Lack of Diversity in the Computer

The Problems and Solutions with a Lack of Diversity in the Computer Science Field

Like other STEM professions in the United States, the computer science field is faced with diversity problems ranging from race to gender. The area lacks diversity. Two primary issues cause the lack of diversity in the computing world: the pipeline issue, referring to the lack of early access to resources, and cultural issue, which denotes biased workplace and discriminations within the profession. Technology and computing hold a great promise to address the world’s problems, both small and big, and shape the world around us. However, if people working in the computing fields do not equally represent populations and communities, the solutions they generate will not be representative either. With a lack of diversity in the computing world, technologies will emerge with unpremeditated bias and narrow acumen into the diversity of people intended to use or depend on them (Howard and Borenstein 1530). This study explores how lack of diversity plays in the computer science field and how the education systems can be modeled to address the problem. The researcher will give an overview on gender, race, and ethnic representation and then shift the focus into educational strategies and policies to bridge the diversity gaps in the computing field.


Diversity problem in the computer field is apparent in gender, race, and ethnic representations. Concerning gender representation, the field of computing and information science has more men than women. Study shows that women’s enrolment in computer science majors and completion of computer science undergraduate and graduate degrees has been lower. The effort to change the pattern has been inadequate, partly because of a lack of research on the pathways into and out of the computer science classes, particularly in community colleges (Denner, Jill, et al. 342). In the US, the proportion of women in the computer science major peaked in the mid-1980s but declining ever since. About 37.1% of computer science undergraduate degrees were awarded to women in 1984. However, the number declined to 29.9% in 1990 and 26.7% in 1998. A study by the Computing Research Association shows that less than 12% of bachelor’s degrees in computer science were awarded to women by the US Ph.D. granting institution in 2011. Besides, the number of women graduating with master’s degrees peaked in 2000 to 33% and declined to 27% in 2008 (National Science Foundation 2). Overall, the US’s female representation in computer science has declined for the past three decades. At the moment, women constitute 18% of all graduates with bachelor’s degrees in computer science. Further study has shown that women’s determination to pursue computer science as a major has declined with only 0.3% of women compared to 3.3% of men who planned to pursue computer science (Sax, Linda, et al. 258). Klinger and Svensson also noted wide gender gaps in the computing field, noting that only 12% of machine-learning scientists are women (Klinger and Svensson 2078). The findings indicate a gender gap in the field.

Besides the gender representation problems, there are also race and ethnic imbalances in computer science. In one study, Kunda (Klinger and Svensson, 2078) described the technology workers as a homogeneous group comprising white men in the late twenties to mid-thirties. Most computer programmers still comprise mostly young white and Asian males, depicting both race and gender imbalance. Large technology firms such as Google have also been in the limelight for biased recruitment, which also means biases in the computer programs they produce. Timnit Gebru, a renowned AI researcher and Google employee, claimed in 2020 that Google fired her for criticizing the company’s inadequate efforts to increase minority hiring. Gebru was worried about the biases the Google programmers created into the AI systems. In one of her studies, Gebru, a black woman, demonstrated how Google facial recognition programs were not working well with nonwhite faces (Klinger and Svensson, 2077). In 2010, there was a media outburst of cases where facial recognition algorithms could not quickly identify non-Caucasian faces (Howard and Borenstein 1524). A year later, Google made a faux pas, labeling Blacks as gorillas in the company’s new photos application for android and iOS, an application aimed to become an intelligent digital assistant. In 2016, the IA algorithm was used in an international beauty contest, the first of its kind, to select the most attractive contestants from a group of about 6000 contestants from 100 countries. Race also played a more significant role in this case concerning the algorithm thinking process as out of the 44 winners, 36 were whites (Howard and Borenstein 1525). Further studies show a disproportionate representation of race and ethnic groups in academics, which later translated into the workforce.

People of color are underrepresented in computer science, just like in other STEM courses. In the 2017-2018 school calendar, Black students constituted 9% of undergraduate degree awards, 13% of master’s degree awards, and 7% of research Ph.D. awards in the computer science field (Fry, Kennedy, and Funk, 4). The research further pointed out that 2.4% of new Ph.D. graduates in AI in the US 2019 were Blacks, and only 3.2% were Hispanic (Fry, Kennedy, and Funk, 5). Such findings raised concern as to whether lack of diversity in the computer science field, and related specializations such as AI and mathematics, both in the academic and workforce, contribute to rampant cases of AI algorithms, such as facial expression bias, as witnessed in many Google and other applications, and what should be done to correct the biases.

Education remodeling, through policies and programs, is seen as one strategy to increase the diversity computer science field, just like in other STEM courses. A simple method in the education setting to increase diversity is ensuring a culturally authentic classroom. For learners to truly connect with computer science, they must see themselves and their community reflected in the classroom materials. Achieving this is simple; it can be done by putting up simple posters of people sharing their backgrounds and who have professionally advanced in STEMP careers (Fields, Deborah Ann, et al. 21). In a study, Dempster, Onah, and Blair advocated increasing academic diversity inter-disciplinary computer science in higher education. Such include providing tailored computing modules to learners in their first year from academically diverse backgrounds and have selected a non-computer science discipline as their primary subject (Dempster, Onah, and Blair, 3).

Students can take core modules introduction to the program and computational thinking with python and JavaScript before selecting elective courses such as creating web applications, advanced python, making sense of data, digital crafting, and history and evolution of computing. However, flexibility in learning is critical to accommodate studying these courses alongside the student’s major. In such a case, blended learning is preferred, replacing lectures with online classes, slides, and quizzes, reinforced by face-to-face staff time in weekly workshops fashioned around collaboration (Dempster, Onah, and Blair, 5). This will increase overall learners’ computational thinking, which is becoming fundamental workforce skills in the digital era where every trade and profession is increasingly dependent on computer technology (Pearson 42).

Additionally, course instructors also play a critical role in encouraging women and students from minority communities to be interested in computer science. Faculty encouragement has been cited as a vital factor for women and minority groups’ retention in computer science at the college and university level. Respect from the course instructors is predictive of student satisfaction and long-term STEM objectives for both female and male students (Denner, Jill, et al. 343). Further study shows that when community college learners feel validated by the college faculty, they are more likely to intend to continue with the programs. Besides, advice and encouragement from individuals both at home and in school are also critical to helping female computer science students to develop their self-concept and desire to continue with the program. The support is also critical during the formative period in high schools. Having a role model or a mentor at the high school level is associated with success and persistence in undergraduate computer science (Denner, Jill, et al. 343). Besides, learners need opportunities and tools to apply the knowledge acquired to their lives. Theories such as project-based learning and constructivism suggest that the most effective method to get novice interest in learning a new topic is to hook them up with meaningful goals (Blikstein 421). Other educational programs could include inclusive summer and after-school programs, whether summer coding camp or robotics team. After-school programs are also creative ways of encouraging diverse groups of students to develop an interest in computer science (Blikstein 421).

Overall, there is a diversity problem in the computer science field, characterized by the underrepresentation of women and minority races and ethnic groups. Nonetheless, that can be corrected by remodeling our education systems to encourage culturally authentic classrooms, introducing non-computer students to basic computing skills alongside their majors, and initiative inclusive after-school programs to encourage more learners to develop an interest in the field.

Works Cited

Blikstein, Paulo. “Maker movement in education: History and prospects.” Handbook of technology education (2018): 419-437.

Dempster, Paul, Daniel Onah, and Lynne Blair. “Increasing academic diversity and inter-disciplinarity of Computer Science in Higher Education.” Proceedings of the 4th Conference on Computing Education Practice 2020. 2020.