Search engines have fundamentally transformed the way we access information in the digital age. They provide access to diverse knowledge across various digital platforms. However, search engines are not immune to biases and many of which stem from ingrained human prejudices.
One of the most prevalent forms of bias in search engines is algorithmic bias.
Given the vast volume of information present in their databases, search engines primarily employ algorithms that prioritize the identification and delivery of content deemed relevant to individual users. This personalized approach, while aiming to provide users with content they are likely to engage with but it can inadvertently distort information. It has the potential to privilege mainstream viewpoints and marginalizing alternative voices and perspectives.
Algorithmic biases in search engines affect political events
The influence of algorithmic bias in search engines on political events is a matter of significant concern, as exemplified by the 2016 U.S. presidential election. During this election cycle, allegations arose regarding algorithmic bias in Google’s search results related to presidential candidates. The proliferation of Fake news became a dominant theme, with some asserting that without the presence of such misinformation, Donald Trump might not have become president(library guides, 2016)..
“FAKE NEWS AVI” by Nikko Russano is licensed under CC BY 2.0.
On one hand, Trump’s supporters propagated a fabricated website falsely claiming Trump’s victory in the presidential race, underscoring the potential manipulation of public perception through misleading information. Concurrently, Trump himself accused Google of harboring political bias and implementing a censorship regime and also alleging that the internet was being manipulated to sway public opinion against him. He further contended that the mainstream media refrained from producing favorable coverage about him(Superville, 2018).
This illustrative case underscores the apprehensions surrounding the impact of search engine algorithms on political events. The partiality exhibited by search engines can expose the general populace to deceptive and biased information, thereby fostering a climate of increased political polarization.
Although Google has taken measures to address these issues, it remains evident that search engines face substantial challenges in maintaining neutrality and preventing the dissemination of erroneous information.
Search engines reinforece Sexism
Search engines are formidable tools that make us to receive and process information. However, they may inadvertently reinforce gender bias. The algorithms themselves are devoid of inherent prejudice, but they are trained on data imbued with preexisting societal biases. Search engines normally perpetuate these biases by presenting results that conform to stereotypes.
“Gender+Bias+Pair+Station” by tonyarch59 is licensed under CC BY-NC-SA 2.0.
For instance, when entering the term “professor” in Google, it becomes apparent that it is challenging to locate photographs of non-white or non-male scholars. In contrast, women are often closely associated with terms such as “kitchen” and “household.” This underscores how search engines inadvertently reinforce gender inequality by highlighting professional achievements and skills(The Representation Project, 2019). As users search more frequently for career achievements associated with males, the visibility of females in those professions often diminishes. The primary idea conveyed is that certain challenging professions are unsuitable for women, thereby exacerbating gender disparities across various sectors.
Furthermore, In the Turkish language, it has gender-neutral pronouns. However, Google Translate frequently translates neutral terms directly as “he” is an engineer or “she” is a nurse. The algorithms directly link “engineer” with “he” which is reflecting real-world gender imbalances(Olson, n.d.).
Search engines unintentionally convey gender bias or and also objectify women. When searching for content related to women, it is more common to encounter sexual innuendo and suggestive material. This type of search perpetuates not only cultural stereotypes about women but also fosters a bad online environment.
“FIGHT SEXISM!” by Phreak 2.0 is licensed under CC BY-NC-ND 2.0.
The reinforcement of gender, inequality, and the absence of diversity are all outcomes of gender bias propagated through search engines. This reinforcement of gender bias can influence users’ attitudes and beliefs regarding gender.
Racism is a deeply entrenched and detrimental social issue
Search engines also exacerbate racial disparities by prioritizing content that aligns with common stereotypes.
For instance, Safiya researched how search engines amplify biases related to racial stereotypes(Noble, 2018). She noted that when she searched for “Black girls” on Google, the top results were often explicit content. This perpetuates the mainstream perception that Black girls are commodified and objectified for adult content. Such depictions strip them of their humanity.
Another example involves Dylann, who committed a tragic act of violence against Black churchgoers after browsing websites promoting hatred, violence, and illegal content against Black individuals. These anti-Black websites propagate hostility and threats, fostering collective resistance against Black communities(Allison, 2021).
Arwa conducted an experiment where she inputted images of 40,000 individuals of diverse ages, races, and genders into Google to observe how the algorithm categorizes them. The results revealed that the label “sexy” was primarily associated with non-white individuals, especially women of color(Aaron, 2021). In contrast, when a white individual appeared, the labels often pertained to typical professions like businessman or police officer. It also show actions they were engaged in, such as singing or public speaking.
The following showed that the full coversation between Orestis Papakyriakopoulos and Arwa Michelle Mboya and theyed talk about Search engines perpetuate stereotypes in many ways.
Overall, the algorithm itself does not fabricate bias. It reflects biases that exist within humanity. These biases are particularly prevalent among those in positions of power. Search engines merely mirror the influence wielded by those with authority and wealth.
The emergence of commercial bias on search engines can indeed lead to market disruption.
Many search engines enhance the visibility of their products or content by charging advertisers fees.
Google Shopping was initially conceived with the promise of providing consumers with a more convenient and cost-effective shopping experience. However, commercial bias became evident during its implementation. When consumers search for cameras on the Google Shopping, the photos of cameras from paid advertisers appear on the page and accompanied by detailed price and specification information(JVG, 2021). Same as the Bikini followed:
“Man, man, wat is dat Google shopping toch slecht” by Marjolein van der Kolk is licensed under CC BY-NC 2.0.
Despite Google’s firm assertion that they do not favor any particular product and that their primary objective is to help consumers find the most suitable products for them. However, it is beneficial for all stakeholders involved. Companies gain increased exposure, consumers receive accurate product information and Google generates revenue through advertising fees.
In summary, search engines prominently display paid advertisements at the top of search results and these advertisements significantly influence user choices. When users perceive that search results favor advertisers, it can potentially damage trust in the search engine and compromise the user experience. Moreover, most users perceive search engines as neutral platforms, and any instance of commercial bias can raise questions about the objectivity and fairness of search engine results.
Google primarily operates by collaborating with advertisers to generate profits. Many individuals view search engines as channels for acquiring knowledge without considering the underlying influence of vested interests.
Conclusion
In essence, the vast amount of data within databases leads algorithms to predominantly extract and display information relevant to us on the homepage. This bias can polarize opinions and exacerbate discrimination. The inherent unfairness in search engines can mislead ordinary people into making erroneous decisions. Gender bias in search engines can deepen the divide between genders, fueling more conflicts and animosities. Moreover, it can contribute to the prevalence of violence and harassment against women and placing them in precarious situations. Search engines also inadvertently reinforce racial discrimination by categorizing individuals as either white or non-white. Especially for non-white women, they frequently being associated with explicit content. This poses risks of societal discord and conflicts, both within and between societies. Although the emergence of commercial bias benefits advertisers, consumers, and search engine platforms. It can disrupt the market and lead to monopolistic situations. In addition, when search results are unfavorable to consumers, it erodes their trust in search engines.
We cannot entirely eliminate biases in search engines. Search engines represent a complex and multifaceted issue. Instead, we should utilize multiple search engines to seek diverse perspectives.
References：
Aaron Nathans. (2021, October 20). Princeton Engineering – How Search Engines Show Their Bias: Orestis Papakyriakopoulos and Arwa Michelle Mboya. (n.d.). Princeton Engineering. https://engineering.princeton.edu/news/2021/10/20/how-search-engines-show-their-bias-
orestis-papakyriakopoulos-and-arwa-michelle-mboya
Allison Hagan. (2021, September 30). Search Engines Like Google Are Powered By Racist, Misogynist Algorithms, Says MacArthur Fellow. NPR Illinois.
https://www.nprillinois.org/2021-09-30/search-engines-like-google-are-powered-by-racist- misogynist-algorithms-says-macarthur-fellow
JVG. (2012, May 31). Google product search results get a commercial bias. VentureBeat.
https://venturebeat.com/business/google-shopping/
Library Guides: Evaluating Information: Fake news in the 2016 US Elections. (2016). Vu.edu.au.
https://libraryguides.vu.edu.au/evaluating_information_guide/fakenews2016
Noble, S. U. (2018). Algorithms of oppression : how search engines reinforce racism(pp.15-63). New
York University Press.
Olson, P. (n.d.). The Algorithm That Helped Google Translate Become Sexist. Forbes. Retrieved
October 7, 2023, from https://www.forbes.com/sites/parmyolson/2018/02/15/the-algorithm-
that-helped-google-translate-become-sexist/?sh=68ea795c7daa
The Representation Project (2019). Search Engine Bias. [online] The Representation Project. Available at: https://therepproject.org/search-engine-bias/
Superville, D. (2018, August 29). Trump accuses Google of burying good news on him. The Sydney Morning Herald.
https://www.smh.com.au/world/north-america/trump-accuses-google-of-burying-good-news-on-him-20180829-p500dg.html
