Danaë Metaxa
Michelle A. Gan
James A. Landay
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Abstract
Algorithmically-mediated content is both a product and producer of dominant social narratives, and it has the potential to impact users' beliefs and behaviors. We present two studies on the content and impact of gender and racial representation in image search results for common occupations. In Study 1, we compare 2020 workforce gender and racial composition to that reflected in image search. We find evidence of underrepresentation on both dimensions: women are underrepresented in search at a rate of 42% women for a field with 50% women; people of color are underrepresented with 16% in search compared to an occupation with 22% people of color (the latter being proportional to the U.S. workforce). We also compare our gender representation data with that collected in 2015 by Kay et al., finding little improvement in the last half-decade. In Study 2, we study people's impressions of occupations and sense of belonging in a given field when shown search results with different proportions of women and people of color. We find that both axes of representation as well as people's own racial and gender identities impact their experience of image search results. We conclude by emphasizing the need for designers and auditors of algorithms to consider the disparate impacts of algorithmic content on users of marginalized identities.
- Joshua Asplund, Motahhare Eslami, Hari Sundaram, Christian Sandvig, and Karrie Karahalios. 2020. Auditing race and gender discrimination in online housing markets. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 14. 24--35.Google Scholar
Cross Ref
- Derek R. Avery. 2003. Reactions to diversity in recruitment advertising--are differences black and white? Journal of applied psychology 88, 4 (2003), 672. https://doi.org/10.1037/0021-9010.88.4.672Google ScholarCross Ref
- Derek R Avery, Sabrina D Volpone, Robert W Stewart, Aleksandra Luksyte, Morela Hernandez, Patrick F McKay, and Michelle (Mikki) R Hebl. 2013. Examining the draw of diversity: How diversity climate perceptions affect job-pursuit intentions. Human Resource Management 52, 2 (2013), 175--193.Google ScholarCross Ref
- Paul Baker and Amanda Potts. 2013. "Why do white people have thin lips?' Google and the perpetuation of stereotypes via auto-complete search forms. Critical Discourse Studies 10, 2 (2013), 187--204. https://doi.org/10.1080/17405904.2012. 744320Google ScholarCross Ref
- Andrea Ballatore. 2015. Google Chemtrails: A Methodology to Analyze Topic Representation in Search Engine Results. First Monday 20, 7 (June 2015). https://doi.org/10.5210/fm.v20i7.5597Google ScholarCross Ref
- Engin Bozdag. 2013. Bias in algorithmic "ltering and personalization. Ethics and Information Technology 15, 3 (2013), 209--227.Google ScholarDigital Library
- Le Chen, Ruijun Ma, Anikó Hannák, and Christo Wilson. 2018. Investigating the Impact of Gender on Rank in Resume Search Engines. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI '18. ACM Press, Montreal QC, Canada, 1--14. https://doi.org/10.1145/3173574.3174225Google ScholarDigital Library
- Sapna Cheryan, Victoria C. Plaut, Paul G. Davies, and Claude M. Steele. 2009. Ambient belonging: how stereotypical cues impact gender participation in computer science. Journal of Personality and Social Psychology 97, 6 (2009), 1045. https://doi.org/10.1037/a0016239Google ScholarCross Ref
- Angèle Christin. 2017. Algorithms in practice: Comparing web journalism and criminal justice. Big Data & Society 4, 2 (2017), 2053951717718855. https://doi.org/10.1177/2053951717718855Google ScholarCross Ref
- Katheryn R Christy and Jesse Fox. 2014. Leaderboards in a virtual classroom: A test of stereotype threat and social comparison explanations for women's math performance. Computers & Education 78 (2014), 66--77.Google ScholarCross Ref
- Harris Cohen. 2020. Bringing fact check information to Google Images. https://blog.google/products/search/bringingfact-check-information-google-images/.Google Scholar
- Shelley J. Correll. 2004. Constraints into Preferences: Gender, Status, and Emerging Career Aspirations. American Sociological Review 69, 1 (2004), 93--113. https://doi.org/10.1177/000312240406900106 arXiv:https://doi.org/10.1177/000312240406900106Google ScholarCross Ref
- Kate Crawford. 2017. The Trouble with Bias (NIPS 2017 Keynote). YouTube. https://www.youtube.com/watch?v=fMym_ BKWQzkGoogle Scholar
- Kimberlé Crenshaw. 1989. Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. University of Chicago Legal Forum (1989), 139.Google Scholar
- George B Cunningham and E Nicole Melton. 2014. Signals and cues: LGBT inclusive advertising and consumer attraction. Sport Marketing Quarterly 23, 1 (2014), 37.Google Scholar
- Cathy Edwards. 2018. Making visual content more useful in Search. https://blog.google/products/search/makingvisual-content-more-useful-search/.Google Scholar
- Robert Epstein and Ronald E. Robertson. 2015. The Search Engine Manipulation Effect (SEME) and Its Possible Impact on the Outcomes of Elections. Proceedings of the National Academy of Sciences 112, 33 (Aug. 2015), E4512--E4521. https://doi.org/10.1073/pnas.1419828112Google ScholarCross Ref
- Sheena Erete, Yolanda A Rankin, and Jakita O Thomas. 2021. I Can't Breathe: Reflections from Black Women in CSCW and HCI. Proceedings of the ACM on Human-Computer Interaction 4, CSCW3 (2021), 1--23.Google ScholarDigital Library
- Adrienne Evans. 2015. Diversity in gender and visual representation: a commentary. Journal of Gender Studies 24, 4 (2015), 473--479. https://doi.org/10.1080/09589236.2015.1047628Google ScholarCross Ref
- Johan Fornäs. 1994. Mirroring meetings, mirroring media: The microphysics of reflexivity. Cultural Studies 8, 2 (1994), 321--340. https://doi.org/10.1080/09502389400490481Google ScholarCross Ref
- Sarah Fox, Amanda Menking, Stephanie Steinhardt, Anna Lauren Hoffmann, and Shaowen Bardzell. 2017. Imagining intersectional futures: Feminist approaches in CSCW. In Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. 387--393.Google ScholarDigital Library
- Ben Gomes. 2017. Our latest quality improvements for Search. https://blog.google/products/search/our-latest-qualityimprovements-search/.Google Scholar
- Amber M. Hamilton. [n.d.]. A Genealogy of Critical Race and Digital Studies: Past, Present, and Future. Sociology of Race and Ethnicity ([n. d.]), 2332649220922577. https://doi.org/10.1177/2332649220922577Google ScholarCross Ref
- Anikó Hannák, Claudia Wagner, David Garcia, Alan Mislove, Markus Strohmaier, and Christo Wilson. 2017. Bias in Online Freelance Marketplaces: Evidence from TaskRabbit and Fiverr. In 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2017). Portland, OR.Google ScholarDigital Library
- Donna Haraway. 2013. Simians, cyborgs, and women: The reinvention of nature. Routledge.Google Scholar
- Matthew Kay, Cynthia Matuszek, and Sean A. Munson. 2015. Unequal Representation and Gender Stereotypes in Image Search Results for Occupations. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems - CHI '15. ACM Press, Seoul, Republic of Korea, 3819--3828. https://doi.org/10.1145/2702123.2702520Google ScholarDigital Library
- Rene F Kizilcec, Andrew Saltarelli, Petra Bonfert-Taylor, Michael Goudzwaard, Ella Hamonic, and Rémi Sharrock. 2020. Welcome to the Course: Early Social Cues Influence Women's Persistence in Computer Science. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1--13.Google ScholarDigital Library
- Juhi Kulshrestha, Motahhare Eslami, Johnnatan Messias, Muhammad Bilal Zafar, Saptarshi Ghosh, Krishna P. Gummadi, and Karrie Karahalios. 2019. Search Bias Quantification: Investigating Political Bias in Social Media and Web Search. Information Retrieval Journal 22, 1--2 (April 2019), 188--227. https://doi.org/10.1007/s10791-018-9341-2Google ScholarDigital Library
- Asaf Levanon, Paula England, and Paul Allison. 2009. Occupational Feminization and Pay: Assessing Causal Dynamics Using 1950--2000 U.S. Census Data. Social Forces 88, 2 (12 2009), 865--891. https://doi.org/10.1353/sof.0.0264Google ScholarCross Ref
- Danaë Metaxa, Joon Sung Park, James A. Landay, and Jeff Hancock. 2019. Search Media and Elections: A Longitudinal Investigation of Political Search Results in the 2018 U.S. Elections. In Proceedings of the 22nd ACM Conference on Computer-Supported Cooperative Work and Social Computing. ACM.Google Scholar
- Danaë Metaxa-Kakavouli, Kelly Wang, James A. Landay, and Jeff Hancock. 2018. Gender-inclusive design: sense of belonging and bias in web interfaces. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1--6. https://doi.org/10.1145/3173574.3174188Google ScholarDigital Library
- Andrew A. Mitchell. 1986. The Effect of Verbal and Visual Components of Advertisements on Brand Attitudes and Attitude Toward the Advertisement. Journal of Consumer Research 13, 1 (06 1986), 12--24. https://doi.org/10.1086/209044Google ScholarCross Ref
- Perry L Moriearty. 2009. Framing justice: Media, bias, and legal decisionmaking. Md. L. Rev. 69 (2009), 849.Google Scholar
- Mary C Murphy, Claude M Steele, and James J Gross. 2007. Signaling threat: How situational cues affect women in math, science, and engineering settings. Psychological science 18, 10 (2007), 879--885.Google ScholarCross Ref
- Safiya Umoja Noble. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. New York University Press, New York.Google Scholar
- U.S. Bureau of Labor Statistics. 2019. Employed persons by detailed occupation, sex, race, and Hispanic or Latino ethnicity. https://www.bls.gov/cps/aa2018/cpsaat11.htmGoogle Scholar
- U.S. Bureau of Labor Statistics. 2020. Employed persons by detailed occupation, sex, race, and Hispanic or Latino ethnicity. https://www.bls.gov/cps/cpsaat11.htmGoogle Scholar
- Jahna Otterbacher, Alessandro Checco, Gianluca Demartini, and Paul Clough. 2018. Investigating user perception of gender bias in image search: the role of sexism. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 933--936. https://doi.org/10.1145/3209978.3210094Google ScholarDigital Library
- Timothy D. Pippert, Laura J. Essenburg, and Edward J. Matchett. 2013. We've got minorities, yes we do: visual representations of racial and ethnic diversity in college recruitment materials. Journal of Marketing for Higher Education 23, 2 (2013), 258--282. https://doi.org/10.1080/08841241.2013.867920Google ScholarCross Ref
- Jean-Christophe Plantin, Carl Lagoze, Paul N. Edwards, and Christian Sandvig. 2018. Infrastructure studies meet platform studies in the age of Google and Facebook. New Media & Society 20, 1 (2018), 293--310. https://doi.org/10. 1177/1461444816661553Google ScholarCross Ref
- Victoria C Plaut, Flannery G Garnett, Laura E Buffardi, and Jeffrey Sanchez-Burks. 2011. ?What about me?" Perceptions of exclusion and Whites' reactions to multiculturalism. Journal of personality and social psychology 101, 2 (2011), 337.Google ScholarCross Ref
- Valerie Purdie-Vaughns, Claude M Steele, Paul G Davies, Ruth Ditlmann, and Jennifer Randall Crosby. 2008. Social identity contingencies: how diversity cues signal threat or safety for African Americans in mainstream institutions. Journal of personality and social psychology 94, 4 (2008), 615.Google ScholarCross Ref
- Inioluwa Deborah Raji and Joy Buolamwini. 2019. Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (Honolulu, HI, USA) (AIES '19). Association for Computing Machinery, New York, NY, USA, 429--435. https://doi.org/10.1145/3306618.3314244Google ScholarDigital Library
- Ronald E. Robertson, David Lazer, and Christo Wilson. 2018. Auditing the Personalization and Composition of Politically-Related Search Engine Results Pages. In Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18. ACM Press, Lyon, France, 955--965. https://doi.org/10.1145/3178876.3186143Google ScholarDigital Library
- Christian Sandvig, Kevin Hamilton, Karrie Karahalios, and Cédric Langbort. 2014. Auditing Algorithms: Research Methods for Detecting Discrimination on Internet Platforms. Data and Discrimination: Converting Critical Concerns into Productive Inquiry 22 (2014).Google Scholar
- Susan Leigh Star. 1999. The Ethnography of Infrastructure. American Behavioral Scientist 43, 3 (1999), 377--391. https://doi.org/10.1177/00027649921955326Google ScholarCross Ref
- statcounter [n.d.]. Search Engine Market Share Worldwide. https://gs.statcounter.com/search-engine-market-shareGoogle Scholar
- Dafina-Lazarus Stewart. 2017. Language of Appeasement. https://www.insidehighered.com/views/2017/03/30/collegesneed-language-shift-not-one-you-think-essay.Google Scholar
- Elaine Swan. 2010. Commodity Diversity: Smiling Faces as a Strategy of Containment. Organization 17, 1 (2010), 77--100. https://doi.org/10.1177/1350508409350043Google ScholarCross Ref
- Latanya Sweeney. 2013. Discrimination in Online Ad Delivery. Queue 11, 3, Article 10 (March 2013), 20 pages. https://doi.org/10.1145/2460276.2460278Google ScholarDigital Library
- Francesca Tripodi. 2018. Searching for Alternative Facts. Technical Report. Data & Society. 64 pages.Google Scholar
Index Terms
- An Image of Society: Gender and Racial Representation and Impact in Image Search Results for Occupations
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