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.
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“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]
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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
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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
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
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