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Recent large-scale T2I models like DALLE-3 have made progress in reducing gender stereotypes when generating single-person images.
Fairface: Face attribute dataset for balanced race, gender, and age
Kimmo Kärkkäinen and Jungseock Joo. 2019 · 1908
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Women and power in organizations
Heli K. Lahtinen and Fiona M. Wilson. 1994 · 1994
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‘men managing leadership? men and women of the corporation revisited’
David Collinson and Jeff Hearn. 1996 · 1996
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Gender, Power and Organisation , pages 214–234
Susan Halford and Pauline Leonard. 2001 · 2001
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Amazon mechanical turk: A research tool for organizations and information systems scholars
Kevin Crowston. 2012 · 2012
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Unequal representation and gender stereotypes in image search results for occupations
Matthew Kay, Cynthia Matuszek, and Sean A. Munson. 2015 · 2015
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Gender bias in coreference resolution: Evaluation and debiasing methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018 · 2018
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Female librarians and male computer programmers? gender bias in occupational images on digital media platforms
Vivek K. Singh, Mary Chayko, Raj Inamdar, and Diana Floegel. 2019 · 2019
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021 · 2021
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2021 · 2021
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How well can text-to-image generative models understand ethical natural language interventions?
Hritik Bansal, Da Yin, Masoud Monajatipoor, and Kai-Wei Chang. 2022 · 2022
Cited alongside, same era.
Karlo-v1.0.alpha on coyo-100m and cc15m
Donghoon Lee, Jisu Choi Jiseob Kim, Jongmin Kim, Woonhyuk Baek Minwoo Byeon, and Saehoon Kim. 2022 · 2022
Cited alongside, same era.
Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. 2022 · 2022
Cited alongside, same era.
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
Mini-dalle3: Interactive text to image by prompting large language models
Zeqiang Lai, Xizhou Zhu, Jifeng Dai, Yu Qiao, and Wenhai Wang. 2023 · 2023
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Social biases through the text-to-image generation lens
Ranjita Naik and Besmira Nushi. 2023 · 2023
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Dall·e 3 system card
OpenAI. 2023 · 2023
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Editing implicit assumptions in text-to-image diffusion models
Hadas Orgad, Bahjat Kawar, and Yonatan Belinkov. 2023 · 2023
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The bias amplification paradox in text-to-image generation
Preethi Seshadri, Sameer Singh, and Yanai Elazar. 2023 · 2023
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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.
A friendly face: Do text-to-image systems rely on stereotypes when the input is under-specified?
Kathleen C. Fraser, Isar Nejadgholi, and Svetlana Kiritchenko. 2023 · 2023
Cited alongside, same era.
Fair diffusion: Instructing text-to-image generation models on fairness
Felix Friedrich, Manuel Brack, Lukas Struppek, Dominik Hintersdorf, Patrick Schramowski, Sasha Luccioni, and Kristian Kersting. 2023 · 2023
Cited alongside, same era.
T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation
Kaiyi Huang, Kaiyue Sun, Enze Xie, Zhenguo Li, and Xihui Liu. 2023 · 2023
Cited alongside, same era.
T2IAT: Measuring valence and stereotypical biases in text-to-image generation
Jialu Wang, Xinyue Liu, Zonglin Di, Yang Liu, and Xin Wang. 2023 · 2023
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Labor force statistics from the current population survey
U.S. Bureau of Labor Statistics. 2024 · 2024
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Yixin Wan and Kai-Wei Chang. 2025 · 2025
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