The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“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 the citing paper
“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)
In the citing paper
“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]
Citations
Cites
(2011) · Cited by 4,430
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
Heilman, M. E. (2001) · Journal of Social Issues · doi:10.1111/0022-4537.00234 · Cited by 2,482
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 1,374
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
Friedman, B., & Nissenbaum, H. (1996) · ACM Transactions on Information Systems · doi:10.1145/230538.230561 · Cited by 1,203 · cited 2×
“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 1,118
Constraints into Preferences: Gender, Status, and Emerging Career Aspirations
Correll, S. J. (2004) · American Sociological Review · doi:10.1177/000312240406900106 · Cited by 934
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
Categorically unequal: the American stratification system
(2008) · Choice Reviews Online · doi:10.5860/choice.45-4699 · Cited by 900
Research works printed but not linked: 2
- Executive Office of the President. Big Data: Seizing Opportunities , Preserving Values. May 2014 . Executive Office of the President. Big Data: Seizing Opportunities, Preserving Values. May 2014.
- Hooper B. Porn star appears on cover of Thai math textbook . United Press International . http://upi.com/5031410787947 Hooper B. Porn star appears on cover of Thai math textbook. United Press International. http://upi.com/5031410787947
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
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
Baker, R. S., & Hawn, A. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00285-9 · Cited by 993
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
“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
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
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 673
A Review on Fairness in Machine Learning
Pessach, D., & Shmueli, E. (2023) · ACM Computing Surveys · doi:10.1145/3494672 · Cited by 598
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
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
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
“, advertisements [75], hotel bookings [53], and image search [45].”
BackgroundFairness-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 428
Learning Gender-Neutral Word Embeddings
Zhao, J., Zhou, Y., Li, Z., Wang, W. W., & Chang, K.-W. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1521 · Cited by 426
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
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
“Further, the Stable Diffusion model makes these errors even when such photos clearly exist on the web: even Google Image Search—which in the past has sparked controversy for its amplification of societal bias [34, 44, 48, 62]— is capable of showing us, upon searching for “African man and his mansion,” an African man,…”
BackgroundThe 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
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 367
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
“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
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 256
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
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
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 344
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
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 251
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
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
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
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
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
Algorithmic governance: A modes of governance approach
Gritsenko, D., & Wood, M. (2020) · Regulation & Governance · doi:10.1111/rego.12367 · Cited by 238
The end of media logics? On algorithms and agency
Klinger, U., & Svensson, J. (2018) · New Media & Society · doi:10.1177/1461444818779750 · Cited by 170
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 189
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
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
Discrimination, artificial intelligence, and algorithmic decision-making
Borgesius, F. Z. (2025) · arXiv · doi:10.48550/arxiv.2510.13465 · Cited by 152
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
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
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 156
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 177
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
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
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 164
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
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 144
Otterbacher, J., Bates, J., & Clough, P. D. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025727 · Cited by 149
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
Žliobaitė, I., & Custers, B. (2016) · Artificial Intelligence and Law · doi:10.1007/s10506-016-9182-5 · Cited by 125
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
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
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 186
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 133
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
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
Trielli, D., & Diakopoulos, N. A. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300683 · Cited by 111
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 114
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 126
Pessach, D., & Shmueli, E. (2023) · Springer eBooks · doi:10.1007/978-3-031-24628-9_37 · Cited by 235
Policy Learning for Fairness in RankingPreprint
Singh, A., & Joachims, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.04056 · Cited by 122
Does Object Recognition Work for Everyone?Preprint
DeVries, T., Misra, I., Wang, C., & van der Maaten, L. J. P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.02659 · Cited by 141
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 109
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 106
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
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
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
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
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 114
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 119
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
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 101
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
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
Hagendorff, T., Bossert, L. N., Tse, Y. F., & Singer, P. (2022) · AI and Ethics · doi:10.1007/s43681-022-00199-9 · Cited by 102
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 330
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 85
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
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
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 95
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 73
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
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 79
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 68
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
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
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
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
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
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
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
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
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
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 71
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
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 65
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
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
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 65
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 58
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 63
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 61
Metadata
- 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
- FWCI
- 110.27
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › 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
