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The Stereotype Content model (SCM) states that we tend to perceive minority groups as cold, incompetent or both.
Intrinsic bias metrics do not correlate with application bias
Seraphina Goldfarb-Tarrant, Rebecca Marchant, Ricardo Muñoz Sánchez, Mugdha Pandya, and Adam Lopez. 2021 · 1940
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The media and the black response
Lewis Diuguid and Adrienne Rivers. 2000 · 2000
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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
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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
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Beastly: What makes animal metaphors offensive?
Nick Haslam, Steve Loughnan, and Pamela Sun. 2011 · 2011
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An intersectional analysis of gender and ethnic stereotypes: Testing three hypotheses
Negin Ghavami and Letitia Anne Peplau. 2013 · 2013
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The emotional cost of humanity: Anticipated exhaustion motivates dehumanization of stigmatized targets
C. Daryl Cameron, Lasana T. Harris, and B. Keith Payne. 2016 · 2016
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Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Demographic aspects of first names
Konstantinos Tzioumis. 2018 · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018 · 2018
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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
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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The woman worked as a babysitter: On biases in language generation
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng. 2019 · 2019
Cited alongside, same era.
Towards debiasing NLU models from unknown biases
Prasetya Ajie Utama, Nafise Sadat Moosavi, and Iryna Gurevych. 2020 · 2020
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Debiasing pre-trained contextualised embeddings
Masahiro Kaneko and Danushka Bollegala. 2021 · 2021
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StereoSet: Measuring stereotypical bias in pretrained language models
Moin Nadeem, Anna Bethke, and Siva Reddy. 2021 · 2021
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Comprehensive stereotype content dictionaries using a semi-automated method
Gandalf Nicolas, Xuechunzi Bai, and Susan T. Fiske. 2021 · 2021
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All immigrants are not alike: Intersectionality matters in views of immigrant groups
Özge Savaş, Ronni M. Greenwood, Benjamin T. Blankenship, Abigail J. Stewart, and Kay Deaux. 2021 · 2021
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Generating datasets with pretrained language models
Timo Schick and Hinrich Schütze. 2021 · 2021
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Self-diagnosis and self-debiasing: A proposal for reducing corpus-based bias in NLP
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Kellie Webster, Xuezhi Wang, Ian Tenney, Alex Beutel, Emily Pitler, Ellie Pavlick, Jilin Chen, Ed H. Chi, and Slav Petrov. 2020 · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Understanding and countering stereotypes: A computational approach to the stereotype content model
Kathleen C. Fraser, Isar Nejadgholi, and Svetlana Kiritchenko. 2021 · 2021
Cited alongside, same era.
Detecting emergent intersectional biases: Contextualized word embeddings contain a distribution of human-like biases
Wei Guo and Aylin Caliskan. 2021 · 2021
Cited alongside, same era.
On transferability of bias mitigation effects in language model fine-tuning
Xisen Jin, Francesco Barbieri, Brendan Kennedy, Aida Mostafazadeh Davani, Leonardo Neves, and Xiang Ren. 2021 · 2021
Cited alongside, same era.
On the intrinsic and extrinsic fairness evaluation metrics for contextualized language representations
Yang Cao, Yada Pruksachatkun, Kai-Wei Chang, Rahul Gupta, Varun Kumar, Jwala Dhamala, and Aram Galstyan. 2022a
Cited in the paper.
Timo Schick, Sahana Udupa, and Hinrich Schütze. 2021 · 2021
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Theory-grounded measurement of U.S. social stereotypes in English language models
Yang Cao, Anna Sotnikova, Hal Daumé III, Rachel Rudinger, and Linda Zou. 2022b · 2022
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An empirical survey of the effectiveness of debiasing techniques for pre-trained language models
Nicholas Meade, Elinor Poole-Dayan, and Siva Reddy. 2022 · 2022
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Upstream mitigation is not all you need: Testing the bias transfer hypothesis in pre-trained language models
Ryan Steed, Swetasudha Panda, Ari Kobren, and Michael Wick. 2022 · 2022
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