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Recent studies have shown that Text-to-Image (T2I) model generations can reflect social stereotypes present in the real world.
Crows-pairs: A challenge dataset for measuring social biases in masked language models
Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel Bowman. 2020 · 1967
Earlier work this paper cites.
Towards a critical race methodology in algorithmic fairness
Alex Hanna, Emily Denton, Andrew Smart, and Jamila Smith-Loud. 2020 · 2020
Earlier work this paper cites.
Towards fairer datasets: Filtering and balancing the distribution of the people subtree in the imagenet hierarchy
Kaiyu Yang, Klint Qinami, Li Fei-Fei, Jia Deng, and Olga Russakovsky. 2020 · 2020
Earlier work this paper cites.
Stereotyping Norwegian salmon: An inventory of pitfalls in fairness benchmark datasets
Su Lin Blodgett, Gilsinia Lopez, Alexandra Olteanu, Robert Sim, and Hanna Wallach. 2021 · 2021
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Stereoset: Measuring stereotypical bias in pretrained language models
Moin Nadeem, Anna Bethke, and Siva Reddy. 2021 · 2021
Earlier work this paper cites.
On releasing annotator-level labels and information in datasets
Vinodkumar Prabhakaran, Aida Mostafazadeh Davani, and Mark Díaz. 2021 · 2021
Earlier work this paper cites.
Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021 · 2021
Earlier work this paper cites.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
Earlier work this paper cites.
Inspecting the geographical representativeness of images from text-to-image models
Abhipsa Basu, R Venkatesh Babu, and Danish Pruthi. 2023 · 2023
Earlier work this paper cites.
Easily accessible text-to-image generation amplifies demographic stereotypes at large scale
Federico Bianchi, Pratyusha Kalluri, Esin Durmus, Faisal Ladhak, Myra Cheng, Debora Nozza, Tatsunori Hashimoto, Dan Jurafsky, James Zou, and Aylin Caliskan. 2023 · 2023
Cited alongside, same era.
Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models
Jaemin Cho, Abhay Zala, and Mohit Bansal. 2023 · 2023
Cited alongside, same era.
Uncurated image-text datasets: Shedding light on demographic bias
Noa Garcia, Yusuke Hirota, Yankun Wu, and Yuta Nakashima. 2023 · 2023
Cited alongside, same era.
‘person’ == light-skinned, western man, and sexualization of women of color: Stereotypes in stable diffusion
Sourojit Ghosh and Aylin Caliskan. 2023 · 2023
Cited alongside, same era.
Facet: Fairness in computer vision evaluation benchmark
Laura Gustafson, Chloe Rolland, Nikhila Ravi, Quentin Duval, Aaron Adcock, Cheng-Yang Fu, Melissa Hall, and Candace Ross. 2023 · 2023
Cited alongside, same era.
Ai’s regimes of representation: A community-centered study of text-to-image models in south asia
Rida Qadri, Renee Shelby, Cynthia L Bennett, and Emily Denton. 2023 · 2023
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Consensus and subjectivity of skin tone annotation for ml fairness
Candice Schumann, Gbolahan O Olanubi, Auriel Wright, Ellis Monk Jr, Courtney Heldreth, and Susanna Ricco. 2023 · 2023
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The tail wagging the dog: Dataset construction biases of social bias benchmarks
Nikil Selvam, Sunipa Dev, Daniel Khashabi, Tushar Khot, and Kai-Wei Chang. 2023 · 2023
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Sociotechnical harms of algorithmic systems: Scoping a taxonomy for harm reduction
Renee Shelby, Shalaleh Rismani, Kathryn Henne, AJung Moon, Negar Rostamzadeh, Paul Nicholas, N’Mah Yilla-Akbari, Jess Gallegos, Andrew Smart, Emilio Garcia, et al. 2023 · 2023
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Stereotypes and smut: The (mis)representation of non-cisgender identities by text-to-image models
Eddie Ungless, Bjorn Ross, and Anne Lauscher. 2023 · 2023
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Susan Hao, Piyush Kumar, Sarah Laszlo, Shivani Poddar, Bhaktipriya Radharapu, and Renee Shelby. 2023 · 2023
Cited alongside, same era.
SeeGULL: A stereotype benchmark with broad geo-cultural coverage leveraging generative models
Akshita Jha, Aida Mostafazadeh Davani, Chandan K Reddy, Shachi Dave, Vinodkumar Prabhakaran, and Sunipa Dev. 2023 · 2023
Cited alongside, same era.
Stable bias: Evaluating societal representations in diffusion models
Sasha Luccioni, Christopher Akiki, Margaret Mitchell, and Yacine Jernite. 2023 · 2023
Cited alongside, same era.
Social biases through the text-to-image generation lens
Ranjita Naik and Besmira Nushi. 2023 · 2023
Cited alongside, same era.
Later among the works it cites.
T2iat: Measuring valence and stereotypical biases in text-to-image generation
Jialu Wang, Xinyue Gabby Liu, Zonglin Di, Yang Liu, and Xin Eric Wang. 2023 · 2023
Later among the works it cites.
Auditing gender presentation differences in text-to-image models
Yanzhe Zhang, Lu Jiang, Greg Turk, and Diyi Yang. 2023 · 2023
Later among the works it cites.
Examining gender and racial bias in large vision–language models using a novel dataset of parallel images
Kathleen Fraser and Svetlana Kiritchenko. 2024 · 2024
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