CoChat
Sign Up
Cited by 69Ethics and Social Impacts of AITopic ModelingHate Speech and Cyberbullying Detection

Perturbation augmentation for fairer NLP

Open access
Qian, Rebecca, Ross, Candace, Fernandes, Jude, Smith, Eric Michael, Kiela, Douwe, Williams, Adina·2022·Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
DOI 10.18653/v1/2022.emnlp-main.646
Open this paper in CoChat — free accountOpen PDF

Abstract

Unwanted and often harmful social biases are becoming ever more salient in NLP research, affecting both models and datasets.In this work, we ask whether training on demographically perturbed data leads to fairer language models.We collect a large dataset of human annotated text perturbations and train a neural perturbation model, which we show outperforms heuristic alternatives.We find that (i) language models (LMs) pre-trained on demographically perturbed corpora are typically more fair, and (ii) LMs finetuned on perturbed GLUE datasets exhibit less demographic bias on downstream tasks, and (iii) fairness improvements do not come at the expense of performance on downstream tasks.Lastly, we discuss outstanding questions about how best to evaluate the (un)fairness of large language models.We hope that this exploration of neural demographic perturbation will help drive more improvement towards fairer NLP.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.18653/v1/2022.emnlp-main.646

Cites

18 of 18 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • PANDA includes annotations for the perturbed demographic words.

    “PANDA includes annotations for the perturbed demographic words.”

    From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Qian et al. 2022

  • PANDA introduces a dataset of 98,583 text perturbations for gender, race/ethnicity, and age groups, with pairs of sentences with a social group changed but semantic meaning preserved.

    “PANDA (Qian et al. 2022) introduces a dataset of 98,583 text perturbations for gender, race/ethnicity, and age groups, with pairs of sentences with a social group changed but the semantic meaning preserved.”

    From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Qian et al. 2022

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 76

  1. Bashar Alhafni, Nizar Habash, and Houda Bouamor. 2020. Gender-aware reinflection using linguistically enhanced neural models. In Proceedings of the Sec- ond Workshop on Gender Bias in Natural Language Processing, pages 139-150, Barcelona, Spain (On- line). Association for Computational Linguistics.
  2. April H. Bailey, Adina Williams, and Andrei Cimpian. 2022. Based on billions of words on the internet, people= men. Science Advances, 8(13):eabm2463. · DOI
  3. Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth. 2017. A convex framework for fair regression. arXiv preprint arXiv:1706.02409. · arXiv
  4. Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accuracy disparities in com- mercial gender classification. In Conference on fair- ness, accountability and transparency, pages 77-91.
  5. PMLR.
  6. Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan. 2017. Semantics derived automatically from lan- guage corpora contain human-like biases. Science, 356(6334):183-186.
  7. Yang Cao, Yada Pruksachatkun, Kai-Wei Chang, Rahul Gupta, Varun Kumar, Jwala Dhamala, and Aram Gal- styan. 2022. On the intrinsic and extrinsic fairness evaluation metrics for contextualized language repre- sentations. In Proceedings of the 60th Annual Meet- ing of the Association for Computational Linguistics (Volume 2: Short Papers), pages 561-570, Dublin, Ireland. Association for Computational Linguistics.
  8. Daniel Cer, Mona Diab, Eneko Agirre, Iñigo Lopez- Gazpio, and Lucia Specia. 2017. SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 1-14, Vancouver, Canada. Association for Computational Linguistics. · DOI · Paper page
  9. Kai-Wei Chang, Vinodkumar Prabhakaran, and Vicente Ordonez. 2019. Bias and fairness in natural language processing. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Process- ing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): Tu- torial Abstracts, Hong Kong, China. Association for Computational Linguistics.
  10. Sanyuan Chen, Yutai Hou, Yiming Cui, Wanxiang Che, Ting Liu, and Xiangzhan Yu. 2020. Recall and learn: Fine-tuning deep pretrained language models with less forgetting. In Proceedings of the 2020 Confer- ence on Empirical Methods in Natural Language Pro- cessing (EMNLP), pages 7870-7881, Online. Associ- ation for Computational Linguistics. · DOI
  11. Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005. The pascal recognising textual entailment chal- lenge. In Machine Learning Challenges Workshop, pages 177-190. Springer.
  12. Pieter Delobelle, Ewoenam Kwaku Tokpo, Toon Calders, and Bettina Berendt. 2021. Measuring fair- ness with biased rulers: A survey on quantifying biases in pretrained language models. arXiv preprint arXiv:2112.07447. · arXiv
  13. Emily Dinan, Angela Fan, Adina Williams, Jack Ur- banek, Douwe Kiela, and Jason Weston. 2020a. Queens are powerful too: Mitigating gender bias in dialogue generation. In Proceedings of the 2020 Con- ference on Empirical Methods in Natural Language Processing (EMNLP), pages 8173-8188, Online. As- sociation for Computational Linguistics. · DOI
  14. Emily Dinan, Angela Fan, Ledell Wu, Jason Weston, Douwe Kiela, and Adina Williams. 2020b. Multi- dimensional gender bias classification. In Proceed- ings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 314-331, Online. Association for Computational Lin- guistics. · DOI
  15. Chris Emmery, Ákos Kádár, Grzegorz Chrupała, and Walter Daelemans. 2022. Cyberbullying classifiers are sensitive to model-agnostic perturbations. arXiv preprint arXiv:2201.06384. · arXiv
  16. Lisa Fan, Marshall White, Eva Sharma, Ruisi Su, Pra- fulla Kumar Choubey, Ruihong Huang, and Lu Wang. 2019. In plain sight: Media bias through the lens of factual reporting. In Proceedings of the 2019 Confer- ence on Empirical Methods in Natural Language Pro- cessing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6342-6348, Hong Kong, China. Association for Computational Linguistics. · DOI
  17. Anjalie Field, Su Lin Blodgett, Zeerak Waseem, and Yulia Tsvetkov. 2021. A survey of race, racism, and anti-racism in NLP. In Proceedings of the 59th An- nual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer- ence on Natural Language Processing (Volume 1: Long Papers), pages 1905-1925, Online. Association for Computational Linguistics. · DOI
  18. Adam D Galinsky, Kurt Hugenberg, Carla Groom, and Galen V Bodenhausen. 2003. The reappropriation of stigmatizing labels: Implications for social iden- tity. In Identity issues in groups. Emerald Group Publishing Limited. · DOI
  19. Mor Geva, Ankit Gupta, and Jonathan Berant. 2020. Injecting numerical reasoning skills into language models. In Proceedings of the 58th Annual Meet- ing of the Association for Computational Linguistics, pages 946-958, Online. Association for Computa- tional Linguistics. · DOI
  20. Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, and Bill Dolan. 2007. The third PASCAL recognizing textual entailment challenge. In Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, pages 1-9, Prague. Association for Computational Linguistics.
  21. Seraphina Goldfarb-Tarrant, Rebecca Marchant, Ri- cardo Muñoz Sánchez, Mugdha Pandya, and Adam Lopez. 2021. Intrinsic bias metrics do not correlate with application bias. In Proceedings of the 59th An- nual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer- ence on Natural Language Processing (Volume 1: Long Papers), pages 1926-1940, Online. Association for Computational Linguistics. · DOI · Paper page
  22. Ian J Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio. 2013. An em- pirical investigation of catastrophic forgetting in gradient-based neural networks. arXiv preprint arXiv:1312.6211. · arXiv
  23. Nizar Habash, Houda Bouamor, and Christine Chung. 2019. Automatic gender identification and reinflec- tion in Arabic. In Proceedings of the First Workshop on Gender Bias in Natural Language Processing, pages 155-165, Florence, Italy. Association for Com- putational Linguistics. · DOI
  24. R Bar Haim, Ido Dagan, Bill Dolan, Lisa Ferro, Danilo Giampiccolo, Bernardo Magnini, and Idan Szpektor. 2006. The second pascal recognising textual entail- ment challenge. In Proceedings of the Second PAS- CAL Challenges Workshop on Recognising Textual Entailment, volume 7.
  25. Rowan Hall Maudslay, Hila Gonen, Ryan Cotterell, and Simone Teufel. 2019. It's all in the name: Mitigating gender bias with name-based counterfactual data sub- stitution. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natu- ral Language Processing (EMNLP-IJCNLP), pages 5267-5275, Hong Kong, China. Association for Com- putational Linguistics. · DOI
  26. Beth Haller, Bruce Dorries, and Jessica Rahn. 2006. Media labeling versus the us disability community identity: a study of shifting cultural language. Dis- ability & Society, 21(1):61-75. · DOI
  27. Tianxing He, Jun Liu, Kyunghyun Cho, Myle Ott, Bing Liu, James Glass, and Fuchun Peng. 2021. Analyzing the forgetting problem in pretrain-finetuning of open- domain dialogue response models. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1121-1133, Online. Association for Computational Linguistics. · DOI
  28. Xisen Jin, Francesco Barbieri, Brendan Kennedy, Aida Mostafazadeh Davani, Leonardo Neves, and Xiang Ren. 2021. On transferability of bias mitigation ef- fects in language model fine-tuning. In Proceedings of the 2021 Conference of the North American Chap- ter of the Association for Computational Linguistics: Human Language Technologies, pages 3770-3783, Online. Association for Computational Linguistics. · DOI
  29. Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020. Scaling laws for neural language models. arxiv, abs/2001.08361. · arXiv
  30. Randa Kayyali. 2013. Us census classifications and arab americans: contestations and definitions of identity markers. Journal of Ethnic and Migration Studies, 39(8):1299-1318. · DOI
  31. Anne Lauscher, Archie Crowley, and Dirk Hovy. 2022. Welcome to the modern world of pronouns: Identity- inclusive natural language processing beyond gender. arXiv preprint arXiv:2202.11923. · arXiv
  32. Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, and Xiang Ren. 2020. Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre- Trained Language Models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6862-6868, Online. Association for Computational Linguistics. · DOI
  33. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Man- dar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692. · arXiv
  34. Kristian Lum, Yunfeng Zhang, and Amanda Bower. 2022. De-biasing "bias" measurement. In Proceed- ings of the 2022 ACM Conference on Fairness, Ac- countability, and Transparency, FAccT '22, New York, NY, USA. Association for Computing Machin- ery.
  35. Zhiyi Ma, Kawin Ethayarajh, Tristan Thrush, Somya Jain, Ledell Wu, Robin Jia, Christopher Potts, Ad- ina Williams, and Douwe Kiela. 2021. Dynaboard: An evaluation-as-a-service platform for holistic next- generation benchmarking. Advances in Neural Infor- mation Processing Systems, 34.
  36. Ninareh Mehrabi, Pei Zhou, Fred Morstatter, Jay Pu- jara, Xiang Ren, and Aram Galstyan. 2021. Lawyers are dishonest? quantifying representational harms in commonsense knowledge resources. In Proceedings of the 2021 Conference on Empirical Methods in Nat- ural Language Processing, pages 5016-5033, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. · DOI
  37. Aditya Krishna Menon and Robert C Williamson. 2018. The cost of fairness in binary classification. In Pro- ceedings of the 1st Conference on Fairness, Account- ability and Transparency, volume 81 of Proceed- ings of Machine Learning Research, pages 107-118.
  38. PMLR.
  39. Jack Merullo, Luke Yeh, Abram Handler, Alvin Gris- som II, Brendan O'Connor, and Mohit Iyyer. 2019. Investigating sports commentator bias within a large corpus of American football broadcasts. In Proceed- ings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th Inter- national Joint Conference on Natural Language Pro- cessing (EMNLP-IJCNLP), pages 6354-6360, Hong Kong, China. Association for Computational Linguis- tics. · DOI
  40. Alexander Miller, Will Feng, Dhruv Batra, Antoine Bor- des, Adam Fisch, Jiasen Lu, Devi Parikh, and Jason Weston. 2017. ParlAI: A dialog research software platform. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 79-84, Copenhagen, Denmark. Association for Computational Linguistics. · DOI
  41. Aaron J Moss, Cheskie Rosenzweig, Jonathan Robin- son, and Leib Litman. 2020. Demographic stability on mechanical turk despite covid-19. Trends in cog- nitive sciences, 24(9):678-680.
  42. Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020. Adversarial NLI: A new benchmark for natural language under- standing. In Proceedings of the 58th Annual Meet- ing of the Association for Computational Linguistics, pages 4885-4901, Online. Association for Computa- tional Linguistics. · DOI · Paper page
  43. Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019. fairseq: A fast, extensible toolkit for sequence modeling. In Proceedings of NAACL-HLT 2019: Demonstrations.
  44. Zoe Papakipos and Joanna Bitton. 2022. Augly: Data augmentations for robustness. arXiv preprint arXiv:2201.06494. · arXiv
  45. Kishore Papineni, Salim Roukos, Todd Ward, and Wei- Jing Zhu. 2002. Bleu: a method for automatic evalu- ation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Compu- tational Linguistics, pages 311-318, Philadelphia, Pennsylvania, USA. Association for Computational Linguistics. · DOI · Paper page
  46. Vinodkumar Prabhakaran, Ben Hutchinson, and Mar- garet Mitchell. 2019. Perturbation sensitivity analysis to detect unintended model biases. In Proceedings of the 2019 Conference on Empirical Methods in Nat- ural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5740-5745, Hong Kong, China. Association for Computational Linguistics. · DOI · Paper page
  47. Marcelo OR Prates, Pedro H Avelar, and Luís C Lamb. 2020. Assessing gender bias in machine translation: a case study with google translate. Neural Computing and Applications, 32(10):6363-6381. · DOI · Paper page
  48. Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, and Christopher D. Manning. 2020. Stanza: A python natural language processing toolkit for many human languages. In Proceedings of the 58th Annual Meet- ing of the Association for Computational Linguistics: System Demonstrations, pages 101-108, Online. As- sociation for Computational Linguistics. · DOI
  49. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. SQuAD: 100,000+ questions for machine comprehension of text. In Proceedings of the 2016 Conference on Empirical Methods in Natu- ral Language Processing, pages 2383-2392, Austin, Texas. Association for Computational Linguistics. · DOI · Paper page
  50. Adi Renduchintala and Adina Williams. 2022. Inves- tigating failures of automatic translationin the case of unambiguous gender. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3454- 3469, Dublin, Ireland. Association for Computational Linguistics.
  51. Paul Röttger, Bertie Vidgen, Dong Nguyen, Zeerak Waseem, Helen Margetts, and Janet Pierrehumbert. 2021. HateCheck: Functional tests for hate speech detection models. In Proceedings of the 59th Annual Meeting of the Association for Computational Lin- guistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 41-58, Online. Association for Com- putational Linguistics. · DOI
  52. Rachel Rudinger, Jason Naradowsky, Brian Leonard, and Benjamin Van Durme. 2018. Gender bias in coreference resolution. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 8-14.
  53. João Sedoc and Lyle Ungar. 2019. The role of protected class word lists in bias identification of contextual- ized word representations. In Proceedings of the First Workshop on Gender Bias in Natural Language Pro- cessing, pages 55-61, Florence, Italy. Association for Computational Linguistics. · DOI
  54. Indira Sen, Mattia Samory, Claudia Wagner, and Is- abelle Augenstein. 2022. Counterfactually aug- mented data and unintended bias: The case of sexism and hate speech detection. arXiv preprint arXiv:2205.04238. · arXiv
  55. Eric Michael Smith, Melissa Hall, Melanie Kambadur, Eleonora Presani, and Adina Williams. 2022. "I'm sorry to hear that": finding bias in language mod- els with a holistic descriptor dataset. arXiv preprint arXiv:2205.09209. · arXiv
  56. Eric Michael Smith and Adina Williams. 2021. Hi, my name is Martha: Using names to measure and mitigate bias in generative dialogue models. arXiv preprint arXiv:2109.03300. · arXiv
  57. Tom W Smith. 1992. Changing racial labels: From "colored" to "negro" to "black" to "african american". Public Opinion Quarterly, 56(4):496-514.
  58. Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 Conference on Empiri- cal Methods in Natural Language Processing, pages 1631-1642, Seattle, Washington, USA. Association for Computational Linguistics.
  59. Pierre Stock and Moustapha Cissé. 2018. Convnets and imagenet beyond accuracy: Understanding mistakes and uncovering biases. In Computer Vision -ECCV 2018 -15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part VI, volume 11210 of Lecture Notes in Computer Science, pages 504-519. Springer. · DOI
  60. Tony Sun, Kellie Webster, Apu Shah, William Yang Wang, and Melvin Johnson. 2021. They, them, theirs: Rewriting with gender-neutral english. arXiv preprint arXiv:2102.06788. · arXiv
  61. Zeerak Talat, Hagen Blix, Josef Valvoda, Maya Indira Ganesh, Ryan Cotterell, and Adina Williams. 2021. A word on machine ethics: A response to jiang et al. (2021). arXiv preprint arXiv:2111.04158. · arXiv
  62. Tristan Thrush, Kushal Tirumala, Anmol Gupta, Max Bartolo, Pedro Rodriguez, Tariq Kane, William Gaviria Rojas, Peter Mattson, Adina Williams, and Douwe Kiela. 2022. Dynatask: A framework for creating dynamic AI benchmark tasks. In Proceed- ings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstra- tions, pages 174-181, Dublin, Ireland. Association for Computational Linguistics.
  63. Eva Vanmassenhove and Johanna Monti. 2021. gENder- IT: An annotated English-Italian parallel challenge set for cross-linguistic natural gender phenomena. In Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing, pages 1-7, Online. Association for Computational Linguistics. · DOI
  64. Alex Wang, Yada Pruksachatkun, Nikita Nangia, Aman- preet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2019a. Superglue: A stick- ier benchmark for general-purpose language under- standing systems. In Advances in Neural Information Processing Systems.
  65. Alex Wang, Yada Pruksachatkun, Nikita Nangia, Aman- preet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2019b. SuperGLUE: A stick- ier benchmark for general-purpose language under- standing systems. Advances in neural information processing systems, 32.
  66. Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018. Glue: A multi-task benchmark and analysis platform for natural language understanding. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pages 353-355.
  67. Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, and Xuanjing Huang. 2021. TextFlint: Unified multilingual robustness evaluation toolkit for natural language processing. In Proceedings of the 59th Annual Meeting of the Association for Compu- tational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations, pages 347-355, Online. Association for Computational Linguistics. · DOI
  68. Kellie Webster, Xuezhi Wang, Ian Tenney, Alex Beutel, Emily Pitler, Ellie Pavlick, Jilin Chen, Ed Chi, and Slav Petrov. 2020. Measuring and reducing gendered correlations in pre-trained models. arXiv preprint arXiv:2010.06032. · arXiv
  69. Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. A broad-coverage challenge corpus for sen- tence understanding through inference. In Proceed- ings of the 2018 Conference of the North American Chapter of the Association for Computational Lin- guistics: Human Language Technologies, Volume 1 (Long Papers), pages 1112-1122, New Orleans, Louisiana. Association for Computational Linguis- tics. · DOI · Paper page
  70. Han Zhao and Geoffrey J. Gordon. 2019. Inherent trade- offs in learning fair representations. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Sys- tems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pages 15649-15659.
  71. Jieyu Zhao, Tianlu Wang, Mark Yatskar, Ryan Cotterell, Vicente Ordonez, and Kai-Wei Chang. 2019. Gender bias in contextualized word embeddings. In Proceed- ings of the 2019 Conference of the North American Chapter of the Association for Computational Lin- guistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 629-634, Minneapo- lis, Minnesota. Association for Computational Lin- guistics. · DOI
  72. Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Or- donez, and Kai-Wei Chang. 2018. Gender bias in coreference resolution: Evaluation and debiasing methods. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, Volume 2 (Short Papers), pages 15-20, New Orleans, Louisiana. Association for Computational Linguistics. · DOI · Paper page
  73. Yiyun Zhao and Steven Bethard. 2020. How does BERT's attention change when you fine-tune? an analysis methodology and a case study in negation scope. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4729-4747, Online. Association for Computational Linguistics. · DOI
  74. Lal Zimman and Will Hayworth. 2020. How we got here: Short-scale change in identity labels for trans, cis, and non-binary people in the 2000s. Proceedings of the Linguistic Society of America, 5(1):499-513.
  75. Indre Zliobaite. 2015. On the relation between accu- racy and fairness in binary classification. CoRR, abs/1505.05723. · arXiv
  76. Ran Zmigrod, Sabrina J. Mielke, Hanna Wallach, and Ryan Cotterell. 2019. Counterfactual data augmen- tation for mitigating gender stereotypes in languages with rich morphology. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1651-1661, Florence, Italy. Asso- ciation for Computational Linguistics. · DOI

Cited by

Sorted by citations

Authors
Qian, R., Ross, C., Fernandes, J., Smith, E. M., Kiela, D., & Williams, A.
Affiliations
Meta (United States) (US)
ORCID
Venue
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Publisher
Association for Computational Linguistics
Published
2022
Pages
9496-9521
DOI
10.18653/v1/2022.emnlp-main.646
OpenAlex
W4385573981
Cited by
69
References
18
Citations by year
2026: 10 · 2025: 18 · 2024: 12 · 2023: 26 · 2022: 3
FWCI
13.49
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
Keywords
demographic bias, text perturbation, GLUE benchmark, fairness evaluation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review