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
Items 301–400 of 507
Mirror, Mirror: Exploring Stereotype Presence Among Top-N Recommendations That May Reach Children
Ungruh, R., Nahadi, M. A., & Pera, M. S. (2025) · ACM Transactions on Recommender Systems · doi:10.1145/3721987 · Cited by 5
Urman, A., Makhortykh, M., & Hannák, A. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732026 · Cited by 6
State media control influences large language models
Waight, H. C., Yang, E., Yuan, Y., Messing, S., Roberts, M. E., Stewart, B. M., & Tucker, J. A. (2026) · Nature · doi:10.1038/s41586-026-10506-7 · Cited by 13
(Dis)Obedience in Digital Societies
Quadflieg, S., Neuburg, K., & Nestler, S. (2022) · Digitale Gesellschaft · doi:10.1515/9783839457634 · Cited by 8
Conceptual Modeling of Gender-Inclusive Requirements
Nunes, I. S., Moreira, A., & Araújo, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-89022-3_31 · Cited by 5
Generating and Controlling Diversity in Image Search
Tanjim, M. M., Sinha, R., Singh, K. K., Mahadevan, S., Arbour, D. T., Sinha, M., & Cottrell, G. W. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00396 · Cited by 5
Face to Face with a Sexist Robot: Investigating How Women React to Sexist Robot Behaviors
Garcha, D. S., Geiskkovitch, D. Y., Thiessen, R., Prentice, S., Fischer, K., & Young, J. (2023) · International Journal of Social Robotics · doi:10.1007/s12369-023-01001-4 · Cited by 6
Online Algorithms for Matchings with Proportional Fairness Constraints and Diversity Constraints
Louis, A., Nasre, M., Nimbhorkar, P., & Sankar, G. S. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230442 · Cited by 5
Ahn, Y., & Lin, Y. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3650044 · Cited by 5
Materialität – Digitalisierung – Bildung
:null (2023) · Verlag Julius Klinkhardt eBooks · doi:10.35468/5979 · Cited by 6
Menking, A., McDonald, D. W., & Zachry, M. R. (2017) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2998181.2998254 · Cited by 5
Distributed Fair k-Center Clustering Problems with Outliers
Yuan, F., Diao, L., Du, D., & Liu, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-96772-7_39 · Cited by 5
Group Fairness in Multiwinner Voting.Preprint
Celis, L. E., Huang, L., & Vishnoi, N. K. (2017) · arXiv (Cornell University) · Cited by 5
Weakly Supervised Machine Learning for Cyberbullying Detection
Raisi, E. (2019) · VTechWorks (Virginia Tech) · Cited by 5
Artificial Intelligence, Machine Learning, and Gender Bias
Weidner, N. U. (2020) · The International Encyclopedia of Gender, Media, and Communication · doi:10.1002/9781119429128.iegmc316 · Cited by 5
Laugwitz, L. L. (2021) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/gt28f · Cited by 6
Inclination of NLP Applications Towards Stereotypical and Gender Biased ResultsPreprint
Chatterjee, M., Ahmed, A. L. S. A. E., Dadure, P., & Pakray, P. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4096410 · Cited by 5
Algorithmic Bias in Image-Generating Artificial Intelligence: Prevalence and User PerceptionsPreprint
Messingschlager, T. V., & Appel, M. (2024) · doi:10.31234/osf.io/wjeps · Cited by 5
“It’s a Whole Vibe”: testing evaluations of grammatical and ungrammatical AAE on Twitter
Holliday, N. R., & Tano, M. (2021) · Linguistics Vanguard · doi:10.1515/lingvan-2020-0095 · Cited by 4
Did You Find It on the Internet? Ethical Complexities of Search Engine Rankings
Canca, C. (2021) · Springer eBooks · doi:10.1007/978-3-030-86144-5_19 · Cited by 4
Bias in Face Image Classification Machine Learning Models: The Impact of Annotator’s Gender and Race
Andreas, Herodotou, S., Θεοδοσίου, Ζ., & Lanitis, A. (2022) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-08337-2_8 · Cited by 4
Cachel, K., & Rundensteiner, E. A. (2022) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3514094.3534160 · Cited by 8
Benchmarking the Fairness of Image Upsampling Methods
Laszkiewicz, M., Daunhawer, I., Vogt, J. E., Fischer, A., & Lederer, J. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658921 · Cited by 7
Examining racial stereotypes in YouTube autocomplete suggestions
Ha, E., Kong, H., & Jhaver, S. (2025) · New Media & Society · doi:10.1177/14614448251346503 · Cited by 4
VLBiasBench: A Comprehensive Benchmark for Evaluating Bias in Large Vision-Language Model
Wang, S., Cao, X., Zhang, J., Yuan, Z., Shan, S., Chen, X., & Gao, W. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3683747 · Cited by 49
COMPUTING EDUCATION RESEARCHComputational making
Tenenberg, J. (2018) · ACM Inroads · doi:10.1145/3177861 · Cited by 4
Measuring Embedded Human-Like Biases in Face Recognition Models
Lee, S., Oh, S., Kim, M., & Park, E. (2022) · doi:10.3390/cmsf2022003002 · Cited by 5
Gender Bias in Big Data Analysis
Misa, T. J. (2022) · Information & Culture · doi:10.7560/ic57303 · Cited by 4
Dissecting users' needs for search result explanations
Juneja, P., Zhang, W., Smith, A., Lamba, H., Tetreault, J., & Jaimes, A. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642059 · Cited by 14
: Mitigating Gender Bias in Neural Team Recommendation via Female-Advocate Loss Regularization
Moasses, R., Rajaei, D., Loghmani, H. G., Saeedi, M., & Fani, H. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-71975-2_6 · Cited by 4
Internet image search outputs propagate climate change sentiment and impact policy support
Berkebile-Weinberg, M., Gao, R., Tang, R., & Vlasceanu, M. (2024) · Nature Climate Change · doi:10.1038/s41558-024-02178-w · Cited by 10
Analysing Gender Bias in IMDB Films Based on Social Networks
Chen, J., & Cui, M. (2020) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/806/1/012022 · Cited by 4
A General Methodology to Quantify Biases in Natural Language Data
Chen, J., Xu, A., Liu, Z., Guo, Y., Liu, X., Tong, Y., Akkiraju, R., & Carroll, J. M. (2020) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3334480.3382949 · Cited by 5
Gender Bias in Italian Word Embeddings
Biasion, D., Fabris, A., Silvello, G., & Susto, G. A. (2020) · Accademia University Press eBooks · doi:10.4000/books.aaccademia.8280 · Cited by 4
Madaan, N., Mehta, S., Mittal, S., & Suvarna, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.10615 · Cited by 4
ViBE: A Tool for Measuring and Mitigating Bias in Image DatasetsPreprint
Wang, A., Narayanan, A., & Russakovsky, O. (2020) · arXiv (Cornell University) · Cited by 4
A new degree of freedom for opinion dynamics models: the arbitrariness of scalesPreprint
Carpentras, D., Dinkelberg, A., & Quayle, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.04788 · Cited by 4
User Acceptance of Gender Stereotypes in Automated Career RecommendationsPreprint
Wang, C., Wang, K., Bian, A., Islam, R., Keya, K. N., Foulds, J. R., & Pan, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.07112 · Cited by 14
Critical Empirical Study on Black-box Explanations in AIPreprint
John-Mathews, J.-M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.15067 · Cited by 4
Bangura, G. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5087870 · Cited by 4
Johnson, D. K. N. (2025) · The Review of Black Political Economy · doi:10.1177/00346446251331615 · Cited by 3
Understanding Perceptions of Algorithmic Bias Through the Risk Perception and Attitude Framework
Overbye-Thompson, H. E., Garcia, E. A., Zhang, X., & Wang, L. H. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2546661 · Cited by 3
On Designing Visceral Encounters with Synthetic Intimate Imagery
Ortega, A. G., Genç, H. U., van der Maden, W., Comber, R., Lampinen, A., & Balaam, M. (2026) · Designing Interactive Systems Conference · doi:10.1145/3800645.3812969 · Cited by 3
Risk and responsibility in human-robot interaction
Fischer, K. (2023) · Pragmatics & beyond. New series · doi:10.1075/pbns.336.07fis · Cited by 3
The Impact of Group Membership Bias on the Quality and Fairness of Exposure in Ranking
Vardasbi, A., de Rijke, M., Díaz, F., & Dehghani, M. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657752 · Cited by 3
Antar, A. D., Molaei, S., Chen, Y.-Y., Lee, M. L., & Banović, N. (2024) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3654777.3676323 · Cited by 9
Efficient Bias Mitigation Without Privileged Information
Zarlenga, M. E., Sankaranarayanan, S., Andrews, J. T. A., Shams, Z., Jamnik, M., & Xiang, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73220-1_9 · Cited by 5
When computers stand in the schoolhouse door
Savage, N. (2016) · Communications of the ACM · doi:10.1145/2875029 · Cited by 3
Maximizing Submodular Functions for Recommendation in the Presence of Biases
Mehrotra, A., & Vishnoi, N. K. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583195 · Cited by 3
Computer Vision, Human Likeness, and Problematic Behaviors
Otterbacher, J. (2023) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3563359.3597381 · Cited by 3
Reflexive Practices in Software Engineering
Boyd, A. E. (2023) · doi:10.1109/fairware59297.2023.00006 · Cited by 4
Zhang, Z., Yang, J., Liu, L., Xu, X., Rong, G., & Feng, Q. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i15.29638 · Cited by 3
"(Weitergeleitet von Journalistin)": The Gendered Presentation of Professions on Wikipedia
Zagovora, O., Flöck, F., & Wagner, C. (2017) · Social Science Open Access Repository (GESIS – Leibniz Institute for the Social Sciences) · doi:10.48550/arxiv.1706.03848 · Cited by 3
"Sharing small pieces of the world"
Menking, A., Rangarajan, V., & Gilbert, M. (2018) · International Symposium on Open Collaboration · doi:10.1145/3233391.3233537 · Cited by 3
Gamified Text Testing for Sustainable Fairness
Takan, S., Ergün, D., & Katipoğlu, G. (2023) · Sustainability · doi:10.3390/su15032292 · Cited by 3
Privacy-Preserving Fair Item Ranking
Sun, J. A., Pentyala, S., Cock, M. D., & Farnadi, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-28238-6_13 · Cited by 3
Klenk, M. (2020) · Diametros · doi:10.33392/diam.1520 · Cited by 3
Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving restPreprint
Ilanchezian, I., Vepakomma, P., Singh, A., Gupta, O., Prasanna, G. N. S., & Raskar, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.12734 · Cited by 4
Dialect Diversity in Text Summarization on TwitterPreprint
Celis, L. E., & Keswani, V. (2020) · arXiv (Cornell University) · Cited by 2
Diagnosing Gender Bias in Image Recognition SystemsPreprint
Schwemmer, C., Knight, C. R., Bello-Pardo, E. D., Oklobdzija, S. N., Schoonvelde, M., & Lockhart, J. W. (2018) · doi:10.31235/osf.io/as25q · Cited by 3
An Anti-Subordination Approach to Fair ClassificationPreprint
Keswani, V., & Celis, L. E. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4116835 · Cited by 3
The Algorithmic Public Opinion: a Policy OverviewPreprint
Reviglio, U. (2022) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/bjfkm · Cited by 4
Schwerpunkt: Digitale Transformation und Gender Bias
Bröcher, L., Gredel, E., & Schelenz, L. (2023) · GENDER – Zeitschrift für Geschlecht Kultur und Gesellschaft · doi:10.3224/gender.v15i1 · Cited by 2
Clair, J. A., & Struzska-Tyamayev, M. M. (2023) · Oxford Research Encyclopedia of Business and Management · doi:10.1093/acrefore/9780190224851.013.49 · Cited by 3
Lütz, F. (2023) · GENDER – Zeitschrift für Geschlecht Kultur und Gesellschaft · doi:10.3224/gender.v15i1.03 · Cited by 4
Extending the Understanding of Gendered Career Choices in STEM: A Culture-rooted Theoretical Model
Gewinner, I., Bauer, V. A., & Osterburg, M. (2024) · Emerald Group Publishing Limited eBooks · doi:10.1108/s2056-375220240000010004 · Cited by 2
The Effect of Gender De-biased Recommendations — A User Study on Gender-specific Preferences
Krause, T., Göritz, L., & Gratz, R. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713155 · Cited by 2
ImageBiTe: A Framework for Evaluating Representational Harms in Text-to-Image Models
Morales, S., Clarisó, R., & Cabot, J. (2025) · IEEE/ACM International Conference on AI Engineering – Software Engineering for AI (CAIN) · doi:10.1109/cain66642.2025.00019 · Cited by 2
Chou, W. S., Gaysynsky, A., Senft, N., Muro, A., Schrader, K. E., & Iles, I. A. (2025) · Health Communication · doi:10.1080/10410236.2025.2537807 · Cited by 5
What does genius look like? Investigating brilliance bias in AI-generated images
Shihadeh, J., Ackerman, M., & Loker, D. (2025) · Ai & Society · doi:10.1007/s00146-025-02752-6 · Cited by 3
Through the AI looking glass: measuring gendered objectification in user-generated AI images
Asim, A. (2025) · AI and Ethics · doi:10.1007/s43681-025-00895-2 · Cited by 2
Social stereotypes in AI text-to-image generation
Barve, S., Mao, A., Shi, J. M., Juneja, P., & Saha, K. (2026) · AI and Ethics · doi:10.1007/s43681-026-01146-8 · Cited by 16
AI Loves Boobies: Unpacking Harmful Imaginaries in Community-Based Generative AI
Ortega, A. G., van der Maden, W., Lampinen, A., Comber, R., & Balaam, M. (2026) · Designing Interactive Systems Conference · doi:10.1145/3800645.3812786 · Cited by 2
Computational Methods in Professional Communication
Valdez, A. S. C., Adam, L., Assenmacher, D., Burbach, L., Bonart, M., Frischlich, L., & Schar, P. (2019) · IEEE International Professional Communication Conference (ProComm) · doi:10.1109/procomm.2019.00063 · Cited by 2
Auditing for Diversity Using Representative Examples
Keswani, V., & Celis, L. E. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467433 · Cited by 3
Gender, Sex, and the Constraints of Machine Learning Methods
Lockhart, J. W. (2023) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197653609.013.24 · Cited by 2
Digital divides in scene recognition: uncovering socioeconomic biases in deep learning systems
Greene, M. R., Josyula, M., Si, W., & Hart, J. A. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-04719-w · Cited by 1
A set of distinct facial traits learned by machines is not predictive of appearance bias in the wild
Steed, R., & Caliskan, A. (2021) · AI and Ethics · doi:10.1007/s43681-020-00035-y · Cited by 4
How Fair is Fairness-aware Representative Ranking?
Saxena, A., Fletcher, G., & Pechenizkiy, M. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442442.3453458 · Cited by 1
Fair Regret Minimization Queries
Ma, Y., & Zheng, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-91608-4_51 · Cited by 2
Fairness in Socio-Technical Systems: A Case Study of Wikipedia
Damadi, M. S., & Davoust, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-42141-9_6 · Cited by 2
Incentives for Item Duplication Under Fair Ranking Policies
Nunzio, G. M. D., Fabris, A., Silvello, G., & Susto, G. A. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-78818-6_7 · Cited by 2
Investigating Misinformation in Online Marketplaces: An Audit Study on AmazonPreprint
Hussein, E., & Eldardiry, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.12468 · Cited by 4
Issues in Computer Vision Data Collection: Bias, Consent, and Label Taxonomy
Dulhanty, C. (2020) · UWSpace (University of Waterloo) · Cited by 3
FairyTED: A Fair Rating Predictor for TED Talk Data
Acharyya, R., Das, S., Chattoraj, A., & Tanveer, M. I. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i01.5368 · Cited by 1
Advanced Analytics for Ethical Considerations in Mining Industry
Kaul, A., & Soofastaei, A. (2022) · Springer eBooks · doi:10.1007/978-3-030-91589-6_3 · Cited by 1
Three Implementation Gaps to Harnessing Inclusive AI in Organizations
Collett, C., Neff, G., & Axente, M. (2024) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003320791-30 · Cited by 1
A (1+varepsilon)-approximation algorithm for diversity-aware k-median
ZHANG, Z., Xiaohong, C. H. E. N., Limei, L. I. U., Jian, R. E. N., JIANG, L., & Feng, Q. (2024) · Scientia Sinica Informationis · doi:10.1360/ssi-2024-0108 · Cited by 1
Shaw, P. (2024) · Media Watch · doi:10.1177/09760911241299133 · Cited by 1
GENIE: Socially Unbiased Generative Text-to-Image Editing
Lau, J. K., Phan, R. C. ‐., Rajanala, S., Cox, I. J., & Pal, A. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888322 · Cited by 1
Shaw, P., & Rabindranath, M. (2025) · Journal of Creative Communications · doi:10.1177/09732586251348885 · Cited by 2
Gender disparities in Chinese middle school science textbooks: A semiotic analysis
Zhao, J., & Wotipka, C. M. (2025) · Women s Studies International Forum · doi:10.1016/j.wsif.2025.103192 · Cited by 4
Zhang, Z., Yang, J., Liu, L., Xu, X., Rong, G., & Feng, Q. (2025) · Information and Computation · doi:10.1016/j.ic.2025.105343 · Cited by 1
Research Methods for the Analysis of Visual Emotion Cues in Animals: A Workshop Report
Correia‐Caeiro, C., Zamansky, A., Karl, S., & Bremhorst, A. (2025) · Animals · doi:10.3390/ani15213142 · Cited by 1
Fairness in Machine Learning: A Review for Statisticians
He, X., & Li, Y. (2025) · Journal of the American Statistical Association · doi:10.1080/01621459.2025.2579579 · Cited by 2
AI and Gender—The Ambivalent Role of AI in Advancing Gender Equality for SDG 5
Stecker, L., Boecker, M., Kowol, U., & Raniga, T. (2026) · Journal of Human Rights and Social Work · doi:10.1007/s41134-025-00432-5 · Cited by 3
Who Gets Written In? Gender, Identity, and Moderation in AO3’s Celebrity Fanfiction
Zhang, P., & Gareth Tyson, A. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791973 · Cited by 1
Kim, S., Jo, Y., Na, S., Lim, H., Lee, E., Choi, Y. M., Cho, S., & Hong, H. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790820 · Cited by 2
Aligning Recommendations with User Popularity Preferences
Schirmer, M., Thielmann, A., Schwöbel, P., Martynec, T., Benedetto, G. D., London, B., & Stein, Y. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3806483 · Cited by 1
The UK Online Gender Audit 2018: A comprehensive audit of gender within the UK's online environment
Huluba, A.-M., Kingdon, J., & McLaren, I. (2018) · Heliyon · doi:10.1016/j.heliyon.2018.e01001 · Cited by 1
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
