“Whenever a Black person or group used White people as a standard of measurement and cast another Black person or group as inferior, it was another instance of racism.”
― Ibram X. Kendi, Stamped from the Beginning: The Definitive History of Racist Ideas in America
Our vision at Charlottesville Women in Tech and Tech-Girls is “to bridge the gender gap in tech by providing a safe and welcoming environment for women and girls to connect, learn and collaborate in Charlottesville.” We are well-versed in gender equity issues in tech and we have shared a lot about how we got here, highlighted women and girls in tech who are role models for the future, and offered a variety of solutions for changing the equation. We recognize that racism and the patriarchy are two sides of the same coin that have systematically diminished the value of women, especially Black women. We need to do better ensuring that we focus on both. So what makes addressing racial equity in tech different?
First, let’s look at the numbers. There is, we know, a wide gender gap in computer science education. Even though today, more women than men earn college degrees and more Black people are earning college degrees then they did in 1991, white men are dominating college computer science departments (Women and Minorities in Tech, By the Numbers, Wired, 2019).
This computer science education problem starts in K-12. In Barbara Ericson’s annual study of AP CS courses,which is a strong predictor of taking CS in college, only nine states had more than ten Black women pass the AP CSA exam in 2019. The K-12 Computer Science Framework shares some more sobering statistics:
- 44% of 12th graders attend high schools that offer any computer science courses. Students with the least access are Native American, Black, and Latino, from lower-income backgrounds; and from rural areas.
- 21.9% of students taking the 2015 AP® Computer Science A exam were female, the worst female participation rate of all the AP exams.
- 3.9% were Black or African American, 9% were Hispanic or Latino, and 0.4% were American Indian.
There are gaps in the racial diversity of tech companies (The future of diversity and inclusion in tech, TechCrunch, 2019) which do not represent the demographics of the U.S. population: 60% white, 13% Black, 6% Asian and 18% Hispanic (U.S. Census Bureau, 2019). There are pay gaps in the tech field. Black men and Hispanic women earned 91 cents compared to White and Asian males, but Black women are the most affected, earning an average of 89 cents on the dollar (Pay gap narrowing in tech, Black women most affected: Study, Dice, 2019).
How does racism play a role in these numbers? Seeing and understanding racial discrimination can be challenging, especially if you are part of the dominant culture. “Race and ethnicity are difficult topics to discuss. They evoke feelings of guilt and defensive reactions from those in the majority, as well as anger and frustration from those in the minority” (Addressing institutional racism within initiatives for SIGCHI’s diversity and inclusion, ACM, June 2020).
Racism is multi-faceted and strongly embedded in our culture. In her Critical Race Theory session at the Computer Science Teachers’ Association (CSTA) conference this month, Shana V. White shared this image of the four dimensions of racism: institutional, structural, interpersonal and internalized.
- Institutional racism creates policies and practices that reinforce racist standards within a workspace or organization. Institutional racism results in racial stratification and disparities in employment, housing, education, healthcare, government and other sectors.
- Structural racism maintains and upholds racist policies across multiple institutions and society like zoning policies keeping families that can’t afford single-family homes out of high-performing school districts, tax policies that prevent Black wealth accumulation and mass-incarceration practices that remove parents from children’s homes and strain those left behind (The banality of racism in education, Brookings, 2020). The technology that we build also perpetuates racism through facial recognition technologies that target criminal suspects on the basis of skin color, automated risk profiling systems that disproportionately identify Latinx people as illegal immigrants and credit scoring algorithms that disproportionately identify Black people as risks (Of course technology perpetuates racism, MIT Technology Review, 2020).
- Interpersonal racism occurs between individuals. This is a bias that occurs when individuals interact with others and their personal racial beliefs affect their public interactions. This can take many forms from outright prejudice and discrimination to unconscious bias and unchecked privilege that result in microaggressions.
- Internalized racism lies within individuals. This type of racism is made up of our private beliefs and biases about race and is influenced by culture.
Still, it’s hard to see racism when you are not impacted by it. Put another way, “invisibility, with regard to Whiteness, offers immunity. To be unmarked by race allows you to reap the benefits but escape responsibility for your role in an unjust system,” writes Ruha Benjamin, author of Race After Technology. Robin DiAngelo, author of White Fragility, offered some examples for “Seeing the Racial Water” in her 2019 NCWIT keynote:
- Children notice race very early, and they also notice the differential value assigned to race.
- You can get through teacher education (and lots of other disciplines) without ever discussing racism.
- Racism is a system, not an event and none of us are exempt from its forces.
- The status quo in this society is racism, it is the default setting.
As a country, we are just beginning to reckon with the impact that systemic racism has had on all facets of our society. Meanwhile, Dr. Tychonievich, Assistant Professor in UVA CS Department, shares that groups underrepresented in computer science and technology, like Black women and men will continue to face side effects that help maintain the status quo:
- Implicit bias — Kirwan Institute for the Study of Race and Ethnicity defines implicit bias as, “the attitudes or stereotypes that affect our understanding, actions, and decisions in an unconscious manner. These biases, which encompass both favorable and unfavorable assessments, are activated involuntarily and without an individual’s awareness or intentional control.” Dr. Tychoniech notes that this especially comes into play when your conscious mind is overtaxed and busy, so your subconscious treats “expected” as a proxy for “good”.
- Stereotype threat — manifests as inhibiting doubt and high-pressure anxiety in the mind of someone who is part of an ethnic, racial, gender or cultural group with a negative stereotype associated with “people like them”.
- Pigeonholing — roles are assigned according to stereotypes and manifests when labor is divided based on implicit bias, most experience or picking who you know best.
- Imposter Syndrome — a feeling like you do not belong and are faking it and manifests when culture says “people like you” don’t belong.
- Differential Preparation — education or the workplace environment are “overfit” to the experiences of the majority.
This is the first in our series of three blog posts about Racial Equity in Tech. In part 2, we will highlight some pioneers who are role models that we can look to for guidance on addressing racial equity in tech. If you want to take a deeper dive into exploring racism, we hope you will join us August 12 for the CWIT Book Club Discussion. We will be discussing two books: Stamped From the Beginning by Ibram X. Kendi and Stamped: Racism: Antiracism and You by Jason Reynolds and Ibram X. Kendi (written for young adults). You can also check out the resource list below to learn more about issues related to racial equity in tech.
- Addressing institutional racism within initiatives for SIGCHI’s diversity and inclusion (ACM, June 2020)
- Barbara Ericson’s analysis of the 2019 Advanced Placement CS data (Computing Education Research Blog, 2020)
- Critical Race Theory (Shana V. White, CSTA session, 2020)
- CS Teachers, It’s (Past) Time To Learn About Race (Dr. Mark Guzdial, University of Michigan, 2020)
- Dismantling the 4 Dimensions of Racism (Nicole Bedford, 2020)
- Of course technology perpetuates racism (MIT Technology Review, 2020)
- Kiran Institute for the Study of Race and Ethnicity (Ohio State University, 2015)
- Microagressions: The Game! (Dr. Colleen Lewis, 2018)
- Pay gap narrowing in tech, Black women most affected: Study (Dice, 2019)
- Race After Technology (book by Ruha Benjamin, 2019)
- Seeing the Water: Whiteness is Daily Life (video with Robin DiAngelo, 2016)
- The Color of Our Future: An Online Conversation Series on the Empowerment and Inclusion of Black Women & Girls in Tech (NCWIT, February 2020, videos)
- The future of diversity and inclusion in tech (TechCrunch, 2019)
- Women and Minorities in Tech, By the Numbers (Wired, 2019)