On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?🦜. FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency March 2021 Pages 610–623
Bias in Contextual Embeddings
Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, Rachel Rudinger.
On Measuring Social Biases in Sentence Encoders. NAACL 2019.
Keita Kurita, Nidhi Vyas, Ayush Pareek, Alan W Black, and Yulia Tsvetkov. 2019. Measuring bias in contextualized word representations. In Proceedings of the First Workshop on Gender Bias in Natu- ral Language Processing, pages 166–172, Florence, Italy. Association for Computational Linguistics.
Keita Kurita, Nidhi Vyas, Ayush Pareek, Alan W Black and Yulia Tsvetkov. 2019. Quantifying Social Biases in Contextual Word Representations. Proc. of Workshop on Gender Bias for NLP
Bias in different NLP tasks
Danielle Saunders and Bill Byrne. 2020.
Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem. ACL 2020
Zhao, Jieyu, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. Gender bias in coreference resolution: Evaluation and debiasing methods." NAACL 2018
Rachel Rudinger, Jason Naradowsky, Brian Leonard, and Benjamin Van Durme. 2018. Gender bias in coreference resolution. In NAACL.
Svetlana Kiritchenko, Saif M. Mohammad. 2018. Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems. Workshop on Ethics in NLP 2018.
Rachel Rudinger, Chandler May, and Benjamin Van Durme. 2017. Social bias in elicited natural language inferences. In ACL Workshop on Ethics in NLP, pages 74–79.
Andrew Gaut, Tony Sun, Shirlyn Tang, Yuxin Huang, Jing Qian, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang. 2019. Towards Understanding Gender Bias in Relation Extraction.
Garimella, Aparna, Carmen Banea, Dirk Hovy, and Rada Mihalcea. Women’s Syntactic Resilience and Men’s Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing. ACL 2019.
Hila Gonen and Kellie Webster. 2020. Automatically Identifying Gender Issues in Machine Translation using Perturbations. Arxiv
Yang Trista Cao, Hal Daumé III. 2019. Toward Gender-Inclusive Coreference Resolution
Ninareh Mehrabi, Thamme Gowda, Fred Morstatter, Nanyun Peng, Aram Galstyan. 2019. Man is to Person as Woman is to Location: Measuring Gender Bias in Named Entity Recognition.
Marcelo Prates, Pedro Avelar, and Luis C. Lamb. 2019. Assessing Gender Bias in Machine Translation – A Case Study with Google Translate
Kellie Webster, Marta Recasens, Vera Axelrod, and Jason Baldridge. 2018. Mind the GAP: A balanced corpus of gendered ambiguous pronouns. TACL.
Bias amplification
Zhao, J., Wang, T., Yatskar, M., Ordonez, V and Chang, M.-W. (2017) Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraint. EMNLP
Shengyu Jia, Tao Meng, Jieyu Zhao and Kai-Wei Chang. 2020.
Mitigating Gender Bias Amplification in Distribution by Posterior Regularization.
ACL 2020
Race
R. Tatman, C. Kasten, “Effects of talker dialect, gender and race on accuracy of Bing speech and YouTube automatic captions” in INTERSPEECH (2017), pp. 934–938.
Sen and Wasow (2016) Race as a Bundle of Sticks: Designs that Estimate Effects of Seemingly Immutable Characteristics, Annual Review of Political Science
S. L. Blodgett, B. O’Connor. 2017. Racial disparity in natural language processing: A case study of social media African-American English. in Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) Workshop (KDD, 2017).
Yi Chern Tan and L Elisa Celis. 2019.
Assessing social and intersectional biases in contextualized word representations.
In Advances in Neural Information Processing Systems, pages 13209–13220, 2019.
Bowker, Geoffrey C., and Susan Leigh Star. 1999. Introduction and Chapter 1 in Sorting Things Out: Classification and Its Consequences. Cambridge: MIT Press.
Safiya Noble. Algorithms of Oppression.
Lots more topics
Ferrer, Xavier, van Nuenen, Tom, Such, Jose M., and Criado, Natalia, 2021.
Discovering and categorising language biases in Reddit. Proceedings of ICWSM.
Vinodkumar Prabhakaran, Ben Hutchinson, Margaret Mitchell. 2019. Perturbation Sensitivity Analysis to Detect Unintended Model Biases. EMNLP 2019.
R Zmigrod, SJ Mielke, H Wallach, R Cotterell. 2019. Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology arXiv:1906.04571, 2019.
Sun, Tony, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, and William Yang Wang. 2019. Mitigating gender bias in natural language processing: Literature review. ACL 2019
Hila Gonen, Yova Kementchedjhieva, Yoav Goldberg. 2019. How does Grammatical Gender Affect Noun Representations in Gender-Marking Languages? CoNLL 2019. https://arxiv.org/abs/1910.14161
Vaibhav Kumar, Tenzin Singhay Bhotia, Vaibhav Kumar, Tanmoy Chakraborty. 2020.
Nurse is Closer to Woman than Surgeon? Mitigating Gender-Biased Proximities in Word Embeddings. TACL.
Mattia Samory, Indira Sen, Julian Kohne, Fabian Floeck, Claudia Wagner. 2020.
"Unsex me here": Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples. Arxiv
Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, Stuart Shieber. 2020. Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias
Candace Ross, Boris Katz, Andrei Barbu. 2020. Measuring Social Biases in Grounded Vision and Language Embeddings. https://arxiv.org/abs/2002.08911
Deven Shah, H. Andrew Schwartz, Dirk Hovy. 2020. Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview.
Thomas Manzini, Yao Chong, Yulia Tsvetkov and Alan W Black. 2019.
Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings.
NAACL 2019.
Zhong, Ruiqi, Yanda Chen, Desmond Patton, Charlotte Selous, and Kathy McKeown. "Detecting and Reducing Bias in a High Stakes Domain." arXiv preprint arXiv:1908.11474(2019).
Pei Zhou, Weijia Shi, Jieyu Zhao, Kuan-Hao Huang, Muhao Chen, Ryan Cotterell and Kai-Wei Chang. 2019. Examining Gender Bias in Languages with Grammatical Gender. EMNLP-IJCNLP 2019.
Shauli Ravfogel, Yanai Elazar, Hila Gonen, Michael Twiton, Yoav
Goldberg. 2020.
Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection. ACL 2020
Moin Nadeem, Anna Bethke, and Siva Reddy. 2020.
StereoSet: Measuring stereotypical bias in pretrained language models. Arxiv
Rob Voigt, David Jurgens, Vinodkumar Prabhakaran, Dan Jurafsky, and Yulia Tsvetkov. 2018. RtGender: A Corpus of Responses to Gender for Studying Gender Bias. LREC 2018
Kawin Ethayarajh, David Duvenaud, and Graeme Hirst. 2019.
Understanding undesirable word embedding associations.
ACL 2019.
Oshin Agarwal, Funda Durupınar, Norman I. Badler, and Ani Nenkova. 2019.
Word embeddings (also) encode human personality stereotypes.
In Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
Kawin Ethayarajh. 2020.
Is Your Classifier Actually Biased? Measuring Fairness under Uncertainty with Bernstein Bounds. ACL 2020
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, Nanyun Peng. 2020.
Towards Controllable Biases in Language Generation. Arxiv
Prasetya Ajie Utama, Nafise Sadat Moosavi, Iryna Gurevych. 2020.
Mind the Trade-off: Debiasing NLU Models without Degrading the
In-distribution Performance.
ACL 2020
Vaibhav Kumar, Tenzin Singhay Bhotia, Vaibhav Kumar, Tanmoy Chakraborty. 2020
Nurse is Closer to Woman than Surgeon? Mitigating Gender-Biased Proximities in Word Embeddings. TACL.
Papakyriakopoulos, Orestis, Simon Hegelich, Juan Carlos Medina Serrano, and Fabienne Marco. "Bias in word embeddings." In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 446-457. 2020.