Structural inequalities of gender and race on the Internet

Tut 12

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The development of the Internet is affected to some extent by structural inequalities. Structural inequalities means the unequal status of one group of people relative to another, reinforced and sustained by different aspects such as roles, rights and decision-making capacity (Economic and Social Commission for Western Asia (ESCWA), 2016). The continuity of the Internet makes the structural biases from the early Internet exert a subtle influence on the contemporary digital culture and the development of the Internet. At the same time, the development of the Internet is also exacerbating the power of inequalities, such as the inequalities within modern Silicon Valley, social discrimination against women and race, and algorithmic bias of artificial intelligence.

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Internet users development

With about 2.89 billion monthly active users as of the second quarter of 2021 (Statista, 2021b), Facebook is the world’s largest social network. The company also said it had used at least one of its other products, such as WhatsApp and Instagram. Wechat has more than 1.25 billion monthly active users of all ages (Statista, 2021a), and YouTube has more than 2 billion monthly active users (Dean, 2021). This means that digital media has reached a large part of the world’s population, and with it, the amount of time that users spend using it has skyrocketed, and the amount of information available has increased exponentially. The question then is whether the Internet amplifies or shrinks such structural inequalities.

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The Internet shaped by structural inequality to some extent

First of all, the most representative feature is the contemporary existence of a still predominantly white, male and middle class culture (Lusoli & Turner, 2020), which is one of the early features of the Internet and continues to this day. In the next place, Lusoli and Turner (2020) mentioned the emergence of a counterculture called Bro-culture in the communes of the 1960s, as well as the exclusion of ethnic minorities. As a result, some people in what is now the San Francisco Bay Area have been evicted and become long-distance commuters. Inequalities within Silicon Valley also shows the impact of the structural inequalities on Internet.

Googlization and search results for black women and women

Moreover, Noble (2018) noted that searches in 2009 reflected the sexualization of black women and their naturalization as sex objects. Noble had a second search for black women in 2011 was still filled with pornography. Also, the abundance of black feminism on Noble’s computer didn’t change the type of search results. The information of Black girls are still about porn and sex. This may indicate that search results are influenced by the search preferences of the majority of people, or by mainstream culture. In reality, however, it is not only black women, but also other people of color , such as Asian girls and Caucasian. According to the googlization proposed by Vaidhyanathan (2011), he believes that Google has penetrated and simultaneously influenced our culture, such as personal opinions and judgments. He added that Google is considered to be no different from the Internet (Internet and search engine), and that Google is the most comprehensive collection of individual and collective opinions and judgments. It is not just society that influences the results of Internet searches, Internet also influences the perceptions of society, and it is related to the development of the internet.

In addition, Noble (2018) pointed to a 2013 campaign launched by the United Nations to bring awareness and attention to sexism and discriminatory ways of dealing with women in society through a series of Google searches, the campaign says that society still has many different sexist views towards women. For example, women should not be able to vote, they should work, they should stay in their place, in the kitchen or at home, they should be slaves. Although the campaign took place, it did not target search engines. Nevertheless, the reality is that companies like Google, which is an information monopoly, have the ability to sort and rank search results by all kinds of topics. In this case, user clicks and business processes that allow paid ads to be prioritized in search results (Noble, 2018), leading to a lower ranking of female images on the search page. For instance, when searching for “Chief executive officer” images, results are mostly male. Plus, it can also change search results by region. Therefore, Google’s search results reveal some of beliefs of society and prejudices about women and race.×815/1320×0/filters:focal(0x0:1132×815):format(webp):no_upscale()/

Algorithmic bias in artificial intelligence

At the same time, the bias of an algorithm also confirms the social inequalities of artificial intelligence. A courtroom sentencing software called Northpointe is used to assess future crimes defendants may commit, and this type of artificial intelligence software is believed to have grossly mispredicted defendants’ future activities, leading to excessive incarceration of black defendants. For whites, recidivism is predicted to be low or no (Noble, 2018). The tricky part about algorithmic bias is that the engineers responsible for programming could be biased even if they do not have subjective race, gender, age, and so on, and artificial intelligence cannot recognize right from wrong will be a possible to be biased. Thus, structural inequalities affects the Internet and Internet is increasing it.

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Gender structural inequality shows in different ways

However, there will be opponents who argue that the Internet reduces inequalities. The reason is, in 2021, at least 51 women of color will be represented in the 535 members of The U.S. Congress, the largest number to date. And there are 115 people of color (Buchholz, 2021). Besides, the popularization and development of the Internet allows individuals to communicate, create and share, individuals can get information and materials, but also get an easier access to knowledge channel, especially for people who have previously had limited access to knowledge such as the poor and the lower class. While these are all signs of reduced and improved inequalities, they are not whole. Souter (2018) proposed that the poor will have less access to technology than the rich, and that highly educated people have more skills in these areas. Simultaneously, gender is also used in some societies as a factor of income and education inequalities, with men earning more on average in most societies. Furthermore, in some countries boys are more likely to go to school than girls. Especially in the least developed countries, fewer girls are finishing school. (Souter, 2018). Hence, structural inequalities about gender are still widespread.

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Structural gender inequality is also reflected in the fact that globally, women are about 12% less likely to use the Internet (Souter, 2018). Moreover, the universality of the Internet allows users to share information freely, which will produce a lot of false information. At the same time, there are countless producers of information, and different views will make different descriptions of the same thing. In such a process, it is inevitable that there will be comments and opinions of structural inequalities. As a result, the Internet still amplifies structural inequalities.

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Looking ahead

In conclusion, structural inequalities is influencing the development of Internet from different ways, and Internet has magnified the problem of inequalities. Not just inequalities within Silicon Valley, discriminatory views about women and race, but algorithmic bias in artificial intelligence. While the number of members of color in the U.S. Congress is increasing each year, but the spread of the Internet has magnified structural inequalities, and structural inequalities about race and gender remain widespread. Therefore, we cannot argue that the Internet has improved structural inequality. The government or relevant departments, digital platforms should try to fix this problem, but it will be a long process.











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