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Multiple metrics have been introduced to measure fairness in various natural language processing tasks.
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 · 1901
Earlier work this paper cites.
Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020 · 1909
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 1910
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Reducing sentiment bias in language models via counterfactual evaluation
Po-Sen Huang, Huan Zhang, Ray Jiang, Robert Stanforth, Johannes Welbl, Jack W. Rae, Vishal Maini, Dani Yogatama, and Pushmeet Kohli. 2020 · 1911
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Social bias frames: Reasoning about social and power implications of language
Maarten Sap, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, and Yejin Choi. 2020 · 1911
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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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Stereoset: Measuring stereotypical bias in pretrained language models
Moin Nadeem, Anna Bethke, and Siva Reddy. 2020 · 2004
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Detecting emergent intersectional biases: Contextualized word embeddings contain a distribution of human-like biases
Wei Guo and Aylin Caliskan. 2021 · 2006
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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 · 2010
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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. 2020 · 2010
Cited alongside, same era.
Fairness and robustness in invariant learning: A case study in toxicity classification
Robert Adragna, Elliot Creager, David Madras, and Richard S. Zemel. 2020 · 2011
Cited alongside, same era.
Hatexplain: A benchmark dataset for explainable hate speech detection
Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukherjee. 2020 · 2012
Cited alongside, same era.
The Problem With Bias: Allocative Versus Representational Harms in Machine Learning
Solon Barocas, Kate Crawford, Aaron Shapiro, and Hanna Wallach. 2017 · 2017
Cited alongside, same era.
Nuanced metrics for measuring unintended bias with real data for text classification
BERT has a mouth, and it must speak: BERT as a Markov random field language model
Alex Wang and Kyunghyun Cho. 2019 · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, and Quoc V. Le. 2019 · 2019
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Gender bias in contextualized word embeddings
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Ryan Cotterell, Vicente Ordonez, and Kai-Wei Chang. 2019 · 2019
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Demographics should not be the reason of toxicity: Mitigating discrimination in text classifications with instance weighting
Guanhua Zhang, Bing Bai, Junqi Zhang, Kun Bai, Conghui Zhu, and Tiejun Zhao. 2020 · 2020
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GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow
Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman. 2021 · 2021
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Daniel Borkan, Lucas Dixon, Jeffrey Scott Sorensen, Nithum Thain, and Lucy Vasserman. 2019 · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Jigsaw unintended bias in toxicity classification
Conversational AI Jigsaw. 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.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Cited alongside, same era.
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 · 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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Analyzing stereotypes in generative text inference tasks
Anna Sotnikova, Yang Trista Cao, Hal Daumé III, and Rachel Rudinger. 2021 · 2021
Later among the works it cites.