Unequal Representation and Gender Stereotypes in Image Search Results for Occupations

CHI, pp. 3819-3828, 2015.

Cited by: 187|Bibtex|Views125|Links
EI
Keywords:
miscellaneousstereotypesinequalitybiasimage searchMore(2+)
Weibo:
We find that image search results for occupations slightly exaggerate gender stereotypes and portray the minority gender for an occupational less professionally

Abstract:

Information environments have the power to affect people's perceptions and behaviors. In this paper, we present the results of studies in which we characterize the gender bias present in image search results for a variety of occupations. We experimentally evaluate the effects of bias in image search results on the images people choose to ...More

Code:

Data:

Introduction
  • Billions of people interact with interfaces that help them access information and make decisions.
  • As increasing amounts of information become available, systems designers turn to algorithms to select which information to show to whom
  • These algorithms and the interfaces built on them can influence people’s behaviors and perceptions about the world.
  • The Internet and large data sets create many new opportunities for engaging with data and using it in communication and to support decision making
  • They come with challenges and pitfalls.
  • The authors' research questions were guided by prior work in stereotyping and biases, the role of media in forming, perpetuating, or challenging these, and contemporary discussions of the effects of stereotypes and biases information environments.
Highlights
  • Every day, billions of people interact with interfaces that help them access information and make decisions
  • In the studies presented in this paper, we investigate the prevalence and risks of gender-based stereotyping and bias in image search results for occupations
  • We choose the portrayal of occupations because it is a topic of societal importance that has recently received attention and efforts to ameliorate biases. While efforts such as the partnership between Getty Images and Lean In may make more diverse or positive images available, and to those who access the Lean In collection, many people turn to major search engines when looking to illustrate a topic, and so we focus our attention on the image search results for a major search engine
  • We considered asking turkers whether the person in question has the given occupation; this implicitly asks them to decide if a person of that gender could be a hairdresser, so we opted to filter occupations with multiple genders in the majority of images
  • We find that image search results for occupations slightly exaggerate gender stereotypes and portray the minority gender for an occupational less professionally
  • If stereotyping occurs in image results, we would expect a profession with 75% males in the Bureau of Labor and Statistics to have more than 75% males in the image results
  • There is a slight underrepresentation of women. This stereotype exaggeration is consistent with perceptions of result quality – people believe results are better when they agree with the stereotype – but risks reinforcing or even increasing perceptions of actual gender segregation in careers
Methods
  • The authors used the top 8 male and female images from each profession, as these images will be used again in study 3, below.
  • The authors piloted a study in which people were asked to give 5 adjectives describing the person in each image, but found that this task was too difficult.
  • The authors had turkers indicate on a 5-point scale whether they felt each adjective described the person in the picture.
  • Each image was rated by at least 3 turkers
Results
  • A requirement of this study was to obtain a representative dataset of images of individuals in different occupations with properly labelled gender.
  • This required some filtering to ensure that images had correctly labelled genders, depicted only people of that gender, and were generally images of people in the first place.
Conclusion
  • The authors' results provide guidance on the short-term effects of possible changes to search engine algorithms and highlight tensions in possible designs of search algorithms.

    As a design space, what other kinds of search results could the authors design and what might be the consequences?

    There are two sets of adjustments that can be made: adjusting the gender distribution, and adjusting the distribution of qualitative image characteristics within genders.
  • There is a slight underrepresentation of women
  • This stereotype exaggeration is consistent with perceptions of result quality – people believe results are better when they agree with the stereotype – but risks reinforcing or even increasing perceptions of actual gender segregation in careers.
  • Addressing concerns such as these in search engines and other information sources, requires balancing design tensions.
  • The authors hope to advance a constructive discussion on gender representation as a design dimension in information systems
Summary
  • Introduction:

    Billions of people interact with interfaces that help them access information and make decisions.
  • As increasing amounts of information become available, systems designers turn to algorithms to select which information to show to whom
  • These algorithms and the interfaces built on them can influence people’s behaviors and perceptions about the world.
  • The Internet and large data sets create many new opportunities for engaging with data and using it in communication and to support decision making
  • They come with challenges and pitfalls.
  • The authors' research questions were guided by prior work in stereotyping and biases, the role of media in forming, perpetuating, or challenging these, and contemporary discussions of the effects of stereotypes and biases information environments.
  • Methods:

    The authors used the top 8 male and female images from each profession, as these images will be used again in study 3, below.
  • The authors piloted a study in which people were asked to give 5 adjectives describing the person in each image, but found that this task was too difficult.
  • The authors had turkers indicate on a 5-point scale whether they felt each adjective described the person in the picture.
  • Each image was rated by at least 3 turkers
  • Results:

    A requirement of this study was to obtain a representative dataset of images of individuals in different occupations with properly labelled gender.
  • This required some filtering to ensure that images had correctly labelled genders, depicted only people of that gender, and were generally images of people in the first place.
  • Conclusion:

    The authors' results provide guidance on the short-term effects of possible changes to search engine algorithms and highlight tensions in possible designs of search algorithms.

    As a design space, what other kinds of search results could the authors design and what might be the consequences?

    There are two sets of adjustments that can be made: adjusting the gender distribution, and adjusting the distribution of qualitative image characteristics within genders.
  • There is a slight underrepresentation of women
  • This stereotype exaggeration is consistent with perceptions of result quality – people believe results are better when they agree with the stereotype – but risks reinforcing or even increasing perceptions of actual gender segregation in careers.
  • Addressing concerns such as these in search engines and other information sources, requires balancing design tensions.
  • The authors hope to advance a constructive discussion on gender representation as a design dimension in information systems
Tables
  • Table1: Factors affecting image selection in Study 3. Coefficients are on a logit scale. Note the stereotype effect: greater % image gender in BLS is associated with higher probability that an image is selected
  • Table2: Factors affecting search result quality ratings in Study 3. Coefficients are on a logit scale
  • Table3: Effects of the manipulated search result and a person’s pre-existing opinion of % women in an occupation on their opinion after seeing the manipulated result (Study 4)
Download tables as Excel
Funding
  • Presents the results of studies in which characterizes the gender bias present in image search results for a variety of occupations
  • Evaluates the effects of bias in image search results on the images people choose to represent those careers and on people’s perceptions of the prevalence of men and women in each occupation
  • Finds evidence for both stereotype exaggeration and systematic underrepresentation of women in search results
  • Finds that people rate search results higher when they are consistent with stereotypes for a career, and shifting the representation of gender in image search results can shift people’s perceptions about real-world distributions
  • Evaluates whether and how these biases affect people’s perceptions of search result quality, their beliefs about the occupations represented, and the choices they make
Reference
  • Arigbabu OA; Ahmad SMS, Adnan WAN, Yussof S, Iranmanesh V, Malallah, FL, Gender recognition on real world faces based on shape representation and neural network. ICCOINS 2014.
    Google ScholarLocate open access versionFindings
  • Baker P, Potts A. “Why do white people have thin lips?” Google and the perpetuation of stereotypes via autocomplete search forms. Crit Disc St 10(2): 187-204.
    Google ScholarLocate open access versionFindings
  • Behm-Morawitz E, Mastro D. The Effects of the Sexualization of Female Video Game Characters on Gender Stereotyping and Female Self-Concept. Sex Roles 2009; 61(11-12): 808-823.
    Google ScholarLocate open access versionFindings
  • Bodenhausen GV, Wyer RS. Effects of stereotypes in decision making and information-processing strategies. J Pers Soc Psychol 1985; 48(2): 267. 5. Bureau of Labor Statistics. Labor Force Statistics from the Current Population Survey, Section 11. 5 February 2013. http://www.bls.gov/cps/aa2012/cpsaat11.htm.Coltrane S, Adams M. Work–family imagery and gender stereotypes: Television and the reproduction of difference. J Vocat Behav 1997; 50(2):323-347.
    Locate open access versionFindings
  • 6. Correll SJ. Gender and the career choice process: The role of biased self-assessments. Am J Sociol 2001; 106(6): 1691–1730.
    Google ScholarLocate open access versionFindings
  • 7. Correll SJ. Constraints into preferences: Gender, status, and emerging career aspirations. Am Sociol Rev 2004; 69(1): 93-113.
    Google ScholarLocate open access versionFindings
  • 8. Executive Office of the President. Big Data: Seizing Opportunities, Preserving Values. May 2014.
    Google ScholarFindings
  • 9. Friedman B, Nissenbaum H. Bias in computer systems. ACM T Inform Syst 1996; 14(3): 330-347.
    Google ScholarLocate open access versionFindings
  • 10. Gerbner G, Gross L, Morgan M, Signorielli N. Living with television: The dynamics of the cultivation process. Perspectives on media effects 1986: 17-40.
    Google ScholarFindings
  • 11. Graves SB. Television and Prejudice Reduction: When Does Television as a Vicarious Experience Make a Difference? J Soc Issues 1999; 55(4): 707-727.
    Google ScholarLocate open access versionFindings
  • 12. Grossman P. New Partnership with LeanIn.org. InFocus by Getty Images. http://infocus.gettyimages.com/post/new-partnership-with-leaninorg.
    Findings
  • 13. Halpert JA, Wilson ML, Hickman JL. Pregnancy as a source of bias in performance appraisals. J Organ Behav 1993.
    Google ScholarLocate open access versionFindings
  • 14. Haslam SA, Turner JC, Oakes PJ, Reynolds KJ, Doosje, B From personal pictures in the head to collective tools in the word: how shared stereotypes allow groups to represent and change social reality. In C McGarty, VY Yzerbyt, R Spears (eds.). Stereotypes as explanations: The formation of meaningful beliefs about social groups 2002. Cambridge University Press, 157-185.
    Google ScholarFindings
  • 15. Heilman ME. Description and prescription: How gender stereotypes prevent women's ascent up the organizational ladder. J Soc Issues 2001; 57(4): 657-674.
    Google ScholarLocate open access versionFindings
  • 16. Heilman ME. Okimoto, TG. 2008. Motherhood: A potential source of bias in employment decisions. J Appl Psychol 2008; 93(1): 189-198.
    Google ScholarLocate open access versionFindings
  • 17. Hilton JL, & Von Hippel W. Stereotypes. Annu Rev Psychol 1996; 47(1): 237-271.
    Google ScholarLocate open access versionFindings
  • 18. Hooper B. Porn star appears on cover of Thai math textbook. United Press International. http://upi.com/5031410787947
    Findings
  • 19. Introna L, Nissenbaum H. Defining the web: The politics of search engines. Computer 2000; 33(1): 54-62.
    Google ScholarLocate open access versionFindings
  • 20. Jacobs J. Gender Inequality at Work. Thousand Oaks, CA: SAGE Publications, 1995.
    Google ScholarFindings
  • 21. Kammerer,Y, Gerjets P. How search engine users evaluate and select Web search results: The impact of the search engine interface on credibility assessments. Libr Inform Sci 2012; 4: 251–279.
    Google ScholarLocate open access versionFindings
  • 22. Keane MT, O'Brien M, Smyth B. Are people biased in their use of search engines? Commun ACM 2008; 51(2): 49-52.
    Google ScholarLocate open access versionFindings
  • 23. Khorsandi R, Abdel-Mottaleb M. Gender classification using 2-D ear images and sparse representation. 2013 IEEE Workshop on Applications of Computer Vision (WACV), 461-466.
    Google ScholarLocate open access versionFindings
  • 24. Lean In Foundation. Getty Image Collection. http://leanin.org/getty.
    Findings
  • 25. Makinen E, Raisamo R. Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces, IEEE T Pattern Anal 2008; 30(3): 541-547.
    Google ScholarLocate open access versionFindings
  • 26. Massey D. Categorically Unequal: The American Stratification System. NY: Russell Sage Foundation, 2007.
    Google ScholarFindings
  • 27. Miller CC. 10 February 2014. LeanIn.org and Getty Aim to Change Women’s Portrayal in Stock Photos. New York Times, B3. http://nyti.ms/1eLY7ij
    Locate open access versionFindings
  • 28. Pariser E. The Filter Bubble: What the Internet Is Hiding from You. 2011. Penguin Press.
    Google ScholarFindings
  • 29. Potter WJ. Cultivation theory and research, Hum Commun Res 1993; 19(4): 564-601.
    Google ScholarLocate open access versionFindings
  • 30. Shan C. Learning local binary patterns for gender classification on real-world face images, Pattern Recogn Lett 2012; 33(4), 431-437.
    Google ScholarLocate open access versionFindings
  • 31. Shrum LJ. Assessing the Social Influence of Television A Social Cognition Perspective on Cultivation Effects. Commun Res 1995; 22(4): 402-429.
    Google ScholarLocate open access versionFindings
  • 32. Spencer SJ, Steele CM, Quinn DM. Stereotype threat and women's math performance. J Exp Soc Psychol 1999; 35(1): 4-28.
    Google ScholarLocate open access versionFindings
  • 33. Snyder M, Tanke ED, Berscheid E. Social perception and interpersonal behavior: On the self-fulfilling nature of social stereotypes. J Pers Soc Psychol 1977; 35(9): 656–666.
    Google ScholarLocate open access versionFindings
  • 34. Sweeney L. Discrimination in online ad delivery. Commun ACM 2013; 56(5): 44-54.
    Google ScholarLocate open access versionFindings
  • 35. Tajfel H. Social stereotypes and social groups. In Turner JC, Giles H. Intergroup Behaviour 1981. Oxford: Blackwell. 144–167.
    Google ScholarLocate open access versionFindings
  • 36. Vaughan L, Thelwall M. Search engine coverage bias: evidence and possible causes. Inform Process Manag 2004; 40(4): 693-707.
    Google ScholarLocate open access versionFindings
  • 37. Williams D. Virtual Cultivation: Online Worlds, Offline Perceptions. J Commun 1996; 56(1): 69–87.
    Google ScholarLocate open access versionFindings
  • 38. Word CO, Zanna MP, Cooper J. The nonverbal mediation of self-fulfilling prophecies in interracial interaction. J Exp Soc Psychol 1974; 10(2): 109–120.
    Google ScholarLocate open access versionFindings
  • 39. X Tang, K Liu, J Cui, F Wen, X Wang. IntentSearch: Capturing User Intention for One-Click Internet Image Search, IEEE T Pattern Anal 34(7): 1342-1353.
    Google ScholarLocate open access versionFindings
  • 40. Zha ZJ, Yang L, Mei T, Wang M, Wang Z, Chua TS, Hua XS. 2010. Visual query suggestion: Towards capturing user intent in internet image search. ACM T Multim Comput 2010; 6(3): a13.
    Google ScholarLocate open access versionFindings
Your rating :
0

 

Best Paper
Best Paper of CHI, 2015
Tags
Comments