The Reproduction of Racialized Discriminatory Knowledge Search Engines Perpetuate

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On: October 4, 2021
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Miazia Schüler

University of Amsterdam, 04.10.2021

In 2010, van Dijck published her essay “Search Engines and the Production of Academic Knowledge,” in which she elaborates on the increasingly growing influence search engines are gaining and argues that “digitized search” has changed the way we learn and how knowledge is produced (574). Google Scholar, for instance, is the preferred search engine, especially for college students, due to the convenience of its embedded ranking system. However, whereas public libraries filter or rank quality materials based on their “academic weight,” Google Scholar ranks according to popularity (van Dijck 575-577). This, however, does not necessarily ensure quality (van Dijck 577). This makes the system “vulnerable to bias and distortion” due to the lack of expertise within the demographics of Google Scholar users (van Dijck 580). Nonetheless, van Dijck claims that Google Scholar is a “co-producer” of academic knowledge that shapes how its users educate themselves (575).

These kinds of dynamics are a common example for the multitude of issues search engines, specifically Google, cause through the biases they perpetuate and are in many ways transferable to racist patterns (Benjamin 44). It is becoming increasingly evident that such tools are “disproportionally harmful to the most vulnerable” (Noble 49-50). 

In September 2011, Noble googled “black girls” when she was confronted with a search result list of porn sites (Figure 1), the very first one being “SugaryBlackPussy.com – Black girls in a hardcore action galeries” (17-18). Noble’s Google search of the keyword “black girls” highlighted the fact that Black girls and women were openly used as racialized bate for porn sites, “dehumanizing them as commodities, as products and as objects of sexual gratification” (18). Google almost exclusively depicted black women as a sexual representation in an environment that “denotes male power, female powerlessness, and sexual violence” (Noble 99). Such structurally embedded inequalities women of color face are actively being reinforced and reproduced through a digital environment that is male-oriented as well as male-dominated (Noble 59). This is what Noble refers to as the male haze (59).

Figure 1: „first page of search results on keywords ’black girls’, September 18 2011“ – Noble’s Screenshot of her search results (19)

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Figure 1: „First page of search results on keywords ’black girls’, September 18 2011“ – Noble’s Screenshot of her search results (19)

Ultimately, the (US) porn industry, just like any other, is driven by neoliberal values and unfolds in capitalist structures for economic growth (Noble 104). Furthermore, the industry has the financial means to advertise itself based on any keywords chose, including those of fetishized marginalized racial groups (Noble 88). Search results are ultimately ranked according to the highest bidding advertisers (Noble 49). In 2011, more than 26 billion USD was spent on Google advertisements (Johnson). Just three years prior, studies found that users largely trust search engine results: 68 percent believe that they are “fair and an unbiased source of information” (Fallows). 62 percent were also unaware of the differentiation of ads and non-ads within their search results (Fallows).

To this day, Google remains the most popular search engine, constituting 92% of worldwide search engine market share (StatCounter). In 2020, 126 billion USD was spent on advertisements on Google sites (Johnson). The search engine is highly praised for its “accessibility and comprehensiveness” (van Dijck 576) as research preferences have shown to be speed and convenience (van Dijck 578). Therefore, van Dijck argues that Google has become “a key player in the global distribution of communication power”. She highlights the “power of search engines to steer collective profiling” and “may eventually shape the production of knowledge, even if subtly and non-intentionally” (583-584). This is especially dangerous for the reproduction/reinforcement of racial stereotypes, hypersexualization, pornographization, and more. We make connections and process information through associative thinking, making search engines like Google “global associative memories of information sources” (van Dijck 584-585). This is also largely why intention must be separated from “its strictly negative connotation in the context of racist practices” and examine how seemingly neutral processes “coexist with forms of malice and neglect” (Benjamin 61). “Creating and normalizing structural and systemic isolation” of marginalized groups in an environment where users are particularly receptive and influenceable will only “reinforce oppressive social and economic relations” (Noble 10). 

In 2012, Google changed its algorithm to suppress pornography as the primary representative search result of black girls. In 2016, they included “more diverse and less sexualized” images of black girls and women. Although many necessary improvements were implemented, many of the results “remain troubling”, as they nonetheless still portray and cater from shallow/outdated to stereotypical/stigmatizing depictions of Black girls and women (Noble 104). Search results for “Black girls” tend to focus on music, empowerment, hair, black is beautiful, support programs, missing people, racism, and bias, and more. Search results for “Black women” tend to focus on the strong Black woman, how to overcome the stereotype of the angry Black woman, hair, corporate success stories and corporate failures, discrimination, and more. This will slightly differ from user to user and is the mere reflection of an impression that can be traced by either independently Google searches. There are countless further examples of Google searches with discriminating results such as “three black teens”, “unprofessional hairstyles”, “professional hairstyles”, and more. All (at a given time) caused the reproduction of racist biases and contributed to stereotypes which Google perpetuates, that many suffer from daily (Benjamin 93-95). 

Noble believes that algorithmic filtering of search results will eventually become a significant human rights issue (1). The “struggle to democratize information” (Benjamin 95) can at the very least be tackled by systemically examining/questioning the legitimacy and quality of information of search results. Van Dijck suggests that “ information literacy” should be part of the curriculum, with a focus on “socio-technical aspects of search” to be able to assess the “usefulness and reliability” of search engines (586-587). Noble recommends educational means such as the Black Girl Code project, which teaches technological and computational literacy from a Black Feminist perspective (64). Alternatively, Black Feminist Technology Studies focuses on an epistemological research approach of racialized and gendered identities within new media objects (171-172). Both aim to equip the tech industry with specializations, especially from the Social Sciences and the Humanities (Noble 70). Indeed, the mere representation will not solve “the problems of racist exclusion” (Benjamin 61-62), nor can the Black female programmers of the industry be solely responsible for it – not now, not in the future (Noble 65). However, what is clear is the need to reassess “information as a public good” and a full-on reevaluation of the implications of our information sources being governed by corporate-controlled advertising companies” (Noble 5). Furthermore, we must question our trust for a monopolistic company to produce knowledge, which provides no insights into how it operates (van Dijck 582).

Bibliography

Benjamin, Ruha. Race After Technology: Abolitionist Tools for the New Jim Code. 1st ed., Medford, Polity, 2019.

Dijck, José van. Search Engines and the Production of Academic Knowledge. International Journal of Cultural Studies, vol. 13, no. 6, 2010, pp. 574–92. doi:10.1177/1367877910376582.

Fallows, Deborah. Search Engine Use. Pew Research Center: Internet, Science & Tech, 2008, www.pewresearch.org/internet/2008/08/06/search-engine-use. Accessed 03.10.2021.

Johnson, Joseph. Google Sites: Advertising Revenue 2001–2020. Statista, 2021, www.statista.com/statistics/266242/advertising-revenue-of-google-sites. Accessed 03.10.2021. 

Noble, Safiya Umoja. Algorithms of Oppression: How Search Engines Reinforce Racism. Illustrated, New York, NYU Press, 2018.

Search Engine Market Share Worldwide. StatCounter Global Stats, 2021, gs.statcounter.com/search-engine-market-share. Accessed 4 Oct. 2021

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