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A large body of research has found substantial gender bias in NLP systems.
Harms of gender exclusivity and challenges in non-binary representation in language technologies
Sunipa Dev, Masoud Monajatipoor, Anaelia Ovalle, Arjun Subramonian, Jeff Phillips, and Kai-Wei Chang. 2021 · 1994
Earlier work this paper cites.
A model of (often mixed) stereotype content: Competence and warmth respectively follow from perceived status and competition
Susan T. Fiske, Amy J. C. Cuddy, Peter Glick, and Jun Xu. 2002 · 2002
Earlier work this paper cites.
The BIAS map: Behaviors from intergroup affect and stereotypes
Amy J. C. Cuddy, Susan T. Fiske, and Peter Glick. 2007 · 2007
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Warmth and Competence as Universal Dimensions of Social Perception: The Stereotype Content Model and the BIAS Map
Amy J. C. Cuddy, Susan T. Fiske, and Peter Glick. 2008 · 2008
Earlier work this paper cites.
Stereotype content model across cultures: Towards universal similarities and some differences
Amy J. C. Cuddy, Susan T. Fiske, Virginia S. Y. Kwan, Peter Glick, Stéphanie Demoulin, Jacques-Philippe Leyens, Michael Harris Bond, Jean-Claude Croizet, Naomi Ellemers, Ed Sleebos, Tin Tin Htun, Hyun-Jeong Kim, Greg Maio, Judi Perry, Kristina Petkova, Valery Todorov, Rosa Rodríguez-Bailón, Elena Morales, Miguel Moya, Marisol Palacios, Vanessa Smith, Rolando Perez, Jorge Vala, and Rene Ziegler. 2009 · 2009
Earlier work this paper cites.
The Trouble with Bias - NIPS 2017 Keynote - Kate Crawford #NIPS2017
Kate Crawford. 2017 · 2017
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spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing
Matthew Honnibal and Ines Montani. 2017 · 2017
Earlier work this paper cites.
Stereotype Content: Warmth and Competence Endure
Susan T. Fiske. 2018 · 2018
Earlier work this paper cites.
Word embeddings quantify 100 years of gender and ethnic stereotypes
Nikhil Garg, Londa Schiebinger, Dan Jurafsky, and James Zou. 2018 · 2018
Earlier work this paper cites.
Language (technology) is power: A critical survey of “bias” in NLP
Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach. 2020 · 2020
Earlier work this paper cites.
Universal dimensions of individuals’ perception: Revisiting the operationalization of warmth and competence with a mixed-method approach
Georgios Halkias and Adamantios Diamantopoulos. 2020 · 2020
Earlier work this paper cites.
Gender and Representation Bias in GPT-3 Generated Stories
Li Lucy and David Bamman. 2021 · 2021
Earlier work this paper cites.
StereoSet: Measuring stereotypical bias in pretrained language models
Moin Nadeem, Anna Bethke, and Siva Reddy. 2021 · 2021
Cited alongside, same era.
Comprehensive stereotype content dictionaries using a semi-automated method
Gandalf Nicolas, Xuechunzi Bai, and Susan T. Fiske. 2021 · 2021
Cited alongside, same era.
A survey on gender bias in natural language processing
Karolina Stanczak and Isabelle Augenstein. 2021 · 2021
Cited alongside, same era.
Theory-Grounded Measurement of U.S. Social Stereotypes in English Language Models
Yang Trista Cao, Anna Sotnikova, Hal Daumé III, Rachel Rudinger, and Linda Zou. 2022 · 2022
Cited alongside, same era.
Examining the structural validity of stereotype content scales–a preregistered re-analysis of published data and discussion of possible future directions
M-T Friehs, Patrick F Kotzur, Johanna Böttcher, A-KC Zöller, Tabea Lüttmer, Ulrich Wagner, Frank Asbrock, and Maarten HW Van Zalk. 2022 · 2022
StereoMap: Quantifying the awareness of human-like stereotypes in large language models
Sullam Jeoung, Yubin Ge, and Jana Diesner. 2023 · 2023
Later among the works it cites.
Nationality Bias in Text Generation
Pranav Narayanan Venkit, Sanjana Gautam, Ruchi Panchanadikar, Ting-Hao Huang, and Shomir Wilson. 2023 · 2023
Later among the works it cites.
Biases in Large Language Models: Origins, Inventory, and Discussion
Roberto Navigli, Simone Conia, and Björn Ross. 2023 · 2023
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"Im not Racist but…": Discovering Bias in the Internal Knowledge of Large Language Models
Abel Salinas, Louis Penafiel, Robert McCormack, and Fred Morstatter. 2023 · 2023
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“Kelly is a warm person, Joseph is a role model”: Gender biases in LLM-generated reference letters
Yixin Wan, George Pu, Jiao Sun, Aparna Garimella, Kai-Wei Chang, and Nanyun Peng. 2023 · 2023
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Cited alongside, same era.
Applying the Stereotype Content Model to assess disability bias in popular pre-trained NLP models underlying AI-based assistive technologies
Brienna Herold, James Waller, and Raja Kushalnagar. 2022 · 2022
Cited alongside, same era.
A Robust Bias Mitigation Procedure Based on the Stereotype Content Model
Eddie Ungless, Amy Rafferty, Hrichika Nag, and Björn Ross. 2022 · 2022
Cited alongside, same era.
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc V. Le, and Denny Zhou. 2022 · 2022
Cited alongside, same era.
Queer in ai: A case study in community-led participatory ai
Organizers Of Queer in AI, Anaelia Ovalle, Arjun Subramonian, Ashwin Singh, Claas Voelcker, Danica J Sutherland, Davide Locatelli, Eva Breznik, Filip Klubicka, Hang Yuan, et al. 2023 · 2023
Cited alongside, same era.
Marked personas: Using natural language prompts to measure stereotypes in language models
Myra Cheng, Esin Durmus, and Dan Jurafsky. 2023 · 2023
Cited alongside, same era.
Queer People are People First: Deconstructing Sexual Identity Stereotypes in Large Language Models
Harnoor Dhingra, Preetiha Jayashanker, Sayali Moghe, and Emma Strubell. 2023 · 2023
Cited alongside, same era.
This prompt is measuring <mask>: evaluating bias evaluation in language models
Seraphina Goldfarb-Tarrant, Eddie Ungless, Esma Balkir, and Su Lin Blodgett. 2023 · 2023
Cited alongside, same era.
Yanhong Bai, Jiabao Zhao, Jinxin Shi, Zhentao Xie, Xingjiao Wu, and Liang He. 2024 · 2024
Later among the works it cites.
Subtle biases need subtler measures: Dual metrics for evaluating representative and affinity bias in large language models
Abhishek Kumar, Sarfaroz Yunusov, and Ali Emami. 2024 · 2024
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Exploring the Relationship Between Intrinsic Stigma in Masked Language Models and Training Data Using the Stereotype Content Model
Mario Mina, Júlia Falcão, and Aitor Gonzalez-Agirre. 2024 · 2024
Later among the works it cites.
Uncovering Stereotypes in Large Language Models: A Task Complexity-based Approach
Hari Shrawgi, Prasanjit Rath, Tushar Singhal, and Sandipan Dandapat. 2024 · 2024
Later among the works it cites.
Amplifying trans and nonbinary voices: A community-centred harm taxonomy for LLMs
Eddie L. Ungless, Sunipa Dev, Cynthia L. Bennett, Rebecca Gulotta, Jasmijn Bastings, and Remi Denton. 2025 · 2025
Closest in time.
The colorful future of LLMs: Evaluating and improving LLMs as emotional supporters for queer youth
Shir Lissak, Nitay Calderon, Geva Shenkman, Yaakov Ophir, Eyal Fruchter, Anat Brunstein Klomek, and Roi Reichart. 2024 · 2079
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Theories of “gender” in NLP bias research
Hannah Devinney, Jenny Björklund, and Henrik Björklund. 2022 · 2083
Closest in time.