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The capabilities of natural language models trained on large-scale data have increased immensely over the past few years.
A New Measure of Rank Correlation
M. G. Kendall · 1938
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Where women work-an analysis by industry and occupation
Elizabeth Waldman and Beverly J McEaddy · 1974
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Occupational segregation by sex: Determinants and changes
Andrea H Beller · 1982
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Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics
K. Crenshaw · 1989
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Comparable worth: Theories and evidence
Paula England · 1992
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Educational presorting and occupational segregation
Lex Borghans and Loek Groot · 1999
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Do women choose different jobs from men? mechanisms of application segregation in the market for managerial workers
Roxana Barbulescu and Matthew Bidwell · 2013
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Explaining the Gender Wage Gap, may 2014
Sarah Jane Glynn · 2014
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The stanford corenlp natural language processing toolkit
Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Rose Finkel, Steven Bethard, and David McClosky · 2014
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Big data’s disparate impact
Solon Barocas and Andrew D. Selbst · 2016
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y. Zou, Venkatesh Saligrama, and A. Kalai · 2016
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Family-friendly policies and women’s wages–is there a trade-off? skill investments, occupational segregation and the gender pay gap in germany, sweden and the uk
Anne Grönlund and Charlotta Magnusson · 2016
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Semantics derived automatically from language corpora contain human-like biases
A. Caliskan, J. Bryson, and A. Narayanan · 2017
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Social bias in elicited natural language inferences
Rachel Rudinger, Chandler May, and Benjamin Van Durme · 2017
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Addressing age-related bias in sentiment analysis
Mark Diaz, I. Johnson, Amanda Lazar, A. Piper, and Darren Gergle · 2018
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Examining gender and race bias in two hundred sentiment analysis systems
Svetlana Kiritchenko and Saif M. Mohammad · 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 R. Bowman · 2018
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Learning gender-neutral word embeddings
Jieyu Zhao, Yichao Zhou, Z. Li, W. Wang, and Kai-Wei Chang · 2018
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H. Gonen and Y. Goldberg · 2019
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The role of gender stereotypes in hiring: a field experiment
María José González López, Clara Cortina Trilla, and Jorge Rodríguez · 2019
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Measuring bias in contextualized word representations
Keita Kurita, N. Vyas, Ayush Pareek, A. Black, and Yulia Tsvetkov · 2019
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Towards a human-like open-domain chatbot
D. Adiwardana, Minh-Thang Luong, D. So, J. Hall, Noah Fiedel, R. Thoppilan, Z. Yang, Apoorv Kulshreshtha, G. Nemade, Yifeng Lu, and Quoc V. Le · 2020
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Climbing towards nlu: On meaning, form, and understanding in the age of data
Emily M Bender and Alexander Koller · 2020
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Investigating gender bias in bert
Rishabh Bhardwaj, Navonil Majumder, and Soujanya Poria · 2020
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Language (technology) is power: A critical survey of "bias" in nlp
Su Lin Blodgett, Solon Barocas, Hal Daum’e, and H. Wallach · 2020
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An intersectional definition of fairness
J. Foulds and Shimei Pan · 2020
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Natural language processing based jaro-the interviewing chatbot
Jitendra Purohit, Aditya Bagwe, Rishabh Mehta, Ojaswini Mangaonkar, and Elizabeth George · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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The woman worked as a babysitter: On biases in language generation
Emily Sheng, Kai-Wei Chang, P. Natarajan, and Nanyun Peng · 2019
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Release strategies and the social impacts of language models
Irene Solaiman, Miles Brundage, J. Clark, Amanda Askell, Ariel Herbert-Voss, Jeff Wu, Alec Radford, and J. Wang · 2019
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Assessing social and intersectional biases in contextualized word representations
Y. Tan and L. Celis · 2019
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Employed peons by detailed occupation, sex, race, and Hispanic or Latino ethnicity, 2019
US Labor Bureau of Statistics · 2019
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Superglue: A stickier benchmark for general-purpose language understanding systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman · 2019
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Pengcheng He, Xiaodong Liu, Jianfeng Gao, and W. Chen · 2020
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US: Religious Records-Part 2, 2020
Institute for Genealogical Studies · 2020
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Stereoset: Measuring stereotypical bias in pretrained language models
Moin Nadeem, Anna Bethke, and Siva Reddy · 2020
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Religious Landscape Study, 2020
Pew Research · 2020
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Privacy Considerations in Large Language Models, 2020
Carlini, N · 2020
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Bad seeds: Evaluating lexical methods for bias measurement
Maria Antoniak and David Mimno · 2021
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On the dangers of stochastic parrots: Can language models be too big?
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell · 2021
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Stereotyping norwegian salmon: An inventory of pitfalls in fairness benchmark datasets
Su Lin Blodgett, Gilsinia Lopez, Alexandra Olteanu, Robert Sim, and Hanna M. Wallach · 2021
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List of most popular names, 2021
Wikipedia · 2021
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On the dangers of stochastic parrots: Can language models be too big?
Bender, E. M., Gebru, T., McMillan-Major, A., and Shmitchell, S · 2021
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