‘Why do white people have thin lips?’ Google and the perpetuation of stereotypes via auto-complete search forms

Baker, Paul; Potts, Amanda

doi:10.1080/17405904.2012.744320

Cited by 94 publications

(76 citation statements)

References 25 publications

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“…Previous studies of search engines' portrayal of people have either been conducted manually on a small set of queries (e.g., [5]), or within a particular context (e.g., professions [26]). Our method allows us to study more generalized stereotypes based on a large set of character traits (e.g., who is an "emotional person"?)…”

Section: Bias In Image Search: Perpetuating Social Stereotypesmentioning

confidence: 99%

“…Baker and Potts [5] studied Google's auto-complete feature, designed to help users formulate queries by suggesting terms and phrases. They found that auto-complete associates questions about appearance, behavior and attitudes to particular social groups, stereotyping some more negatively than others.…”

Section: Bias In Image Search: Perpetuating Social Stereotypesmentioning

confidence: 99%

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Competent Men and Warm Women

Otterbacher

Bates

Clough

2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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There is much concern about algorithms that underlie information services and the view of the world they present. We develop a novel method for examining the content and strength of gender stereotypes in image search, inspired by the trait adjective checklist method. We compare the gender distribution in photos retrieved by Bing for the query "person" and for queries based on 68 character traits (e.g., "intelligent person") in four regional markets. Photos of men are more often retrieved for "person," as compared to women. As predicted, photos of women are more often retrieved for warm traits (e.g., "emotional") whereas agentic traits (e.g., "rational") are represented by photos of men. A backlash effect, where stereotype-incongruent individuals are penalized, is observed. However, backlash is more prevalent for "competent women" than "warm men." Results underline the need to understand how and why biases enter search algorithms and at which stages of the engineering process.

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Section: Bias In Image Search: Perpetuating Social Stereotypesmentioning

confidence: 99%

Section: Bias In Image Search: Perpetuating Social Stereotypesmentioning

confidence: 99%

Competent Men and Warm Women

Otterbacher

Bates

Clough

2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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“…First, the search target's dignity and autonomy may be harmed due to the spread of misinformation about them. Such misinformation may be about a specific individual, as in Wullf's case, or about minority or disempowered-group members, as in the prejudicial suggestions about gays, Jews, or the Māori (Baker and Potts, 2013;Elers, 2014). Second, the searcher is harmed when she develops false, biased, or skewed beliefs.…”

Section: Epistemic Virtues and Harms Of Autocompletementioning

confidence: 99%

Responsible epistemic technologies: A social-epistemological analysis of autocompleted web search

Miller

Record

2016

New Media & Society

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Information providing and gathering increasingly involve technologies like search engines, which actively shape their epistemic surroundings. Yet, a satisfying account of the epistemic responsibilities associated with them does not exist. We analyze automatically generated search suggestions from the perspective of social epistemology to illustrate how epistemic responsibilities associated with a technology can be derived and assigned. Drawing on our previously developed theoretical framework that connects responsible epistemic behavior to practicability, we address two questions: first, given the different technological possibilities available to searchers, the search technology, and search providers, who should bear which responsibilities? Second, given the technology's epistemically relevant features and potential harms, how should search terms be autocompleted? Our analysis reveals that epistemic responsibility lies mostly with search providers, which should eliminate three categories of autosuggestions: those that result from organized attacks, those that perpetuate damaging stereotypes, and those that associate negative characteristics with specific individuals.

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“…Constituting a new form of power, it has been suggested that the design of algorithms and algorithmic procedures foster certain cultures, ideologies and identities. Mager (2012), for example, has demonstrated how search engines embody an "algorithmic ideology" closely connected to capitalist modes of production, while others have shown how Google perpetuates stereotypes related to race and gender through its deliveries of online content (Olofsson 2015;Baker & Potts 2013). Because algorithms serve as cultural intermediaries that help content "find us", they also affect how cultural capital is acquired and how taste is shaped (Beer 2013; see also Morris 2015).…”

Section: Algorithms and User Profilingmentioning

confidence: 99%

Tracking Gendered Streams

Eriksson

Johansson

2017

Cult. Unbound

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One of the most prominent features of digital music services is the provision of personalized music recommendations that come about through the profiling of users and audiences. Based on a range of "bot experiments, " this article investigates if, and how, gendered patterns in music recommendations are provided by the streaming service Spotify. While our experiments did not give any strong indications that Spotify assigns different taste profiles to male and female users, the study showed that male artists were highly overrepresented in Spotify's music recommendations; an issue which we argue prompts users to cite hegemonic masculine norms within the music industries. Although the results should be approached as historically and contextually contingent, we argue that they point to how gender and gendered tastes may be constituted through the interplay between users and algorithmic knowledge-making processes, and how digital content delivery may maintain and challenge gender relations and gendered power differentials within the music industries. Seen through the lens of critical research on software, music and gender performativity, the experiments thus provide insights into how gender is shaped and attributed meaning as it materializes in contemporary music streams.

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‘Why do white people have thin lips?’ Google and the perpetuation of stereotypes via auto-complete search forms

Cited by 94 publications

References 25 publications

Competent Men and Warm Women

Competent Men and Warm Women

Responsible epistemic technologies: A social-epistemological analysis of autocompleted web search

Tracking Gendered Streams

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