Despite alleged progressiveness, racism is prominent in the computer science industry and community. The lack of racial, ethnic, and socioeconomic diversity is linked to the ongoing oppression found in Eurocentric countries. Racist and classist terminology and practices in computer science are beginning to be challenged and changed by more progressive programmers. However, many programmers in the industry argue against these changes in practice. They argue that these practices have been used as long as programming has existed, so they should not change. The computer science and programming industries will be one of the last to provide equal opportunities in employment and advancement, due to the existing practices being upheld and rejected of change by senior members. In order to change the course of the field’s diversity, the exposure of computer concepts, both high and low-level, needs to be equal for people of all racial, ethnic and socioeconomic backgrounds.
The computer workforce is dominated by White people, similarly to every other workforce in the United States. Computer programming is a remote field, yet it still gets dominated by Whites from across the world. For example, Google’s tech department globally is 60% White, 31% Asian, 2% Black, 3% Latino, and 1% American Native/Alaskan Native and Native Hawaiian/Pacific Islander (United States Equal Employment Opportunity Commission). Computer science fields are heavily whitewashed, which makes Black, Indigenous, Latino, and Asian people feel unwelcome to join and participate.
Common terminology in computer science and programming, especially in database and web programming, are “master” and “slave”. In programming, these terms refer to the control that the “master” object has over the “slave” object, whatever those objects may be. Many criticize these terms due to their negative connotation and racially charged history in Europe and the United States. The change from “master” and “slave” to “leader”/”follower” or “primary”/”secondary”, or other similar viable terms, has become much more standard among developers . However, primarily White programmers criticize this with arguments stating that the “master”/”slave” terminology is the industry standard, the change is pointless, and that many databases, frameworks, and database services use this terminology (Django). Due to White dominance in the field, the “master”/”slave” terminology change has been very slow, as White privilege allows them to disregard the offensiveness of this terminology, despite its clear connotation relating to the ongoing enslavement of marginalized people in Eurocentric countries.
Despite this slow change in terminology, some of the largest web frameworks and database services have made the switch from “master”/”slave”, such as Django and Heroku. Additionally, Codecademy, a popular website dedicated to providing online programming courses for beginners in primarily web and database-intended programming languages teaches using “parent” and “child” terminology, rather than the offensive “master” and “slave” terms. This heightens the amount of new programmers in the field that use this less offensive terminology from the start of their hobby or career.
Marginalized groups participate in self-segregation, whether they are conscious of or it or not. Minorities will often seek out communities and groups that include other minorities, especially those in their racial and/or ethnic groups. This self-segregation is often caused by the danger and discomfort that marginalized groups feel when around people from oppressive groups. This is present in the tech field as well, “Since there is already a lack of African-Americans in the tech sector, it makes others more wary to join” (Giang). Not only does it make other minority students and workers wary of joining the programming and tech fields, but it also makes them feel uncomfortable. They self-segregate into areas and fields not only with their peers, but also with like professors and instructors. In 1994, only 0.25% of computer science professors in all of North America were Black (Williams). Students of color with teachers that are also racially oppressed have shown to be more successful, have stronger bonds, and experience less racially fueled microaggression and general aggression by their instructors than with White teachers (Rich). As shown in the recent harassment and attacks at the University of Missouri by White students against primarily Black students (mariehaileyy), being the only of one’s racial group can bring physical vulnerability.
Middle and upper class White people are defaulted to be more successful and interested in computer science starting at Kindergarten age. Academic exposure and peer exposure account for over half of the influential factors for pursuing a degree in computer science (Google), one of the fastest growing fields in the country, yet much of the population is not exposed to computer science concepts. The academic exposure to computer science is disproportionately pushed towards middle/upper class White students; lower class students and students of color are approximately 50% less likely to have or be aware of computer-based learning opportunities at their school than their middle class and White counterparts (Gallup, & Google). For the learning opportunities that they do have in this field, very few of them actually pertain to core computer science concepts (Google). Principals and superintendents, in general, do not believe that these computer courses are critical to student success, even though a large portion of teachers, students, and parents, of all racial and socioeconomic backgrounds, disagree with them (Gallup, & Google). Computer programming and science is a generally known field; it is simply too daunting for people who do not come from high-level technological backgrounds to grasp the concepts. If more schools would focus on implementing effective computer science and programming courses, more students would become interested and knowledgeable about computer science. Therefore, their degree selections, options for higher education, and post-high school occupations would be expanded.
Early exposure to computers and computer concepts are crucial to success in computer science fields. According to Google, “Students with increased exposure to computer technology are more confident in their own skills and more likely to consider learning computer science in the future”. This study also showed that only 75% of Latino students, 85% of Black students, and 98% of White students have access to a computer with internet. This lack of internet access for Black and Latino students creates a huge gap between the number of brown students and White students that may grow an interest for computer science fields.
Racist narratives about the abilities of various racial groups in scientific, technological, engineering, and mathematic (STEM) subjects reduce the number of participants for each race in these subjects. Self-perception of ability in STEM subjects directly correlates with the racist narratives, for example: “Asians are good at math”, that are culturally prominent in the United States. A study by Niral Shah found that 57% of high school students believed that Asian people are genetically better at mathematics than other races, despite the fact that approximately 83% of the students believed that mathematical abilities were not related to genetics (6). Students in this study believed that their race was to blame for the ability, or inability, to exceed in mathematics, despite their actual ability (9-16). This self-perception of different racial groups’ abilities in mathematics causes them to believe they will not succeed in a computer science department, due to the high amount of mathematical ability needed for the field. When these racist narratives are eliminated from the United States culture, the confidence in math among Black, Latino, and southeast Asian students will greatly increase, and thus, their involvement in computer science will increase as a whole for their race.
Lower socioeconomic status and segregation contribute greatly to the lack of people of color in computer science. Ongoing neighborhood and educational segregation based on race produces students that are more focused on the events in their neighborhood than their classes. They are less likely to actually learn the class content, and are more likely to miss school times. Neighborhoods with higher minority populations have higher crime rates, causing these students excessive stress and distractions that hinder their learning environment.
Health insurance coverage is critical to the success of students of all races and all socioeconomic statuses. More than half of non-Asian people of color are either not covered by medical insurance, or are covered by public medical insurance (Artiga et. al). This lack of health coverage results in more and longer illnesses that prevent the school attendance needed to be successful. Schools in lower-income areas have much higher dropout rates due to poor quality of teaching, racial prejudice, environmental factors outside of school, and lack of peer motivation and influence. All of these factors result in a large number of students that have no chance of attending higher education, or becoming interested in high-level technological work fields.
There is a growing number of programs for Black and Latino individuals, especially female-identifying, who are interested in the computer science fields. Black Girls CODE is a primary example of one of these progressive and empowering programs that are directed at female Black youth interested in computer science. This program, founded by a female Black programmer herself, promotes underprivileged Black women and teens to learn computer science concepts from all existing levels. Of the other existing programs, this is one of the few that focuses on many different aspects of programming, from web-based front-end and back-end, to game development, and basic scripting (Black Girls CODE). There are many other programs existing for empowering women and people of color in the technological field, such as Code for Progress.
Technology is needed in social justice and civil rights activism just as much as any other workforce. With this ever-growing need for technological knowledge is a need for programs to teach these previously uninterested activists to code. Code for Progress fills this need by appealing their coding programs to adult individuals interested or involved in civil rights activism. Their one-year course, which takes place in Washington, D.C., continues to promote programming diversity and breaking the digital racial divide (Code for Progress).
The availability of the Code for Progress program is a major issue, though. The program suggests already existing-backgrounds in basic computer concepts, and some attributes that are typically gained through college or other higher education. On top of these issues, most working people do not have the free time needed in order to participate in this program. Code for Progress, Black Girls CODE, and other similar programs, are just the beginning of the elimination of racial discrimination in the computer science field.
Generations of institutional racism and segregation cause people of color and lower-income status to have less college/advanced education and high-level technological backgrounds. This results in less peer influence and pressure to attend college or pursue a degree in computer science. On top of that, people in these situations often have great stress due to heightened neighborhood violence, police activity, and crime. Many Black people in higher education have shown interest in non-technological social and government work over technological work (Giang). However, this can be attributed by over 50% to the lack of societal pressure and academic exposure to join computer science programs. The ongoing lack of educational experiences available to class and racial minorities contributes to shrinking opportunities for people of these groups in computer science.
- The Annie E. Casey Foundation. “Race Matters: Unequal Opportunities in Education”. The Annie E. Casey Foundation. Baltimore: The Annie E. Casey Foundation. 2006. Web. 12 Nov. 2015.
- Artiga, S., Smedley, B., Ng’andu, J., Ko Chin, K., Huang, P., Iyanrick, J., & Goodwin, A. “Health Coverage by Race and Ethnicity: The Potential Impact of the Affordable Healthcare Act”. Kaiser Family Foundation. The Henry J. Kaiser Family Foundation. Washington, DC: The Henry J. Kaiser Family Foundation. 13 Mar. 2013. Web. 14 Nov. 2015.
- Black Girls Code. Black Girls CODE, 2014. Web. 28 Nov 2015.
- Code for Progress. Code for Progress, 2014. Web. 28 Nov 2015.
- Django/Django, Pull request #2692. “replaced occurrences of master/slave terminology with leader/follower”. 20 May 2014, Web. 12 Nov. 2015.
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- Hailey (mariehaileyy). “Racist cult in speaker circle blue pick up truck with white guys and no license plate circling there car around us dont go to campus [sic]”. 10 Nov 2015, 6:42 PM PST. Tweet. 22 Nov. 2015.
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- Rich, Motoko. “Where Are the Teachers of Color?”. The New York Times. 12 April 2015: SR3. Print.
- Shah, Niral. “Mixed-race Learners and the Reification of Mathematical Ability as Genetic”. National Council of Teachers of Mathematics. The Westin Boston Waterfront, Boston, MA. 13 April 2015. Conference Presentation.
- United States Equal Employment Opportunity Commission. “2014 Employer Information Report”. 2014 Report. Mountain View, CA: Google, Inc. Google Diversity. Web. 18 Nov. 2015.
- Williams, S. “Computer Scientists of the African Diaspora”. Buffalo: Mathematicians of the African Diaspora (MAD). Buffalo University. 25 May 1997. Web. 12 Nov. 2015.
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