing how they operate through a process of “rendering technical” whereby problems are framed in terms of solutions that NGO workers have the power to solve.
 Anna Vitores and Adriana Gil-Juárez point out “the persistence of the same question for years reinforces a feeling of ‘stability’… or even inexorability related to the topic of women in computing” (2015: 2). This stability provides further means to measure and compare over time, but seemingly achieves very little.
 Vitores and Gil-Juárez (2015), for example, suggest several alternative perspectives that open up new questions and do not reproduce the same assumptions as just focusing on women in tech and specifically the “pipeline” problem.
Ashcraft, Catherine, and Sarah Blithe. 2010. Women in IT: The Facts. http://www.ncwit.org/sites/default/files/legacy/pdf/NCWIT_TheFacts_rev2010.pdf.
Ferguson, James. 1990. The Anti-Politics Machine: Development and Depoliticization of Bureaucratic Power in Lesotho. Cambridge: Cambridge University Press.
Li, Tania Murray. 2007. The Will to Improve: Governmentality, Development, and the Practice of Politics. Durham, NC: Duke University Press.
Montanelli Jr, Richard G, and Sandra A Mamrak. 1976. “The Status of Women and Minorities in Academic Computer Science.” Communications of the ACM 19 (10): 578–81. doi:10.1145/360349.360361.
Rittel, Horst W. J., and Melvin M. Webber. 1973. “Dilemmas in a General Theory of Planning.” Policy Sciences 4: 155–69.
Vitores, Anna, and Adriana Gil-Juárez. 2015. “The Trouble with ‘women in Computing’: A Critical Examination of the Deployment of Research on the Gender Gap in Computer Science.” Journal of Gender Studies, 1–15. doi:10.1080/09589236.2015.1087309.
Samantha Breslin is a PhD Candidate in anthropology at Memorial University of Newfoundland. Her research looks at the “making” of computer scientists in Singapore, including knowledge-making practices, national and personal computing imaginaries, local and transnational networks of students, professors, and knowledge, and performances (and silences) of gender.