
The development of the Internet has linked the world together. Different races, cultures, and ideas have exchanged and collided in the Internet world. In addition to technological progress, such vigorous development also benefits from using algorithmic techniques. Also, as a hot topic in recent years, diversification has become a symbol of the success of technology companies. Diversity is not a word with a specific meaning but is usually reflected in the impact of a company created by its operations, management, and employees.

However, many critics say the lack of diversity in the internet industry is still evident. This paper will analyze three aspects of the impact of the lack of diversity on the Internet and if it potentially affects individuals and society. They are the distribution of people in Silicon Valley companies, the data-driven algorithms, and the Internet censorship system.
Lack of gender representation
Diversification is the trend in contemporary society, which helps develop products and ideas for better services to consumers and generate higher returns for the company (Chang, 2022). Also, a diverse team can better make business decisions (Hak, 2019). As a representative product of the digital era, the Internet has become an integral part of people’s lives and a fundamental condition for developing various industries. It is therefore even more important that its creative team includes a diverse range of employees to help realise the development of products and services.
Data on female Tech employees in 2022 indicated GAFAM as one of the top five tech stocks in the US. Amazon has a near equal male and female workforce, but only about a quarter of its management are women (Chang, 2022). Besides, in technology research and development, Apple and Google only have 23% female workers, compared to 20 at Microsoft (Chang, 2022).

Today, women’s educational attainment is rising significantly. This article provided several data components of men’s and women’s education level. In the US, the proportion of men and women with a bachelor’s degree is 42% and 58%, respectively, 57% and 33% at the master’s level and above (Hanson, 2021). There is no doubt that women are much better educated than before, but this is not reflected in the workplace.
Furthermore, the Internet company (Google) is still not effective in its path to developing diversity. Some critics point out that Google published a report of diversity 2014 to today, and the percentage of female employees has still not changed significantly. Google’s Senior Vice President of People Operations, Laszlo Block, has made the explanation of this situation. However, the statistic shows many problems of gender inequity in the workplace, one of the problems is the unequal distribution of pay between women and men. The lack of gender diversity could be reduced if tech companies gave women more opportunities for promotion and fair treatment. Moreover, as leaders in the internet industry, companies such as Google should take responsibility for promoting gender diversity in the workplace.
Data-driven algorithms create bias
It can be felt that the user’s experience when using the Internet for the first time and after a period becomes more in line with the user’s personal preferences, and these changes can be attributed to the use of data-driven systems. Broadly, a data-driven system is one where we rely on internet searches and recommendations to help us make decisions. After that, the system will be personalized by our choices. However, scholars have pointed out that the use of data-driven algorithmic technologies since Web 2.0 has provided users with efficient access to data and practical information. However, the proliferation of algorithmic systems has been accompanied by a series of social problems (Pitoura, 2020).
Today’s news is far removed from the rigorous censorship of the paper media of the past, and there is editorial bias in the news stories pushed through the internet. According to Sutter (2000), there is an ideological bias in the American media, which is reflected in the ordering of the media during conservative and liberal elections. This is the data from media statistics in the United States, where whites account for 72.6% of the 21,724-practicing media specialist. While biased reporting is not blamed on media practitioners, it is the cultural differences they receive. But biasedness coupled with a model of data-driven algorithms creates an injustice.
Political media’s bias, in a single chart By Newsy All rights Reserved. Retrieved from https://www.youtube.com/watch?v=RL-CHyzgK1Q&t=19s
Research shows that politically biased articles can influence readers’ choice of party and the spread of misinformation.
In addition, the biased nature of media coverage affects the general population. In Asia, a common bias in reporting is that if there is a female role in something immoral, then the media report will be biased toward men. However, for the United States as a diverse country, racism is a major social problem, including the Floyd incident caused by police violence in 2020. According to Kilho’s (2021) team’s research, in nearly a decade of media coverage, the reports of ‘police violence’ and ‘promotion of racial reform’ are marginal. Even if there are protest marches, there are not many reports. Hence, the lack of diversity in media coverage and media workers creates inequities and biases in reporting that hurt both individuals and color groups.
Internet censorship system
China is one of the most censored countries on the Internet, but today’s censorship is, to some extent infringing on the freedom of some users. In China, the Communist Party of China regulates the media in mainland China to ensure that the content published by the media is free from ideological bias (Wu, 1944). In recent years, along with the massive growth in Internet use by the Chinese masses, government regulation of the Internet industry has moved from the media to individual users. Becker points out that censorship in China is usually accomplished through three technical aspects: monitoring users, controlling habits, and controlling content, with media platforms filtering information comprehensively through keyword control (Faust cited Becker, 2019). As a Weibo user, it can feel that since Covid-19, media platforms have become stricter in controlling users’ statements.
how China censors internet By South China Morning Post All rights Reserved. Retrieved form https://www.youtube.com/watch?v=ajR9J9eoq34
As censorship became stricter, platform users began to fight back, and some choosing to voice their displeasure with questions comment on the official account.
In addition, the Chinese media environment has more direct regulation of celebrity speech. On Weibo, the People’s Daily releases a remembrance blog post on every historical anniversary. And then almost all celebrities retweeted to show their support. However, once they have posted supportive comments on a topic that their celebrity status does not allow them to mention, their account will be banned, like LGBT. But in fact, many young Chinese users are supporting this group in a quietly guarded way.

Therefore, China’s Internet censorship system is strictly embodied in various groups, which leads to the lack of sharing diverse viewpoints among users. In addition, the government needs to consider whether censorship’s scope is reasonable and the users’ real feelings.
Conclusion
In internet development, the negative of a lack of diversity is evident. Having a diverse team can help a company to grow better. In the media, this can lead to biased reporting and a lack of positive impact on racism. And strict censorship can result in users only being able to accept a single idea, limiting the exchange of different consciousnesses. In the future, it is important that companies, media workers, and Internet censorship will all need progress towards a harmonious, social, and online environment that allows for diversity.
Reference List
Bastone, N (2019). The 15 most powerful women at Google. Insider. https://www.businessinsider.com/most-powerful-women-at-google-2019-1
Budak, C., Goel, S., & Rao, J. M. (2016). Find and Balanced? Quantifying Media Bias through Crowdsourced Content Analysis. Public Opinion Quarterly, 80(1), 250–271. https://doi.org/10.1093/poq/nfw007
Bock, L (2014). Getting to work on diversity at Google. Google. Retrieved October 13, 2022 https://blog.google/outreach-initiatives/diversity/getting-to-work-on-diversity-at-google/
Chang, J. (2022, October 13). 52 Women in Technology Statistics: 2022 Data on Female Tech Employees. Finance Online. Retrieved October 13, 2022, from https://financesonline.com/women-in-technology-statistics/
Fottrell, Q (2018). This is how much women leaders in tech companies earn vs. men (it’s not pretty). Market Watch. https://www.marketwatch.com/story/this-is-how-much-women-leaders-in-tech-companies-earn-vs-men-its-not-pretty-2018-02-08
Faust, M. (2019). Does the democratic West “learn” from the authoritarian East? Juxtaposing German and Chinese Internet censorship and filter bubbles. East Asian Journal of Popular Culture, 5(1), 55–78. https://doi.org/10.1386/eapc.5.1.55_1
Hak, A. (2019). Why diverse teams make better business decisions. TNW. https://thenextweb.com/news/why-diverse-teams-make-better-business-decisions
Hanson, M. (2021). Education Attainment Statistics. Education Data Imitative. Retrieved October 13, 2022, form https://educationdata.org/education-attainment-statistics
Kilgo, D. (2021). Media bias delegitimizes Black-rights protesters. Nature (London), 593(7859), 315–315. https://doi.org/10.1038/d41586-021-01314-2
Li, C., Zhang, X., (2017) Online regulation and LGBT rights: A test for China’s legal system. Brookings. https://www.brookings.edu/opinions/online-regulations-and-lgbt-rights-a-test-for-chinas-legal-system/
Pitoura, E. (2020). Social-minded Measures of Data Quality: Fairness, Diversity, and Lack of Bias. ACM Journal of Data and Information Quality, 12(3), 1–8. https://doi.org/10.1145/3404193
Sutter, D. (2000). Can the Media Be So Liberal? The Economics of Media Bias. Cota J., 20, 431.
Wu, G. (1994). Command communication: The politics of editorial formulation in the People’s Daily. China Quarterly, 137, 194-211.
Yang, Z (2022). Now China wants to censor online comments. MIT Technology Review. https://www.technologyreview.com/2022/06/18/1054452/china-censors-social-media-comments/