Unequal Representation and Gender Stereotypes in Image Search Results for Occupations
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 represent those careers and on people's perceptions of the prevalence of men and women in each occupation. We find evidence for both stereotype exaggeration and systematic underrepresentation of women in search results. We also find 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. We also discuss tensions between desires for high-quality results and broader societal goals for equality of representation in this space.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Image search results for occupations slightly exaggerate gender stereotypes and portray the minority gender for an occupation less professionally, and there is a slight under-representation of women.
“Kay, Matuszek and Munson found that “image search results for occupations slightly exaggerate gender stereotypes and portray the minority gender for an occupation less professionally. There is also a slight under-representation of women.””
From Discrimination, artificial intelligence, and algorithmic decision-making (2025) · cites this paper as Kay, Matuszek and Munson 2015
In recruitment, algorithmic bias can manifest in gender, race, skin color, and personality.
“In recruitment, algorithmic bias can manifest in gender, race, skin color, and personality.”
From Ethics and discrimination in artificial intelligence-enabled recruitment practices (2023) · cites this paper as Kay et al. (2015)
Certain users on advertising platforms are more valuable to advertisers than others.
“For example, it is well-known that certain users on advertising platforms are more valuable to advertisers than others [48,55,65].”
From Discrimination through Optimization (2019) · cites this paper as [48]
Cites
(2011) · Cited by 4,430 · see citing works
Stereotype Threat and Women's Math Performance
Spencer, S. J., Steele, C. M., & Quinn, D. M. (1999) · Journal of Experimental Social Psychology · doi:10.1006/jesp.1998.1373 · Cited by 3,435 · see citing works
Heilman, M. E. (2001) · Journal of Social Issues · doi:10.1111/0022-4537.00234 · Cited by 2,482 · see citing works
Gender and the Career Choice Process: The Role of Biased Self‐Assessments
Correll, S. J. (2001) · American Journal of Sociology · doi:10.1086/321299 · Cited by 1,320 · see citing works
Social perception and interpersonal behavior: On the self-fulfilling nature of social stereotypes.
Snyder, M., Tanke, E. D., & Berscheid, E. (1977) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.35.9.656 · Cited by 1,312 · see citing works
Friedman, B., & Nissenbaum, H. (1996) · ACM Transactions on Information Systems · doi:10.1145/230538.230561 · Cited by 1,203 · cited 2× · see citing works
“Both algorithms and interfaces, however, can be biased in how they represent the world [9,34].”
BackgroundHilton, J. L., & von Hippel, W. (1996) · Annual Review of Psychology · doi:10.1146/annurev.psych.47.1.237 · Cited by 980 · see citing works
Constraints into Preferences: Gender, Status, and Emerging Career Aspirations
Correll, S. J. (2004) · American Sociological Review · doi:10.1177/000312240406900106 · Cited by 934 · see citing works
The nonverbal mediation of self-fulfilling prophecies in interracial interaction
Word, C. O., Zanna, M. P., & Cooper, J. (1974) · Journal of Experimental Social Psychology · doi:10.1016/0022-1031(74)90059-6 · Cited by 902 · see citing works
Categorically unequal: the American stratification system
(2008) · Choice Reviews Online · doi:10.5860/choice.45-4699 · Cited by 900 · see citing works
Discrimination in online ad delivery
Sweeney, L. (2013) · Communications of the ACM · doi:10.1145/2447976.2447990 · Cited by 783 · see citing works
Living with Television: The Dynamics of the Cultivation Process
Morgan, M. (1986) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · Cited by 772 · see citing works
Effects of stereotypes in decision making and information-processing strategies.
Bodenhausen, G. V., & Wyer, R. S. (1985) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.48.2.267 · Cited by 482 · see citing works
Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces
Mäkinen, E., & Raisamo, R. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70800 · Cited by 358 · see citing works
Learning local binary patterns for gender classification on real-world face images
Shan, C. (2011) · Pattern Recognition Letters · doi:10.1016/j.patrec.2011.05.016 · Cited by 350 · see citing works
Motherhood: A potential source of bias in employment decisions.
Heilman, M. E., & Okimoto, T. G. (2008) · Journal of Applied Psychology · doi:10.1037/0021-9010.93.1.189 · Cited by 342 · see citing works
Social stereotypes and social groups.
Tajfel, H. (2001) · Cited by 336 · see citing works
Graves, S. B. (1999) · Journal of Social Issues · doi:10.1111/0022-4537.00143 · Cited by 296 · see citing works
Search engine coverage bias: evidence and possible causes
Vaughan, L., & Thelwall, M. (2004) · Information Processing & Management · doi:10.1016/s0306-4573(03)00063-3 · Cited by 259 · see citing works
Cultivation Theory and Research.
Potter, W. J. (1993) · Human Communication Research · doi:10.1111/j.1468-2958.1993.tb00313.x · Cited by 253 · see citing works
Behm‐Morawitz, E., & Mastro, D. E. (2009) · Sex Roles · doi:10.1007/s11199-009-9683-8 · Cited by 252 · see citing works
Virtual Cultivation: Online Worlds, Offline Perceptions
Williams, D. C. (2006) · Journal of Communication · doi:10.1111/j.1460-2466.2006.00004.x · Cited by 203 · see citing works
Assessing the Social Influence of Television
Shrum, L. J. (1995) · Communication Research · doi:10.1177/009365095022004002 · Cited by 182 · see citing works
Pregnancy as a source of bias in performance appraisals
Halpert, J. A., Wilson, M. L., & Hickman, J. L. (1993) · Journal of Organizational Behavior · doi:10.1002/job.4030140704 · Cited by 180 · see citing works
Are people biased in their use of search engines?
Keane, M. T., O’Brien, M., & Smyth, B. (2008) · Communications of the ACM · doi:10.1145/1314215.1314224 · Cited by 170 · see citing works
Baker, P., & Potts, A. (2013) · Critical Discourse Studies · doi:10.1080/17405904.2012.744320 · Cited by 135 · see citing works
Zha, Z.-J., Yang, L., Mei, T., Wang, M., Wang, Z., Chua, T., & Hua, X. (2010) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/1823746.1823747 · Cited by 125 · see citing works
IntentSearch: Capturing User Intention for One-Click Internet Image Search
Tang, X., Liu, K., Cui, J., Wen, F., & Wang, X. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.242 · Cited by 114 · see citing works
Work–Family Imagery and Gender Stereotypes: Television and the Reproduction of Difference
Coltrane, S., & Adams, M. A. (1997) · Journal of Vocational Behavior · doi:10.1006/jvbe.1996.1575 · Cited by 111 · see citing works
Defining the Web: the politics of search engines
Introna, L. D., & Nissenbaum, H. F. (2000) · Computer · doi:10.1109/2.816269 · Cited by 109 · see citing works
(1995) · Choice Reviews Online · doi:10.5860/choice.32-5946 · Cited by 103 · see citing works
Haslam, S. A., Turner, J. C., Oakes, P. J., Reynolds, K. J., & Doosje, B. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511489877.009 · Cited by 92 · see citing works
Jacobs, J. A. (1995) · Cited by 57 · see citing works
Kammerer, Y., & Gerjets, P. H. (2012) · Library and information science · doi:10.1108/s1876-0562(2012)002012a012 · Cited by 48 · see citing works
Gender classification using 2-D ear images and sparse representation
Khorsandi, R., & Abdel-Mottaleb, M. S. (2013) · Proceedings · doi:10.1109/wacv.2013.6475055 · Cited by 28 · see citing works
Gender recognition on real world faces based on shape representation and neural network
Arigbabu, O. A., Ahmad, S. M. S., Adnan, W. A. W., Yussof, S., Iranmanesh, V., & Malallah, F. L. (2014) · doi:10.1109/iccoins.2014.6868361 · Cited by 12 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676 · see citing works
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016) · arXiv · doi:10.48550/arxiv.1607.06520 · Cited by 1,615 · see citing works
Baker, R. S., & Hawn, A. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00285-9 · Cited by 993 · see citing works
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1323 · Cited by 829 · see citing works
“Such issues have been demonstrated in various learning systems, including online advertisement systems (Sweeney, 2013), word embedding models (Bolukbasi et al., 2016; Caliskan et al., 2017), online news (Ross and Carter, 2011), web search (Kay et al., 2015), and credit score (Hardt et al., 2016).”
BackgroundImproving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?
Holstein, K., Wortman Vaughan, J., Daumé, H., Dudik, M., & Wallach, H. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300830 · Cited by 777 · see citing works
Gender bias in coreference resolution: Evaluation and debiasing methods
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) · doi:10.18653/v1/n18-2003 · Cited by 684 · see citing works
Fairness of Exposure in Rankings
Singh, A., & Joachims, T. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220088 · Cited by 607 · see citing works
A Review on Fairness in Machine Learning
Pessach, D., & Shmueli, E. (2023) · ACM Computing Surveys · doi:10.1145/3494672 · Cited by 598 · see citing works
Kaur, H., Nori, H., Jenkins, S., Caruana, R., Wallach, H., & Wortman Vaughan, J. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376219 · Cited by 564 · see citing works
Ethics and discrimination in artificial intelligence-enabled recruitment practices
Chen, Z. (2023) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-02079-x · Cited by 450 · see citing works
Fairness testing: testing software for discrimination
Galhotra, S., Brun, Y., & Meliou, A. (2017) · Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering · doi:10.1145/3106237.3106277 · Cited by 405 · see citing works
Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
Geyik, S. C., Ambler, S., & Kenthapadi, K. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330691 · Cited by 382 · see citing works
Learning Gender-Neutral Word Embeddings
Zhao, J., Zhou, Y., Li, Z., Wang, W., & Chang, K.-W. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1521 · Cited by 376 · see citing works
Wang, T., Zhao, J., Yatskar, M., Chang, K.-W., & Ordóñez, V. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00541 · Cited by 353 · see citing works
Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale
Bianchi, F., Kalluri, P., Durmus, E., Ladhak, F., Cheng, M., Nozza, D., Hashimoto, T., Jurafsky, D., Zou, J., & Caliskan, A. (2023) · 2023 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3593013.3594095 · Cited by 342 · see citing works
The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity
Howard, A., & Borenstein, J. (2018) · Science and Engineering Ethics · doi:10.1007/s11948-017-9975-2 · Cited by 337 · see citing works
Gender Bias in Contextualized Word Embeddings
Zhao, J., Wang, T., Yatskar, M., Cotterell, R., Ordóñez, V., & Chang, K.-W. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1064 · Cited by 341 · see citing works
Discrimination through Optimization
Ali, M., Sapiezynski, P., Bogen, M., Korolova, A., Mislove, A., & Rieke, A. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359301 · Cited by 268 · see citing works
“s have shown price discrimination in online retail sites [40], gender discrimination in job sites [15, 41], stereotypical gender roles re-enforced by online translation services [12] and image search [43], disparate performance on gender classi˙cation for Black women [13], and political partisanships in search [19, 47…”
BackgroundLarge image datasets: A pyrrhic win for computer vision?
Birhane, A., & Prabhu, V. U. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv48630.2021.00158 · Cited by 257 · see citing works
Yang, K., Qinami, K., Fei-Fei, L., Deng, J., & Russakovsky, O. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3375709 · Cited by 254 · see citing works
Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction
Shelby, R., Rismani, S., Henne, K., Moon, A., Rostamzadeh, N., Nicholas, P., Yilla-Akbari, N., Gallegos, J., Smart, A., Garcia, E., et al. (2023) · Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3600211.3604673 · Cited by 279 · see citing works
A Qualitative Exploration of Perceptions of Algorithmic Fairness
Woodruff, A., Fox, S. E., Rousso-Schindler, S., & Warshaw, J. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174230 · Cited by 190 · see citing works
Ranking with Fairness ConstraintsPreprint
Celis, L. E., Straszak, D., & Vishnoi, N. K. (2017) · arXiv (Cornell University) · doi:10.4230/lipics.icalp.2018.28 · Cited by 237 · see citing works
For You, or For"You"?: Everyday LGBTQ+ Encounters with TikTok
Simpson, E., & Semaan, B. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3432951 · Cited by 230 · see citing works
HCI Research as Problem-Solving
Oulasvirta, A., & Hornbæk, K. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858283 · Cited by 225 · see citing works
Auditing Partisan Audience Bias within Google Search
Robertson, R. E., Jiang, S., Joseph, K., Friedland, L., Lazer, D., & Wilson, C. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274417 · Cited by 209 · see citing works
Design Within a Patriarchal Society
Sultana, S., Guimbretière, F. V., Sengers, P., & Dell, N. L. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174110 · Cited by 199 · see citing works
Scheuerman, M. K., Wade, K., Lustig, C., & Brubaker, J. R. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3392866 · Cited by 193 · see citing works
Trick and Please. A Mixed-Method Study On User Assumptions About the TikTok Algorithm
Klug, D., Qin, Y., Evans, M. C., & Kaufman, G. F. (2021) · ACM Web Science Conference (WebSci) · doi:10.1145/3447535.3462512 · Cited by 238 · see citing works
Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits
Bandy, J. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449148 · Cited by 193 · see citing works
Algorithmic governance: A modes of governance approach
Gritsenko, D., & Wood, M. (2020) · Regulation & Governance · doi:10.1111/rego.12367 · Cited by 219 · see citing works
The end of media logics? On algorithms and agency
Klinger, U., & Svensson, J. (2018) · New Media & Society · doi:10.1177/1461444818779750 · Cited by 170 · see citing works
Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases
Steed, R., & Caliskan, A. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445932 · Cited by 167 · see citing works
Investigating the Impact of Gender on Rank in Resume Search Engines
Chen, L., Ma, R., Hannák, A., & Wilson, C. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174225 · Cited by 155 · see citing works
How can we manage biases in artificial intelligence systems – A systematic literature review
P．S．, D. V. (2023) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2023.100165 · Cited by 239 · see citing works
Discrimination, artificial intelligence, and algorithmic decision-making
Borgesius, F. Z. (2025) · arXiv · doi:10.48550/arxiv.2510.13465 · Cited by 152 · see citing works
Chen, N.-C., Drouhard, M., Kocielnik, R., Suh, J., & Aragon, C. R. (2018) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3185515 · Cited by 152 · see citing works
On Formalizing Fairness in Prediction with Machine LearningPreprint
Gajane, P., & Pechenizkiy, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.03184 · Cited by 152 · see citing works
Preventing undesirable behavior of intelligent machines
Thomas, P. S., da Silva, B. C., Barto, A. G., Giguere, S., Brun, Y., & Brunskill, E. (2019) · Science · doi:10.1126/science.aag3311 · Cited by 142 · see citing works
Fairness in rankings and recommendations: an overview
Pitoura, E., Stefanidis, K., & Koutrika, G. (2021) · The VLDB Journal · doi:10.1007/s00778-021-00697-y · Cited by 150 · see citing works
Addressing age-related bias in sentiment analysis,
Diaz, M., Johnson, I., Lazar, A., Piper, A. M., & Gergle, D. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173986 · Cited by 138 · see citing works
Brun, Y., & Meliou, A. (2018) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3236024.3264838 · Cited by 138 · see citing works
A survey on bias in visual datasets
Fabbrizzi, S., Papadopoulos, S., Ntoutsi, E., & Kompatsiaris, I. Y. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103552 · Cited by 142 · see citing works
Building and Auditing Fair Algorithms: A Case Study in Candidate Screening
Wilson, C., Ghosh, A., Jiang, S., Mislove, A., Baker, L., Szary, J., Trindel, K., & Polli, F. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445928 · Cited by 133 · see citing works
From Reality to World. A Critical Perspective on AI Fairness
John-Mathews, J.-M., Cardon, D., & Balagué, C. (2022) · Journal of Business Ethics · doi:10.1007/s10551-022-05055-8 · Cited by 124 · see citing works
Otterbacher, J., Bates, J., & Clough, P. D. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025727 · Cited by 128 · see citing works
Toward fairness in AI for people with disabilities SBG@a research roadmap
Guo, A., Kamar, E., Vaughan, J. W., Wallach, H., & Morris, M. R. (2020) · ACM SIGACCESS Accessibility and Computing · doi:10.1145/3386296.3386298 · Cited by 128 · see citing works
Žliobaitė, I., & Custers, B. (2016) · Artificial Intelligence and Law · doi:10.1007/s10506-016-9182-5 · Cited by 125 · see citing works
Societal Biases in Language Generation: Progress and Challenges
Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.330 · Cited by 131 · see citing works
Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power?
Miceli, M., Posada, J., & Yang, T. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3492853 · Cited by 125 · see citing works
Social Biases through the Text-to-Image Generation Lens
Naik, R., & Nushi, B. (2023) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3600211.3604711 · Cited by 127 · see citing works
Perspectives on Digital Humanism
Werthner, H., Prem, E., Lee, E. A., & Ghezzi, C. (2021) · Springer eBooks · doi:10.1007/978-3-030-86144-5 · Cited by 117 · see citing works
Discrimination through Optimization: How Facebook's Ad Delivery Can Lead to Biased OutcomesPreprint
Ali, M., Sapieżyński, P., Bogen, M., Korolova, A., Mislove, A. E., & Rieke, A. (2019) · arXiv (Cornell University) · Cited by 116 · see citing works
Propagation of societal gender inequality by internet search algorithms
Vlasceanu, M., & Amodio, D. M. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2204529119 · Cited by 109 · see citing works
Trielli, D., & Diakopoulos, N. A. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300683 · Cited by 111 · see citing works
Toward creating a fairer ranking in search engine results
Gao, R., & Shah, C. S. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.102138 · Cited by 107 · see citing works
Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook
Yildirim, N., Pushkarna, M., Goyal, N., WATTENBERG, M. P., & Viégas, F. B. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580900 · Cited by 107 · see citing works
Pessach, D., & Shmueli, E. (2023) · Springer eBooks · doi:10.1007/978-3-031-24628-9_37 · Cited by 177 · see citing works
Policy Learning for Fairness in RankingPreprint
Singh, A., & Joachims, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.04056 · Cited by 102 · see citing works
Does Object Recognition Work for Everyone?Preprint
DeVries, T., Misra, I., Wang, C., & van der Maaten, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.02659 · Cited by 132 · see citing works
Toward a Geographic Understanding of the Sharing Economy
Thebault-Spieker, J., Terveen, L., & Hecht, B. J. (2017) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3058499 · Cited by 101 · see citing works
Women’s Participation in Open Source Software: A Survey of the Literature
Trinkenreich, B., Wiese, I., Sarma, A., Gerosa, M. A., & Steinmacher, I. (2022) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3510460 · Cited by 101 · see citing works
DeVos, A., Dhabalia, A., Shen, H., Holstein, K., & Eslami, M. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517441 · Cited by 98 · see citing works
Bryant, D. G., Borenstein, J., & Howard, A. M. (2020) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3319502.3374778 · Cited by 98 · see citing works
McMahon, C., Johnson, I. L., & Hecht, B. J. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14883 · Cited by 97 · see citing works
Themis: automatically testing software for discrimination
Angell, R., Johnson, B., Brun, Y., & Meliou, A. (2018) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3236024.3264590 · Cited by 94 · see citing works
Gender-Inclusive HCI Research and Design: A Conceptual Review
Stumpf, S., Peters, A. N., Bardzell, S., Burnett, M., Busse, D. K., Cauchard, J. R., & Churchill, E. F. (2020) · Foundations and Trends® in Human–Computer Interaction · doi:10.1561/1100000056 · Cited by 107 · see citing works
Visually Grounded Reasoning across Languages and Cultures
Liu, F., Bugliarello, E., Ponti, E. M., Reddy, S., Collier, N., & Elliott, D. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.818 · Cited by 93 · see citing works
Quantifying the Impact of User Attentionon Fair Group Representation in Ranked Lists
Sapiezynski, P., Zeng, W., E Robertson, R., Mislove, A., & Wilson, C. (2019) · Companion Proceedings of The 2019 World Wide Web Conference · doi:10.1145/3308560.3317595 · Cited by 92 · see citing works
Diagnosing Gender Bias in Image Recognition Systems
Schwemmer, C., Knight, C. R., Bello-Pardo, E. D., Oklobdzija, S. N., Schoonvelde, M., & Lockhart, J. W. (2020) · Socius Sociological Research for a Dynamic World · doi:10.1177/2378023120967171 · Cited by 91 · see citing works
Multiwinner Voting with Fairness Constraints
Celis, L. E., Huang, L., & Vishnoi, N. K. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/20 · Cited by 86 · see citing works
Wu, Y., Cao, J., Xu, G., & Tan, Y. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462882 · Cited by 85 · see citing works
Hagendorff, T., Bossert, L. N., Tse, Y. F., & Singer, P. (2022) · AI and Ethics · doi:10.1007/s43681-022-00199-9 · Cited by 91 · see citing works
Gender Bias in Coreference Resolution: Evaluation and Debiasing MethodsPreprint
Zhao, J., Wang, T., Yatskar, M., Ordóñez, V., & Chang, K.-W. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.06876 · Cited by 85 · see citing works
Understanding and Evaluating Racial Biases in Image Captioning
Zhao, D., Wang, A., & Russakovsky, O. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01456 · Cited by 84 · see citing works
Improving fairness in machine learning systems: What do industry practitioners need?Preprint
Holstein, K., Vaughan, J. W., Daumé, H., Dudík, M., & Wallach, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.05239 · Cited by 81 · see citing works
Examining Gender Bias in Languages with Grammatical Gender
Zhou, P., Shi, W., Zhao, J., Huang, K.-H., Chen, M., Cotterell, R., & Chang, K.-W. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1531 · Cited by 81 · see citing works
REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets
Wang, A., Liu, A., Zhang, R., Kleiman, A., Kim, L., Zhao, D., Shirai, I., Narayanan, A., & Russakovsky, O. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01625-5 · Cited by 84 · see citing works
REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets
Wang, A., Narayanan, A., & Russakovsky, O. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58580-8_43 · Cited by 70 · see citing works
Discrimination through optimization: How Facebook's ad delivery can lead to skewed outcomes
Ali, M., Sapiezynski, P., Bogen, M., Korolova, A., Mislove, A., & Rieke, A. (2019) · arXiv · doi:10.48550/arxiv.1904.02095 · Cited by 72 · see citing works
Rekabsaz, N., Kopeinik, S., & Schedl, M. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462949 · Cited by 73 · see citing works
Solyst, J., Yang, E., Xie, S., Ogan, A., Hammer, J., & Eslami, M. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610213 · Cited by 67 · see citing works
Gendered Conversation in a Social Game-Streaming Platform
Nakandala, S., Ciampaglia, G. L., Su, N. M., & Ahn, Y. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14885 · Cited by 67 · see citing works
Auditing News Curation Systems: A Case Study Examining Algorithmic and Editorial Logic in Apple News
Bandy, J., & Diakopoulos, N. A. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7277 · Cited by 66 · see citing works
Do neural ranking models intensify gender bias?
Rekabsaz, N., & Schedl, M. (2020) · Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3397271.3401280 · Cited by 64 · see citing works
Singh, V. K., Chayko, M., Inamdar, R., & Floegel, D. (2020) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24335 · Cited by 69 · see citing works
Perfectly parallel fairness certification of neural networks
Urban, C., Christakis, M., Wüstholz, V., & Zhang, F. (2020) · Proceedings of the ACM on Programming Languages · doi:10.1145/3428253 · Cited by 62 · see citing works
Partisan search behavior and Google results in the 2018 U.S. midterm elections
Trielli, D., & Diakopoulos, N. A. (2020) · Information Communication & Society · doi:10.1080/1369118x.2020.1764605 · Cited by 61 · see citing works
Theory In, Theory Out: The Uses of Social Theory in Machine Learning for Social Science
Radford, J., & Joseph, K. (2020) · Frontiers in Big Data · doi:10.3389/fdata.2020.00018 · Cited by 58 · see citing works
The Effect of Population and "Structural" Biases on Social Media-based Algorithms
Johnson, I. L., McMahon, C., Schöning, J., & Hecht, B. J. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3026015 · Cited by 62 · see citing works
Extending Manual Drawing Practices with Artist-Centric Programming Tools
Jacobs, J., Brandt, J., Měch, R., & Resnick, M. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174164 · Cited by 59 · see citing works
User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets
Wang, L., & Joachims, T. (2021) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3471158.3472260 · Cited by 57 · see citing works
Investigating Gender and Racial Biases in DALL-E Mini Images
Cheong, M., Abedin, E., Ferreira, M., Reimann, R., Chalson, S., Robinson, P., Byrne, J., Ruppanner, L., Alfano, M., & Klein, C. (2024) · ACM Journal on Responsible Computing · doi:10.1145/3649883 · Cited by 57 · see citing works
Interpretable Data-Based Explanations for Fairness Debugging
Pradhan, R., Zhu, J., Glavic, B., & Salimi, B. (2022) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3514221.3517886 · Cited by 54 · see citing works
Sühr, T., Hilgard, S., & Lakkaraju, H. (2021) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3461702.3462602 · Cited by 54 · see citing works
Silva: Interactively Assessing Machine Learning Fairness Using Causality
Yan, J. N., Gu, Z., Lin, H., & Rzeszotarski, J. M. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376447 · Cited by 54 · see citing works
Metaxa, D., Gan, M. A., Goh, S., Hancock, J. T., & Landay, J. A. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449100 · Cited by 53 · see citing works
Mitigating Bias in Algorithmic Systems—A Fish-eye View
Orphanou, K., Otterbacher, J., Kleanthous, S., Batsuren, K., Giunchiglia, F., Bogina, V., Tal, A. S., Hartman, A., & Kuflik, T. (2022) · ACM Computing Surveys · doi:10.1145/3527152 · Cited by 54 · see citing works
Gender stereotype reinforcement: Measuring the gender bias conveyed by ranking algorithms
Fabris, A., Purpura, A., Silvello, G., & Susto, G. A. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102377 · Cited by 58 · see citing works
Representativeness in Statistics, Politics, and Machine Learning
Chasalow, K., & Levy, K. E. C. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445872 · Cited by 52 · see citing works
- Authors
- Kay, M., Matuszek, C., & Munson, S. A.
- Affiliations
- University of Washington (US)
- University of Maryland, Baltimore County (US)
- Venue
- Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2015-04-18
- Pages
- 3819-3828
- OpenAlex
- W2149252982
- Cited by
- 509
- References
- 36
- Citations by year
- 2026: 27 · 2025: 36 · 2024: 66 · 2023: 65 · 2022: 64 · 2021: 84 · 2020: 56 · 2019: 41 · 2018: 36 · 2017: 23 · 2016: 10 · 2012: 1
- FWCI
- 110.27
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Gender Studies › Media, Gender, and Advertising
- Social Sciences › Psychology › Developmental and Educational Psychology › Science Education and Perceptions
- Keywords
- gender stereotypes, gender bias, image search, occupation, gender representation, underrepresentation of women, search result quality
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
