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Gender and race inferred from an individual's name are a notable source of stereotypes and biases that subtly influence social interactions.
Harvesting implicit group attitudes and beliefs from a demonstration web site
Brian A. Nosek, Mahzarin R. Banaji, and Anthony G. Greenwald · 1930
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Measuring individual differences in implicit cognition: The implicit association test
Anthony G. Greenwald, Debbie E. McGhee, and Jordan L. K. Schwartz · 1939
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Biases in Large Language Models: Origins, Inventory, and Discussion
Roberto Navigli, Simone Conia, and Björn Ross · 1955
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Common first names as cues for inferences about personality
Von O. Leirer, David L. Hamilton, and Sandra Carpenter · 1982
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Trust, reciprocity, and social history
Joyce Berg, John Dickhaut, and Kevin McCabe · 1995
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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 · 2003
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Are emily and greg more employable than lakisha and jamal? a field experiment on labor market discrimination
Marianne Bertrand and Sendhil Mullainathan · 2004
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Frequently Occurring Surnames from the 2010 Census, 2010
US Census Bureau · 2010
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Do politicians racially discriminate against constituents? a field experiment on state legislators
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Thinking, fast and slow
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Working twice as hard to get half as far: Race, work ethic, and america’s deserving poor
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Efficient estimation of word representations in vector space, 2013
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Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan · 2017
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Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Models
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Using large language models to simulate multiple humans and replicate human subject studies
Gati Aher, Rosa I. Arriaga, and Adam Tauman Kalai · 2023
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Textbooks Are All You Need, October 2023
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Phi-2: The surprising power of small language models, December 2023
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Measuring Bias in Contextualized Word Representations
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Language Models are Unsupervised Multitask Learners
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Debiasing Word Embeddings from Sentiment Associations in Names
Christoph Hube, Maximilian Idahl, and Besnik Fetahu · 2020
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Hannah Kirk, Yennie Jun, Haider Iqbal, Elias Benussi, Filippo Volpin, Frederic A. Dreyer, Aleksandar Shtedritski, and Yuki M. Asano · 2021
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Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed · 2023
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Gender bias and stereotypes in Large Language Models
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Textbooks Are All You Need II: phi-1.5 technical report, September 2023
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Llama 2: Open Foundation and Fine-Tuned Chat Models, July 2023
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Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, January 2023
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