This gender and race researcher explains why techies don't have to be Trekkies
Through her work at UW's Stereotypes, Identity and Belonging Lab, Sapna Cheryan breaks down stereotypes about tech employees and Asian Americans.
This conversation has been lightly edited for clarity.
I went to a science-focused high school. I related to and liked the science and the scientific method, especially experiments, but I also had what I saw as kind of extracurricular interest in diversity. It was in a lot of books I was reading and things I would think about and talk to my friends about — a lot of it would relate to gender and race. I'm sure that it's due in a large part to growing up as a girl and a second-generation American.
My parents emigrated from India, so I was kind of conscious of what it meant to be someone who wasn't white, and someone who was the daughter of immigrants in the ’80s. When I was growing up, there wasn’t that much on Asian American studies, but there were some authors. Like The Joy Luck Club had come out and things like that, so there was some kind of an awareness of a shared Asian American experience. I read a lot of African American history and African American literature, both extracurricularly and classes I had taken in high school.
I went to Northwestern as an undergrad. I was both a psychology major and an American studies major. And as an American studies major, I focused mostly on race, and of all the classes on race, a lot of them focused on the African American experience. So I was pretty familiar from an academic standpoint with that experience, with the books and the texts and things like that.
I took this class on Asian American political science, and I remember being really interested in the way that the Asian American experience is, in some ways, very similar to the African American experience, but in other ways is very different. The class allowed me to have words and terms for things I'd experienced, like the perpetual foreigner syndrome and the model minority myth, and other kinds of stereotypes that I had personally experienced that I hadn't had the academic training to know how to talk about, or why they were happening or how those things could be used as a kind of wedge between different racial groups.
When I got to graduate school, I realized that the way that people were talking about Asian Americans, at least in psychology, was very much kind of seeing them as proxies for Asians. The Asian American experience in psychology was basically the Asian experience. So when people studied Asian Americans, a lot of times they would study them as being culturally different than Americans, and look at the ways that Asian culture and American culture kind of differed and things like that.
That ended up becoming like a major line of work — I still do work on that — on the perpetual foreigner stereotype and how Asian Americans contend with it personally. I really wanted to establish that Asian Americans had a racial experience in the U.S. to psychologists, who didn't seem to be seeing us that way.
I was more interested in race than gender, but when I moved to Stanford for grad school, [I noticed] a lot of people in psychology had been studying girls and math. That was kind of a major focus in psychology: How do we explain performance differences in math classes and math tests?
Being at a high school that was very engineering, tech and science focused, to me it was really clear that the disparities were in computer science. That was such a huge, emerging field. Gender disparities are way worse in computer science than they are in math, at least at the undergrad level. And so nobody had really been doing that, and I again just thought [my eventual research on this] would be a small thing.
Sapna Cheryan in Guthrie Hall at the University of Washington on April 30, 2019. Cheryan is an associate professor of psychology and recently served on the Barbie Global Advisory Council. (Photo by Dorothy Edwards/Crosscut)
Both my projects [on identity and gender] basically started with this idea: Psychology is not capturing my experience or what I know from the data or from the things I've learned in other places, and I just want to tell them that.
But what happened specifically that got me interested in the stereotypes of computer scientists is that I decided to get a job my first summer in graduate school. I interviewed at a bunch of different tech companies. I was going to do a user research job just as an internship to see what it was like to work in Silicon Valley, in case I decided to not to go into academia.
I saw that the conference rooms were named after Star Trek ships or something like that. My vision of the company was cubicles, kind of nerdy, [with] action figure stuff around. I got the job offer, and the job would have been really cool. I would have been working on cellphones in the early 2000s, so it would have been really fun and interesting. They were going to pay me way more money than my graduate stipend.
But I ended up thinking, "Oh no, I don't think I'm going to enjoy this place.” I just don't think I'm going to fit into this kind of culture environment." And I realized that I made that decision based on the environment, like those Star Trek cues, without even asking anybody about the culture.
I ended up taking a job that had a totally different environment. It was Adobe, so it was a graphic design company, and they had a beautiful, colorful lobby. They had a gym and a cafeteria. I met only men in both of my interviews, and I was actually kind of less interested in the work that Adobe was going to have me do. I had to commute further, and they gave me a lower offer. But I took the Adobe job, because I just felt like, “Oh, I'm going to have a good summer here. I really feel like I can relate to what this culture is like, and I think I'm going to fit in.”
Then I wondered, well, those stereotypes are really just about who computer scientists are and the culture of these tech companies. I had this image of this geeky stereotype, and when I saw that, I thought, “I don't want to be here.” But you could also use those environments to change a stereotype and give a different image of computer science.
Are these current stereotypes part of the reason that women are underrepresented? And, if so, can you change the stereotypes to make a broader image that would be more appealing to a wider group of people? And if you do that, can you get more girls and women to express interest in computer science?
There was a lot of interest almost immediately, because there weren't very many people studying the gender in tech problem [when I began]. But people were realizing that it was a big thing, and it was such a weird thing, because computer science is such a new, modern field. It used to be majority women when it first started.
If any field should not have gender disparities, it should be a field that hasn't been around for that long and one that had the advantage of having women as the first programmers on a large scale during World War II. So I think there was a lot of interest in that, and there was a lot of funding that was available for that one.
After I published my first paper, I was, OK, there are still so many questions, there's so much interest. I'm getting funding for this, and I felt like I still am motivated with this line of work, because I still feel like people don't really understand the problem the way they should. I keep publishing papers basically out of frustration. Even though people have been talking about it for a while, I feel like there's a lot of misconceptions about why women are not in theses fields.
For my race and immigration work, I think the experience is almost the opposite. I think that there's a general interest in race. But studying Asian Americans is still not seen as a superimportant issue, because people think that Asian Americans are doing pretty well, and that if we're going to study race, we should study groups that are more marginalized.
I understand that if we have limited resources, maybe we want to dedicate those resources to helping the groups that need it the most. But, on the other hand, Asian Americans are a very diverse identity. There are a lot of Asian Americans who are also overrepresented in the lowest socioeconomic strata in America, so by homogenizing the group and saying they're all doing well, we kind of ignore that. And from a psychological perspective, studying a group that's stereotyped so differently really allows you to learn a lot more about the nature of discrimination and the way that groups relate to one another.
It's not really accurate to just say that all these groups are facing the same barriers, and that if we just study one group, we can learn about all the groups. There is value in having people study all the different groups, so we can learn more about how discrimination operates and how it maintains itself, and how it takes on these different forms, depending on all these contextual factors and who the group is and things like that
I would say it’s much harder to get funding for [my Asian American identity] work. I wouldn't say it was active pushback, but I would say that it's kind of a devaluing or a lack of recognition of why that work is as important as work on other groups, which leads to less funding.
I'm the principal investigator of the [Stereotypes, Identity and Belonging Lab] that I started when I got to UW. The mission of the lab is to do mostly experimental research and behavioral experiments on understanding the experiences of folks that are underrepresented in certain environments and trying with an eye toward solutions, trying to remedy that underrepresentation.
Right now, people are very focused on what we can do to help and support and train women when it comes to gender disparities. And the way I see the problem is, really, we need to take the magnifying lens off women and what women need to change about themselves and put it on the field and these kind of broader, cultural elements in society, and figure out how we can change those things so that women can be themselves and be in these fields.
I had a one-year position [with Mattel]. They had a global advisory council who advised them on different questions related to the Barbie brand. It was a cool opportunity to take what I knew from the research and apply it to toys, which is a superimportant domain with gender, since toys are so gendered.
We were part of the council when they did a robotics Barbie. That was one that just got released as Barbie Career, last year's Robotics Engineer. That was really fun because that's a field in which there are very few women, and I was really happy to see that they picked robotics as opposed to a biologist or a chemist, because there's a lot more women in those kinds of fields.
These ideas about gender are formed, they're solidified, and they're created in childhood. In some ways, they’re even more intense in childhood because of things like segregation of toys and clothes and things like that. A lot of times they're even more extreme than they are for adults. The University of Washington has some work on children as young as 6, looking at their stereotypes of these different fields. They don't have broad stereotypes about math and sciences. [But] when we ask about programming and robots, which is kind of our proxy computer science and engineering at that age, they flip [and we see a] stereotype that boys are better than girls.
That does not exist with math and science. It’s very interesting, because it mirrors what's happening at the undergrad level, where math and sciences like biology and chemistry are basically gender-balanced in degrees, but computer science and engineering are still very male-dominated.
We've thought a lot about how to address those stereotypes and why they exist. I think a lot of it is kids are really good at picking up on what they see in their world. When they see who has robot toys [or] who's in books and TV shows, I think they get used to these associations.
Kids are kind of rational that way. We have to really talk to them about the world that they're engaging with and not pretend like just because we don't talk about it, they don't see it.