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Considerable efforts to measure and mitigate gender bias in recent years have led to the introduction of an abundance of tasks, datasets, and metrics used in this vein.
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
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Crows-pairs: A challenge dataset for measuring social biases in masked language models
Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel Bowman. 2020a · 1967
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CrowS-pairs: A challenge dataset for measuring social biases in masked language models
Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel R. Bowman. 2020b · 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. 2021 · 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, Ed Chi, and Slav Petrov. 2020 · 2010
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The winograd schema challenge
Hector Levesque, Ernest Davis, and Leora Morgenstern. 2012 · 2012
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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 Adam T Kalai. 2016 · 2016
Earlier work this paper cites.
The problem with bias: Allocative versus representational harms in machine learning
Solon Barocas, Kate Crawford, Aaron Shapiro, and Hanna Wallach. 2017 · 2017
Earlier work this paper cites.
Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, J. Bryson, and A. Narayanan. 2017 · 2017
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The trouble with bias. keynote at neurips
Kate Crawford. 2017 · 2017
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Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2017 · 2017
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Adversarial removal of demographic attributes from text data
Yanai Elazar and Yoav Goldberg. 2018 · 2018
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Examining gender and race bias in two hundred sentiment analysis systems
Svetlana Kiritchenko and Saif Mohammad. 2018 · 2018
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Towards robust and privacy-preserving text representations
Yitong Li, Timothy Baldwin, and Trevor Cohn. 2018 · 2018
Earlier work this paper cites.
Reducing gender bias in abusive language detection
Ji Ho Park, Jamin Shin, and Pascale Fung. 2018 · 2018
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.
RtGender: A corpus for studying differential responses to gender
Rob Voigt, David Jurgens, Vinodkumar Prabhakaran, Dan Jurafsky, and Yulia Tsvetkov. 2018 · 2018
Earlier work this paper cites.
Mind the gap: A balanced corpus of gendered ambiguous pronouns
Kellie Webster, Marta Recasens, Vera Axelrod, and Jason Baldridge. 2018 · 2018
Earlier work this paper cites.
Mitigating unwanted biases with adversarial learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell. 2018 · 2018
Earlier work this paper cites.
Gender bias in coreference resolution: Evaluation and debiasing methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018a · 2018
Earlier work this paper cites.
Gender bias in coreference resolution: Evaluation and debiasing methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018b · 2018
Earlier work this paper cites.
Fairness and Machine Learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan. 2019 · 2019
Earlier work this paper cites.
Identifying and reducing gender bias in word-level language models
Shikha Bordia and Samuel R. Bowman. 2019 · 2019
Cited alongside, same era.
Bias in bios: A case study of semantic representation bias in a high-stakes setting
Maria De-Arteaga, Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, and Adam Tauman Kalai. 2019 · 2019
Cited alongside, same era.
The KnowRef coreference corpus: Removing gender and number cues for difficult pronominal anaphora resolution
Ali Emami, Paul Trichelair, Adam Trischler, Kaheer Suleman, Hannes Schulz, and Jackie Chi Kit Cheung. 2019 · 2019
Cited alongside, same era.
Understanding undesirable word embedding associations
Kawin Ethayarajh, David Duvenaud, and Graeme Hirst. 2019 · 2019
Cited alongside, same era.
Lipstick on a pig: Debiasing methods cover up systematic gender biases in word embeddings but do not remove them
Hila Gonen and Yoav Goldberg. 2019 · 2019
Cited alongside, same era.
Fine-tuning neural machine translation on gender-balanced datasets
Marta R. Costa-jussà and Adrià de Jorge. 2020 · 2020
Later among the works it cites.
Queens are powerful too: Mitigating gender bias in dialogue generation
Emily Dinan, Angela Fan, Adina Williams, Jack Urbanek, Douwe Kiela, and Jason Weston. 2020 · 2020
Later among the works it cites.
Nurse is closer to woman than surgeon? mitigating gender-biased proximities in word embeddings
Vaibhav Kumar, Tenzin Singhay Bhotia, Vaibhav Kumar, and Tanmoy Chakraborty. 2020 · 2020
Later among the works it cites.
Monolingual and multilingual reduction of gender bias in contextualized representations
Sheng Liang, Philipp Dufter, and Hinrich Schütze. 2020 · 2020
Later among the works it cites.
Reducing gender bias in neural machine translation as a domain adaptation problem
Danielle Saunders and Bill Byrne. 2020 · 2020
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Automatic gender identification and reinflection in Arabic
Nizar Habash, Houda Bouamor, and Christine Chung. 2019 · 2019
Cited alongside, same era.
It’s all in the name: Mitigating gender bias with name-based counterfactual data substitution
Rowan Hall Maudslay, Hila Gonen, Ryan Cotterell, and Simone Teufel. 2019 · 2019
Cited alongside, same era.
Designing and interpreting probes with control tasks
John Hewitt and Percy Liang. 2019 · 2019
Cited alongside, same era.
Gender-preserving debiasing for pre-trained word embeddings
Masahiro Kaneko and Danushka Bollegala. 2019 · 2019
Cited alongside, same era.
Conceptor debiasing of word representations evaluated on WEAT
Saket Karve, Lyle Ungar, and João Sedoc. 2019 · 2019
Cited alongside, same era.
Measuring bias in contextualized word representations
Keita Kurita, Nidhi Vyas, Ayush Pareek, Alan W Black, and Yulia Tsvetkov. 2019 · 2019
Cited alongside, same era.
On measuring social biases in sentence encoders
Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, and Rachel Rudinger. 2019a · 2019
Cited alongside, same era.
Neural machine translation doesn’t translate gender coreference right unless you make it
Danielle Saunders, Rosie Sallis, and Bill Byrne. 2020 · 2020
Later among the works it cites.
Neutralizing gender bias in word embeddings with latent disentanglement and counterfactual generation
Seungjae Shin, Kyungwoo Song, JoonHo Jang, Hyemi Kim, Weonyoung Joo, and Il-Chul Moon. 2020 · 2020
Later among the works it cites.
Mitigating gender bias in machine translation with target gender annotations
Artūrs Stafanovičs, Toms Bergmanis, and Mārcis Pinnis. 2020 · 2020
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Information-theoretic probing with minimum description length
Elena Voita and Ivan Titov. 2020 · 2020
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Extensive study on the underlying gender bias in contextualized word embeddings
Christine Basta, Marta R Costa-jussà, and Noe Casas. 2021 · 2021
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Probing classifiers: Promises, shortcomings, and alternatives
Yonatan Belinkov. 2021 · 2021
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. 2021 · 2021
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Toward gender-inclusive coreference resolution: An analysis of gender and bias throughout the machine learning lifecycle*
Yang Trista Cao and Hal Daumé III. 2021 · 2021
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Stereotype and skew: Quantifying gender bias in pre-trained and fine-tuned language models
Daniel de Vassimon Manela, David Errington, Thomas Fisher, Boris van Breugel, and Pasquale Minervini. 2021 · 2021
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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
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Bold: Dataset and metrics for measuring biases in open-ended language generation
Jwala Dhamala, Tony Sun, Varun Kumar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei Chang, and Rahul Gupta. 2021 · 2021
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A survey on bias in deep nlp
Ismael Garrido-Muñoz , Arturo Montejo-Ráez , Fernando Martínez-Santiago , and L. Alfonso Ureña-López . 2021 · 2021
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Detecting emergent intersectional biases: Contextualized word embeddings contain a distribution of human-like biases
Wei Guo and Aylin Caliskan. 2021 · 2021
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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
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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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How does counterfactually augmented data impact models for social computing constructs?
Indira Sen, Mattia Samory, Fabian Flöck, Claudia Wagner, and Isabelle Augenstein. 2021 · 2021
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A survey on gender bias in natural language processing
Karolina Stanczak and Isabelle Augenstein. 2021 · 2021
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On the intrinsic and extrinsic fairness evaluation metrics for contextualized language representations
Yang Trista Cao, Yada Pruksachatkun, Kai-Wei Chang, Rahul Gupta, Varun Kumar, Jwala Dhamala, and Aram Galstyan. 2022 · 2022
Closest in time.
How gender debiasing affects internal model representations, and why it matters
Hadas Orgad, Seraphina Goldfarb-Tarrant, and Yonatan Belinkov. 2022 · 2022
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