To what extent has a lack of diversity influenced the development of the internet? How does this lack of diversity harm societies and individuals?

Many internet users believe the Internet is a cyber world where equality comes foremost. Any users regardless of their age, gender, socio-economic level, ethnicity, or beliefs are entitled to disseminate, circulate and receive any sort of information. The growth of the Internet enables individuals to possess the freedom of speech and the ability to gain full control of their use of the internet, enabling every person’s voice can reach out publicly wherein reality it cannot be held. These factors promote users to view the internet as diverse in the case of transnational online social interactivity; however, since the beginning of the internet’s existence, it demonstrates otherwise. Racism, bigotry towards ethnicities, gender inequality as well as oppression towards minorities have not failed to sustain within the cyber society and thus the everlasting development of the Internet, namely Metaverse, the current augmented reality fears specific minorities speaking of racial inclusivity. This essay probes into diversity intersecting both gender and race as to this modern date need to take into account the globalised nature of increased immigrants and LGBTQ.


“School diversity many hands held together” by Wonder woman0731 is licensed under CC BY 2.0.

The examination of the history of the internet outlines the absence of gender and ethnic fairness as proposed by Alegria and Branch, the population of non-African American men in “supervisory positions” surpassed the rate of 90% in the tech sector in 1960 (2015 as cited in Alegria, 2020, p. 3). Fortunately, the percentage of women gradually rose in this sector, yet those job occupiers were only non-African Americans made up from 1% in 1960 to 25% in 1990 and merely an estimate of 6% was “U.S. born ethnic/racial minority men or women in both 1980 and 1990” (Alegria and Branch, 2015, as cited in Alegria 2020, p. 3). The gender disparity under this sector indicates how institutions in this timeframe rarely recognised women to be assigned to technical or computer engineering occupations as these types of jobs were mainly possessed by and stereotypically considered suitable for men than women, leaving women and girls of colour at the lowest part of the hierarchy in this job sector (Enmerger, 2010 as cited in Alegria, 2020). Most Black women who are already in tech jobs, including students who are enrolling in “Science, Technology, Engineering, and Maths (STEM)” work regularly encounter alienation, receive precariousness in their proficiency as well as “racial micro-aggression” (Brown et al., 2016; Carter-Sowell & Zimmerman, 2015; McGee & Bentley, 2017 as cited in Alegria, 2020, p. 6). The type of work roles given to women also signifies stereotypically and racially designated positions where non-African American women are easily appointed to take “managerial positions due to their recognition of ‘people’ skills” whereas, African American and Asian women can only achieve this opportunity when after their applications explicitly outline their “training, degrees, and strategic job changes” (Alegria, 2020, p. 6). To this modern date, it is unsurprising to see that solely 3% of Black women out of 26% of total women are employed in the computing labour force in 2021 (NCWIT, 2022).

GOOGLE — Algorithmic bias to algorithmic oppression

“Google” by Cesar Solorzano is licensed under CC BY-NC-ND 2.0.

Noble (2017) argues Google’s search engine algorithms incorporate racial and sexist bias, particularly jeopardising Black women and youths due to the lack of opportunity for these minorities recruited in tech work as well as the ability to make decisions which worries that the falsified portrayal of ethnic minorities becoming normalised and maintained due to 73% of internet users considering that search engine delivers almost or entirely valid information. Despite Google’s search results being ranked based on the number of users’ clicks combined with the commercialised paid advertisement, the fundamental issue of racism and oppression towards these groups of minorities stems from Google’s adoption of a “direct mapping of old media traditions into new media architecture” as most conventional media commonly produced unfavourable or stereotyped representations of African Americans (Noble, 2018, p. 24). The utmost problem of the sustainment of prejudice and bigotry towards race and gender on Google Search is due to “big data” that incorporates “decision-making protocols” such as “deep machine learning” are built to benefit the dominant business group that is inevitably intertwined with “global economic and social” imbalance (Noble, 2018, p. 29). This type of machine operates by utilising “algorithms” that imitate “human thinking” which unavoidably embodies bias outlook (Noble, 2018, p. 29). The prevention of algorithms that misrepresent races on the Internet is an utmost challenge for ethnic minorities due to their insufficiency in “economic, political and social capital” (Noble, 2018, p. 26). It is also a huge obstacle for people of colour to obtain “leadership” roles as well as the capability to be in charge of “decision-making tasks” due to the nature of tech occupations that are inclined to favour White ethnic individuals (Alegria, 2020, p. 8). Google 2021 Diversity Annual Report presents 17.8% of White women are assigned to leadership roles in the Google workforce whereas only 1.3% comprise Black women whilst no figures are given for African American women. These factors obstruct women and people of colour’s ability to intervene, perpetuating gender and racial inequality as well as “algorithmic oppression” due to algorithmic prejudice which can pose a risk of “discursive, financial and even physical harm” (Benjamin, 2019; Noble 2018 as cited in Alegria, 2020, p. 9).



Up to this contemporary world, it is still evident that new technologies incorporate algorithmic bias and thus many researchers are concerned regarding Metaverse’s digital components and algorithmic bias’s foreseeable impact on marginalised and oppressed groups of minorities. The first issue that emerged after the arrival of Metaverse is that the cost of “digital avatars” varies depending on the “race, gender and skin colour” (Egkolfopoulou & Gardner, 2021). Facebook has been endeavouring to tackle all sorts of problems in relation to aggressive pressure or intimidation online yet is still on the rise and thus this doubts academics whether Metaverse can provide a safe space and ameliorate racial and gender equality (Wong, 2021). Problems of “technologically-facilitated violence (TFV)” such as racial discrimination, gender bias and algorithmic oppression have already been demonstrated as an immense challenge to tackle in online or virtual video games (Bailey & Burkell, 2021, p. 535). As stated by Bailey and Burkell, games with structural violence that mimics the real world are perceived as appearing more authentic and consequently these experiences deepen prior structural injustices that adversely affect women, girls, and other minorities (2021, p. 535). The issues of sexism and racism are likely to aggravate by algorithmic system as it functions to collect large volumes of user data to classify users and anticipate behaviours or results (Bailey & Burkell, 2021, p. 536). Such an example can be detected from social interactions online that formed “extremist and hate communities” propelled by algorithms (Daniels, 2018 as cited in Bailey & Burkellm, 2021, p. 537). Algorithms and other automated programs have shown their downsides such as the ability to only identify individuals with fair skin colour (Benjamin, 2019 as cited in Alegria, 2019, p. 9) and labelling women of colours’ “natural hairstyles as ‘unprofessional’” (Alexander, 2016, as cited in Bailey & Burkell, 2021, p. 536). Thus, the limitations of current advanced technological systems as well as the digital affordances tend to doubt the technologies’ capability to enhance diversity, cease online oppression and discrimination and heighten the protection of minorities.

In conclusion, the Internet has allowed more than millions of users to access various resources and news, connect with peers and families, and actively interact on social platforms globally. Considering these factors allow users to regard the Internet as a diverse cyber society to a certain extent. Nevertheless, given the percentage of and the type of roles given to women and women of colour in the computing workforce since the 1960s as well as, the automated systems and algorithms have shown augmented issues of gender and racial discrimination that have not been resolved to this date. These worry many users and scholars that Metaverse will exacerbate these unpreventable problems. Therefore, the lack of diversity has influenced the development of the Internet to a significant extent.

Reference List

Alegria, S. N. (2020). What do we mean by broadening participation? Race, inequality, and diversity in tech work. Sociology Compass, 14(6), 1–12.

Bailey, J., & Burkell, J. (2021). Tech-facilitated violence: thinking structurally and intersectionally. Journal of Gender-Based Violence, 5(3), 531–542.×16286662118554

Egkolfopoulou, M., & Gardner, A. (2021, December 6). Even in the Metaverse, Not All Identities Are Created Equal.

Google. (2021). Google 2021 Diversity Annual Report (pp. 1–62).

NCWIT. (2021, March 25). By the Numbers | National Center for Women & Information Technology.

Noble, S. U. (2018). A society, searching. In Algorithms of Oppression: How Search Engines Reinforce Racism (pp. 15–63). NYU Press.

Wong, Q. (2021). As Facebook plans the metaverse, it struggles to combat harassment in VR. CNET.