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Many measures of societal bias in language models have been proposed in recent years.
Bad seeds: Evaluating lexical methods for bias measurement
Maria Antoniak and David Mimno. 2021 · 1904
Earlier work this paper cites.
Roberta: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
Intrinsic bias metrics do not correlate with application bias
Seraphina Goldfarb-Tarrant, Rebecca Marchant, Ricardo Muñoz Sánchez, Mugdha Pandya, and Adam Lopez. 2021 · 1940
Earlier work this paper cites.
CrowS-pairs: A challenge dataset for measuring social biases in masked language models
Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel R. Bowman. 2020 · 1967
Earlier work this paper cites.
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. 2021a · 1994
Earlier work this paper cites.
Measuring and reducing gendered correlations in pre-trained models
Kellie Webster, Xuezhi Wang, Ian Tenney, Alex Beutel, Emily Pitler, Ellie Pavlick, Jilin Chen, and Slav Petrov. 2020 · 2010
Earlier work this paper cites.
Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan. 2017 · 2017
Earlier work this paper cites.
Gender bias in coreference resolution
Rachel Rudinger, Jason Naradowsky, Brian Leonard, and Benjamin Van Durme. 2018 · 2018
Earlier work this paper cites.
Bias in bios: A case study of semantic representation bias in a high-stakes setting
Maria De-Arteaga, Alexey Romanov, Hanna M. Wallach, Jennifer T. Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Cem Geyik, Krishnaram Kenthapadi, and Adam Tauman Kalai. 2019 · 2019
Earlier work this paper cites.
Measuring bias in contextualized word representations
Keita Kurita, Nidhi Vyas, Ayush Pareek, Alan W Black, and Yulia Tsvetkov. 2019 · 2019
Earlier work this paper cites.
On measuring social biases in sentence encoders
Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, and Rachel Rudinger. 2019 · 2019
Cited alongside, same era.
Language (technology) is power: A critical survey of “bias” in NLP
Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach. 2020 · 2020
Cited alongside, same era.
Negated and misprimed probes for pretrained language models: Birds can talk, but cannot fly
Nora Kassner and Hinrich Schütze. 2020 · 2020
Cited alongside, same era.
Predictive biases in natural language processing models: A conceptual framework and overview
Deven Santosh Shah, H. Andrew Schwartz, and Dirk Hovy. 2020 · 2020
Cited alongside, same era.
Mitigating language-dependent ethnic bias in BERT
Jaimeen Ahn and Alice Oh. 2021 · 2021
Cited alongside, same era.
Stereotyping Norwegian salmon: An inventory of pitfalls in fairness benchmark datasets
Challenges in measuring bias via open-ended language generation
Afra Feyza Akyürek, Muhammed Yusuf Kocyigit, Sejin Paik, and Derry Wijaya. 2022 · 2022
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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. 2022 · 2022
Later among the works it cites.
Gender bias in masked language models for multiple languages
Masahiro Kaneko, Aizhan Imankulova, Danushka Bollegala, and Naoaki Okazaki. 2022 · 2022
Later among the works it cites.
An empirical survey of the effectiveness of debiasing techniques for pre-trained language models
Nicholas Meade, Elinor Poole-Dayan, and Siva Reddy. 2022 · 2022
Later among the works it cites.
French CrowS-pairs: Extending a challenge dataset for measuring social bias in masked language models to a language other than English
Aurélie Névéol, Yoann Dupont, Julien Bezançon, and Karën Fort. 2022 · 2022
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Su Lin Blodgett, Gilsinia Lopez, Alexandra Olteanu, Robert Sim, and Hanna Wallach. 2021 · 2021
Cited alongside, same era.
Measuring fairness with biased rulers: A survey on quantifying biases in pretrained language models
Pieter Delobelle, Ewoenam Kwaku Tokpo, Toon Calders, and Bettina Berendt. 2021 · 2021
Cited alongside, same era.
StereoSet: Measuring stereotypical bias in pretrained language models
Moin Nadeem, Anna Bethke, and Siva Reddy. 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.
Frequency effects on syntactic rule learning in transformers
Jason Wei, Dan Garrette, Tal Linzen, and Ellie Pavlick. 2021 · 2021
Cited alongside, same era.
What do bias measures measure?
Sunipa Dev, Emily Sheng, Jieyu Zhao, Jiao Sun, Yu Hou, Mattie Sanseverino, Jiin Kim, Nanyun Peng, and Kai-Wei Chang. 2021b
Cited in the paper.
Choose your lenses: Flaws in gender bias evaluation
Hadas Orgad and Yonatan Belinkov. 2022 · 2022
Later among the works it cites.
SlovakBERT: Slovak masked language model
Matúš Pikuliak, Štefan Grivalský, Martin Konôpka, Miroslav Blšták, Martin Tamajka, Viktor Bachratý, Marian Simko, Pavol Balážik, Michal Trnka, and Filip Uhlárik. 2022 · 2022
Later among the works it cites.
You reap what you sow: On the challenges of bias evaluation under multilingual settings
Zeerak Talat, Aurélie Névéol, Stella Biderman, Miruna Clinciu, Manan Dey, Shayne Longpre, Sasha Luccioni, Maraim Masoud, Margaret Mitchell, Dragomir Radev, Shanya Sharma, Arjun Subramonian, Jaesung Tae, Samson Tan, Deepak Tunuguntla, and Oskar Van Der Wal. 2022 · 2022
Later among the works it cites.
The AI index 2022 annual report
Daniel Zhang, Nestor Maslej, Erik Brynjolfsson, John Etchemendy, Terah Lyons, James Manyika, Helen Ngo, Juan Carlos Niebles, Michael Sellitto, Ellie Sakhaee, Yoav Shoham, Jack Clark, and C. Raymond Perrault. 2022 · 2022
Later among the works it cites.
Theories of "gender" in NLP bias research
Hannah Devinney, Jenny Björklund, and Henrik Björklund. 2022 · 2083
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