Prejudice through searching engines on the Internet

In today’s society, the Internet is at the forefront of social development, and it plays an increasingly significant role in our daily lives, while also bringing us convenience and a diverse lifestyle. The advantage of the internet mainly lies in the timely access to information, providing a more open and equal platform to achieve communication and exchange. In the fast-changing and fast-developing network era, while the Internet brings convenience to people, there are structural problems that have always existed. According to Terry Flew (2019), there are severe problems on the Internet, includes sexism, racism, hate speech, and fake news. Problems on the Internet have been unresolved resulting in the Internet exacerbating these problems and affecting people’s judgment. The Internet does not exist naturally, but is a system built by human intelligence that contains human thinking and stores human information and emotions, so the Internet is inevitably influenced by the social structure of people.

Internet culture

The structural issues have been existed since the Internet exposed, And also largely influenced the current digital world and the real world. Turner (2021) demonstrates that the culture is still dominant by the Middle-class white males. ‘Bro-culture’ in Silicon Valley describes the inequality within their group, includes excluding the minorities. Such a culture leads to a low status for women, fewer opportunities of work, making it difficult to speak out for the status of women to a great extent. Minorities and people of color are also discriminated against in the workplace and are not considered to be as competent as white males in the workplace. This culture not only affects women, the ethical minorities, and black people in the workplace, but is also very common in their lives. It is too frequent to observe evidence of the harassment of women and marginalised groups online within these experiences and practices.

This kind of prejudice is even more exacerbated in black female. Black women are often labeled as mean, ugly and sexualized. Jessica Drakett (2018) points out that there are sexist meme appearing on the Internet, includes memes that joke about women’s bodies and get women back in the kitchen. Those memes will create an uncomfortable feeling for female viewers. This kind of expression of humor as projecting a new aggressive emotion that is also disrespectful and offensive to women. More seriously, this can lead to incorrect guidance of male jokes about women’s bodies, and even teenagers will form incorrect values. Safiya Noble (2018) expresses the commodification of the black female body as worthy of attention. Incomprehensive and obfuscated by the dominant authenticity narratives and lack of search engine partiality. This malicious misunderstanding of them affects their regular lives

Internet is increasing or reducing the inequalities?

The Internet has helped reduce a very small portion of structural social inequality because it increases the opportunities for communication and people can easily access information and express their opinions. The Internet also provides an easy way for the authorities to report inaccurate or illegal information.

Precisely because of the convenience of the Internet, the Internet is now mixed with fish and dragons, very difficult to regulate, filled with a large number of distorted speech, people’s views and perceptions will inevitably be affected. Susan Luckman (1999) points out that because it was built by gendered people and because gender-led people access it in ways that strengthen the subjugating of women, cyberspace could no longer escape the social construction of gender.

Prejudice about women has always existed on the Internet, or the conflict between men and women has always existed on the internet. Parts of males argue that men are more capable than women, whether they are more talented on average in academics, art, sports, etc. When searching for women, the terms are generally housewives, child caring, etc. These phrases can cause resentment among some women and make women feel unequal. Even though gender equality is promoted and women sacrifice more to their families in reality, they are designated as the less capable party on the Internet, and Internet recruitment is now biased against women, such as only hiring married women because they are more stable. These prejudices are misleading for conservative-minded women and affect their ability to bring out their greater value, which is their loss as well as the loss of society.

Algorithms bias

Safiya Noble (2018) brought up the bias of algorithms. There are many algorithms that have examples of gender and racial discrimination. When you search for black people on Google, numerous keywords and phrases of negativity appear, for example, angry, poor, crime. At the beginning of the pandemic, if you search for the virus, you will see Chinese virus or Wuhan virus, which led to a great numbers of cyber-attacks and physical attacks on Chinese people. This is the result of algorithms learning through a large amount of data, which reflects the prejudice of the public against them from the side. In the absence of understanding, other people will be influenced by the algorithm to make one-sided judgments, triggering attacks on their speech, or even produce excessive behavior offline to commit personal attacks, and similar news happens from time to time. Even though this situation is being increasingly improved by the purification of the Internet and the improvement of human beings’ own quality, the sun still cannot shine in all corners, and unjust prejudices and ideas are still hiding in the shadows of the Internet.

Technology have moved to active participants in our social field from passive information producers dwelling on desktop devices. James Handler (2016) discussed that the Internet and the World Wide Web infrastructures enable new applications to grow, and new information is available to you about us and our world through our social interactions with machines.

According to Tom Simonite (2019), the researchers of NIST found that it’s tougher to recognize persons with darker skin for algorithms. It’s unfair to black people and inconvenient for their lives when they need to use facial recognition.

In terms of class, the majority of politicians or rich people on the Internet are predominantly white males, with blacks and women seemingly disappearing from sight. Not only is this reflected, but bias is also reflected everywhere in the Internet media. Male and female protagonists are often predominantly white, and the winners of various awards are also predominantly white. In fact, most of the policy makers in real life are also predominantly white, with people of color only accounting for a small percentage of them, and the power is still in the hands of the majority of whites.

Conclusion

With the intelligent spread of the Internet, we are gradually becoming accustomed to finding the laws of knowing the world and knowing ourselves through data and relying on algorithmic programs. But the algorithm runs on learning and simulating human thinking, so it may simulate both human strengths and inevitably human weaknesses. Social biases are extended under the age of artificial intelligence, and these biases do not hold impartial principles and positions in media communication. They spread misconceptions or bias-laden views, thus influencing the perception and awareness of the community at large, which suffers as a result.

 

References

Drakett, J., Rickett, B., Day, K., & Milnes, K. (2018). Old jokes, new media – Online sexism and constructions of gender in Internet memes. Feminism & Psychology, 28(1), 109–127. https://doi.org/10.1177/0959353517727560

 

Hendler, J., & Mulvehill, A. M. (2016). Social Machines: The Coming Collision of Artificial Intelligence, Social Networking, and Humanity. Apress L. P.

 

Lindaman, D. (2021). Donald Trump’s ‘Chinese virus’: the politics of naming. The Conversation. Retrieved 14 October 2021, from https://theconversation.com/donald-trumps-chinese-virus-the-politics-of-naming-136796.

 

Luckman, S. (1999). (En)gendering the digital body: Feminism and the internet. Hecate, 25(2), 36-47. http://ezproxy.library.usyd.edu.au/login?url=https://www.proquest.com/scholarly-journals/en-gendering-digital-body-feminism-internet/docview/210907727/se-2?accountid=14757

 

Lusoli, A., & Turner, F. (2021). “It’s an Ongoing Bromance”: Counterculture and Cyberculture in Silicon Valley—An Interview with Fred Turner. Journal of Management Inquiry, 30(2), 235–242. https://doi.org/10.1177/1056492620941075

Noble, S. U. (2018). Algorithms of oppression : How search engines reinforce racism. ProQuest Ebook Central https://ebookcentral-proquest-com.ezproxy.library.sydney.edu.au

 

Simonite, T. (2019). The Best Algorithms Still Struggle to Recognize Black Faces. Wired. Retrieved 15 October 2021, from https://www.wired.com/story/best-algorithms-struggle-recognize-black-faces-equally/.

 

Terry Flew (2019), ‘Guarding the Gatekeepers: Trust, Truth and Digital Platforms’, Griffith Review 64, pp. 94-103.