Contrastive Language-Vision AI Models Pretrained on Web-Scraped Multimodal Data Exhibit Sexual Objectification Bias
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RNNs' proficiency in handling sequential information renders them indispensable in applications requiring deep understanding of temporal dynamics, such as natural language tasks and time-series analysis.
“This proficiency in handling sequential information renders them indispensable in applications that require a deep understanding of the temporal dynamics within data, such as natural language tasks and time-series analysis [155], [156].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [155]
LLMs and other generative AI models exhibit novel bias categories, including gender stereotypes, exclusionary norms, disability-related biases, religious stereotypes, and sexual objectification.
“In addition to known categories of bias observed in predictive ML models, new types of bias arise in LLMs and other generative AI models, e.g., gender stereotypes, exclusionary norms, undesirable biases towards mentions of disability, religious stereotypes, and sexual objectification [10,30,106,122].”
From Grounding and Evaluation for Large Language Models: Practical Challenges and Lessons Learned (Survey) (2024) · cites this paper as [122]
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 21,793 · see citing works
High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01042 · Cited by 15,880 · see citing works
Learning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293 · see citing works
Proceedings of IEEE International Conference on Computer Vision
(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117 · see citing works
A Style-Based Generator Architecture for Generative Adversarial Networks
Karras, T., Laine, S., & Aila, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00453 · Cited by 11,714 · see citing works
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821 · see citing works
PaLM: Scaling Language Modeling with Pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590 · see citing works
Taming Transformers for High-Resolution Image Synthesis
Esser, P., Rombach, R., & Ommer, B. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01268 · Cited by 2,514 · see citing works
Hierarchical Text-Conditional Image Generation with CLIP Latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022) · arXiv · doi:10.48550/arxiv.2204.06125 · Cited by 2,511 · see citing works
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., & Crawford, K. (2018) · arXiv · doi:10.48550/arxiv.1803.09010 · Cited by 2,474
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., III, H. D., & Crawford, K. (2021) · Communications of the ACM · doi:10.1145/3458723 · Cited by 2,469 · see citing works
Zero-shot text-to-image generation
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., & Sutskever, I. (2021) · arXiv · doi:10.48550/arxiv.2102.12092 · Cited by 2,417 · see citing works
DeViSE: A Deep Visual-Semantic Embedding Model
Frome, A., Corrado, G. S., Shlens, J., Samy Bengio, A., Dean, J., Ranzato, M., & Mikolov, T. (2013) · Cited by 2,309 · see citing works
Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning
Sharma, P., Ding, N., Goodman, S., & Soricut, R. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1238 · Cited by 1,879 · see citing works
Li, J., Li, D., Xiong, C., & Hoi, S. (2022) · arXiv · doi:10.48550/arxiv.2201.12086 · Cited by 1,741 · see citing works
Mitigating Unwanted Biases with Adversarial Learning
Zhang, B. H., Lemoine, B., & Mitchell, M. (2018) · Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3278721.3278779 · Cited by 1,387 · see citing works
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., & Chen, M. (2021) · arXiv · doi:10.48550/arxiv.2112.10741 · Cited by 1,357 · see citing works
Swim, J. K., Hyers, L. L., Cohen, L. L., & Ferguson, M. J. (2001) · Journal of Social Issues · doi:10.1111/0022-4537.00200 · Cited by 803 · see citing works
Grad-CAM: Why did you say that?Preprint
Selvaraju, R. R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., & Batra, D. (2016) · arXiv.org · doi:10.48550/arxiv.1611.07450 · Cited by 646 · see citing works
ClipCap: CLIP Prefix for Image CaptioningPreprint
Mokady, R., Hertz, A., & Bermano, A. H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.09734 · Cited by 605 · see citing works
Open-vocabulary Object Detection via Vision and Language Knowledge DistillationPreprint
Gu, X., Lin, T.-Y., Kuo, W., & Cui, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.13921 · Cited by 581 · see citing works
Data and its (dis)contents: A survey of dataset development and use in machine learning research
Paullada, A., Raji, I. D., Bender, E. M., Denton, E., & Hanna, A. (2021) · Patterns · doi:10.1016/j.patter.2021.100336 · Cited by 548 · cited 2× · see citing works
“However, due to lack of curation (Paullada et al., 2021), these models also tend to encode humanlike social biases, such as the association of Muslims with terrorism (Goh et al., 2021), or younger individuals with crime (Agarwal et al., 2021).”
BackgroundSurvey of Academic Field Experiences (SAFE): Trainees Report Harassment and Assault
Clancy, K. B. H., Nelson, R. G., Rutherford, J. N., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0102172 · Cited by 505 · see citing works
Contrastive Learning of Medical Visual Representations from Paired Images and TextPreprint
Zhang, Y., Jiang, H., Miura, Y., Manning, C. D., & Langlotz, C. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.00747 · Cited by 465 · see citing works
On Measuring Social Biases in Sentence Encoders
May, C., Wang, A., Bordia, S., Bowman, S. R., & Rudinger, R. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1063 · Cited by 461 · see citing works
The Woman Worked as a Babysitter: On Biases in Language Generation
Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1339 · Cited by 431 · cited 2× · see citing works
“[70] find that language models such as GPT-2 [60] output text showing low "regard" for women and gender minorities, including sexualizing biases in text output.”
BackgroundLAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs
Schuhmann, C., Vencu, R., Beaumont, R., Kaczmarczyk, R., Mullis, C., Katta, A., Coombes, T., Jitsev, J., & Komatsuzaki, A. (2021) · arXiv · doi:10.48550/arxiv.2111.02114 · Cited by 358 · see citing works
Media and sexualization: State of empirical research, 1995–2015
Ward, L. M. (2016) · The Journal of Sex Research · doi:10.1080/00224499.2016.1142496 · Cited by 337 · see citing works
Heflick, N. A., Goldenberg, J. L., Cooper, D., & Puvia, E. (2010) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2010.12.020 · Cited by 299 · see citing works
Sexual Objectification in Music Videos: A Content Analysis Comparing Gender and Genre
Aubrey, J. S., & Frisby, C. M. (2011) · Mass Communication & Society · doi:10.1080/15205436.2010.513468 · Cited by 248 · see citing works
Martin, K. E. (2022) · Auerbach Publications eBooks · doi:10.1201/9781003278290 · Cited by 247 · see citing works
Multimodal Neurons in Artificial Neural Networks
Goh, G., Cammarata, N., Voss, C., Carter, S., Petrov, M., Schubert, L., Radford, A., & Olah, C. (2021) · Distill · doi:10.23915/distill.00030 · Cited by 240 · see citing works
Becoming an object: A review of self-objectification in girls
Daniels, E. A., Zurbriggen, E. L., & Ward, L. M. (2020) · Body Image · doi:10.1016/j.bodyim.2020.02.016 · Cited by 207 · see citing works
Objectification Theory, Self-Objectification, and Body Image
Calogero, R. M. (2012) · Encyclopedia of Body Image and Human Appearance · doi:10.1016/b978-0-12-384925-0.00091-2 · Cited by 197 · see citing works
Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society
(2022) · doi:10.1145/3514094 · Cited by 182 · see citing works
Guo, W., & Caliskan, A. (2021) · Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3461702.3462536 · Cited by 180 · see citing works
Self-citationImage 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
Self-citationMultimodal datasets: misogyny, pornography, and malignant stereotypes
Birhane, A., Prabhu, V. U., & Kahembwe, E. (2021) · arXiv · doi:10.48550/arxiv.2110.01963 · Cited by 160 · see citing works
The Content of Reality Dating Shows and Viewer Perceptions of Dating
Ferris, A. L., Smith, S. W., Greenberg, B. S., & Smith, S. L. (2007) · Journal of Communication · doi:10.1111/j.1460-2466.2007.00354.x · Cited by 146 · see citing works
Tiggemann, M., & Slater, A. (2015) · Journal of Pediatric Psychology · doi:10.1093/jpepsy/jsv021 · Cited by 132 · see citing works
Seeing women as objects: The sexual body part recognition bias
Gervais, S. J., Vescio, T. K., Förster, J., Maass, A., & Suitner, C. (2012) · European Journal of Social Psychology · doi:10.1002/ejsp.1890 · Cited by 130 · see citing works
Verifying images: deepfakes, control, and consent
van der Nagel, E. (2020) · Porn Studies · doi:10.1080/23268743.2020.1741434 · Cited by 110 · see citing works
Gender Bias in Word Embeddings
Caliskan, A., Ajay, P. P., Charlesworth, T. E. S., Wolfe, R., & Banaji, M. R. (2022) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3514094.3534162 · Cited by 84 · see citing works
Self-citationKim, E., Bryant, D. G., Srikanth, D., & Howard, A. (2021) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3461702.3462609 · Cited by 75 · see citing works
Hemamou, L., Felhi, G., Vandenbussche, V., et al. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301573 · Cited by 69 · see citing works
Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right FeaturePreprint
Saleh, B., & Elgammal, A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.00855 · Cited by 68 · see citing works
The Happiness Effect: How Social Media is Driving a Generation to Appear Perfect at Any Cost
Kidd, D. (2018) · Contemporary Sociology A Journal of Reviews · doi:10.1177/0094306118779814o · Cited by 44 · see citing works
CM3: A Causal Masked Multimodal Model of the Internet
Aghajanyan, A., Huang, B., Ross, C., Karpukhin, V., Xu, H., Goyal, N., Okhonko, D., Joshi, M., Ghosh, G., Lewis, M., et al. (2022) · arXiv · doi:10.48550/arxiv.2201.07520 · Cited by 42 · see citing works
Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream ImplicationsPreprint
Agarwal, S., Krueger, G. M., Clark, J., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.02818 · Cited by 42 · see citing works
American == White in Multimodal Language-and-Image AI
Wolfe, R., & Caliskan, A. (2022) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3514094.3534136 · Cited by 36 · see citing works
Self-citationVQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language GuidancePreprint
Crowson, K., Biderman, S., Kornis, D., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2204.08583 · Cited by 30 · see citing works
Now you see me, now you don’t: detecting sexual objectification through a change blindness paradigm
Andrighetto, L., Bracco, F., Chiorri, C., Masini, M., Passarelli, M., & Piccinno, T. F. (2019) · Cognitive Processing · doi:10.1007/s10339-019-00927-w · Cited by 13 · see citing works
Auditing saliency cropping algorithms
Birhane, A., Prabhu, V. U., & Whaley, J. (2022) · IEEE Workshop/Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00158 · Cited by 13 · see citing works
SLIP: Self-supervision meets Language-Image Pre-trainingPreprint
Mu, N., Kirillov, A. M., Wagner, D., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.12750 · Cited by 13 · see citing works
Incorporating Priors with Feature Attribution on Text ClassificationPreprint
Liu, F., & Avci, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.08286 · Cited by 7 · see citing works
Grading video interviews with fairness considerationsPreprint
Singhania, A., Unnam, A., & Aggarwal, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.05461 · Cited by 4 · see citing works
Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image SearchPreprint
Wang, J., Liu, Y., & Wang, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.05433
Flagged first, then by citations
Research works printed but not linked: 18
- Dane Archer, Bonita Iritani, Debra D Kimes, and Michael Barrios. 1983. Face-ism: Five studies of sex differences in facial prominence. Journal of Personality and social Psychology 45, 4 (1983), 725.
- Federico Bianchi, Pratyusha Kalluri, Esin Durmus, Faisal Ladhak, Myra Cheng, Debora Nozza, Tatsunori Hashimoto, Dan Jurafsky, James Zou, and Aylin Caliskan. 2022. Easily accessible text-to-image generation amplifies demographic stereotypes at large scale. arXiv preprint arXiv:2211.03759 (2022). · arXiv
- Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accu- racy disparities in commercial gender classification. In Conference on fairness, accountability and transparency. PMLR, 77-91.
- Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan. 2017. Semantics derived automatically from language corpora contain human-like biases. Science 356, 6334 (2017), 183-186.
- Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, and Ilya Sutskever. 2020. Generative pretraining from pixels. In International Conference on Machine Learning. PMLR, 1691-1703.
- Jacob Cohen. 1992. Statistical power analysis. Current directions in psychological science 1, 3 (1992), 98-101.
- Alice H Eagly and Antonio Mladinic. 1989. Gender stereotypes and attitudes toward women and men. Personality and social psychology bulletin 15, 4 (1989), 543-558.
- Alice H Eagly and Antonio Mladinic. 1994. Are people prejudiced against women? Some answers from research on attitudes, gender stereotypes, and judgments of competence. European review of social psychology 5, 1 (1994), 1-35.
- Barbara L Fredrickson and Tomi-Ann Roberts. 1997. Objectification theory: Toward understanding women's lived experiences and mental health risks. Psy- chology of women quarterly 21, 2 (1997), 173-206.
- Herminia Ibarra, Robin Ely, and Deborah Kolb. 2013. Women rising: The unseen barriers. Harvard business review 91, 9 (2013), 60-66.
- Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V Le, Yunhsuan Sung, Zhen Li, and Tom Duerig. 2021. Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision. arXiv e-prints (2021), arXiv-2102.
- Maddalena Marini and Mahzarin R Banaji. 2020. An implicit gender sex-science association in the general population and STEM faculty. The Journal of General Psychology (2020), 1-28.
- Moin Nadeem, Anna Bethke, and Siva Reddy. 2020. Stereoset: Measuring stereo- typical bias in pretrained language models. arXiv preprint arXiv:2004.09456 (2020). · arXiv
- Joon Sung Park, Michael S Bernstein, Robin N Brewer, Ece Kamar, and Mered- ith Ringel Morris. 2021. Understanding the Representation and Representative- ness of Age in AI Data Sets. arXiv preprint arXiv:2103.09058 (2021). · arXiv
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog 1, 8 (2019), 9.
- Christina Richey. 2015. The CSWA survey on workplace climate and anti- harassment policies. In AAS/Division for Planetary Sciences Meeting Abstracts# 47, Vol. 47. 406-01.
- Richard Socher, Milind Ganjoo, Christopher D Manning, and Andrew Ng. 2013. Zero-Shot Learning Through Cross-Modal Transfer. In Advances in Neural Infor- mation Processing Systems. 935-943.
- Yonglong Tian, Dilip Krishnan, and Phillip Isola. 2019. Contrastive Representation Distillation. In International Conference on Learning Representations.
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McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works
“Moreover, multimodal AI can propagate and amplify biases and stereotypes across different modalities, and if unchecked, this can perpetuate discrimination and social inequities, making it imperative to address algorithmic bias effectively [132], [133], [134].”
BackgroundKenthapadi, K., Sameki, M., & Taly, A. (2024) · Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3637528.3671467 · Cited by 36 · see citing works
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Baines, A., Gruia, L., Collyer-Hoar, G., & Rubegni, E. (2024) · Proceedings of the 23rd Annual ACM Interaction Design and Children Conference · doi:10.1145/3628516.3659404 · Cited by 21 · see citing works
Understanding Gender Bias in AI-Generated Product Descriptions
Kelly, M., Tahaei, M., Smyth, P., & Wilcox, L. (2025) · Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3715275.3732169 · Cited by 4 · see citing works
- Authors
- Wolfe, R., Yang, Y., Howe, B., & Caliskan, A.
- Affiliations
- University of Washington (US)
- Venue
- 2023 ACM Conference on Fairness Accountability and Transparency
- Publisher
- ACM
- Published
- 2023-06-12
- Pages
- 1174-1185
- OpenAlex
- W4377010286
- Cited by
- 42
- References
- 59
- Citations by year
- 2026: 10 · 2025: 14 · 2024: 10 · 2023: 8
- FWCI
- 10.52
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion, cc-by-nc-sa)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- NIST (60NANB20D212T)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- gender bias
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