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We present the Assignment-Maximization Spectral Attribute removaL (AMSAL) algorithm, which erases information from neural representations when the information to be erased is implicit rather than directly being aligned to each input example.
Measuring social bias in knowledge graph embeddings
Joseph Fisher, Dave Palfrey, Christos Christodoulopoulos, and Arpit Mittal. 2019 · 1912
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The hungarian method for the assignment problem
Harold W. Kuhn. 1955 · 1955
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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 · 1967
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Perturbation theory for the singular value decomposition
Gilbert W Stewart. 1990 · 1990
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Information theory, inference and learning algorithms
David J C MacKay. 2003 · 2003
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On minimum-cost assignments in unbalanced bipartite graphs
Lyle Ramshaw and Robert E Tarjan. 2012 · 2012
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Demographic dialectal variation in social media: A case study of African-American English
Su Lin Blodgett, Lisa Green, and Brendan O’Connor. 2016 · 2016
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
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Censoring representations with an adversary
Harrison Edwards and Amos J. Storkey. 2016 · 2016
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Fasttext. zip: Compressing text classification models
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Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan. 2017 · 2017
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Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
Bjarke Felbo, Alan Mislove, Anders Søgaard, Iyad Rahwan, and Sune Lehmann. 2017 · 2017
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Expressively vulgar: The socio-dynamics of vulgarity and its effects on sentiment analysis in social media
Isabel Cachola, Eric Holgate, Daniel Preoţiuc-Pietro, and Junyi Jessy Li. 2018 · 2018
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Privacy-preserving neural representations of text
Maximin Coavoux, Shashi Narayan, and Shay B. Cohen. 2018 · 2018
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Adversarial removal of demographic attributes from text data
Yanai Elazar and Yoav Goldberg. 2018 · 2018
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Towards robust and privacy-preserving text representations
Yitong Li, Timothy Baldwin, and Trevor Cohn. 2018 · 2018
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Learning gender-neutral word embeddings
Jieyu Zhao, Yichao Zhou, Zeyu Li, Wei Wang, and Kai-Wei Chang. 2018 · 2018
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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
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BERT: Pre-training of deep bidirectional transformers for language understanding
Contrastive learning for fair representations
Aili Shen, Xudong Han, Trevor Cohn, Timothy Baldwin, and Lea Frermann. 2021 · 2021
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Dynamically disentangling social bias from task-oriented representations with adversarial attack
Liwen Wang, Yuanmeng Yan, Keqing He, Yanan Wu, and Weiran Xu. 2021 · 2021
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Conditional supervised contrastive learning for fair text classification
Jianfeng Chi, William Shand, Yaodong Yu, Kai-Wei Chang, Han Zhao, and Yuan Tian. 2022 · 2022
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Learning disentangled textual representations via statistical measures of similarity
Pierre Colombo, Guillaume Staerman, Nathan Noiry, and Pablo Piantanida. 2022 · 2022
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Understanding gender bias in knowledge base embeddings
Yupei Du, Qi Zheng, Yuanbin Wu, Man Lan, Yan Yang, and Meirong Ma. 2022 · 2022
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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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
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On measuring social biases in sentence encoders
Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, and Rachel Rudinger. 2019 · 2019
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019 · 2019
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Null it out: Guarding protected attributes by iterative nullspace projection
Shauli Ravfogel, Yanai Elazar, Hila Gonen, Michael Twiton, and Yoav Goldberg. 2020 · 2020
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CausaLM: Causal model explanation through counterfactual language models
Amir Feder, Nadav Oved, Uri Shalit, and Roi Reichart. 2021 · 2021
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Decoupling adversarial training for fair NLP
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fairlib: A unified framework for assessing and improving classification fairness
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Easy adaptation to mitigate gender bias in multilingual text classification
Xiaolei Huang. 2022 · 2022
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An empirical survey of the effectiveness of debiasing techniques for pre-trained language models
Nicholas Meade, Elinor Poole-Dayan, and Siva Reddy. 2022 · 2022
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Linear adversarial concept erasure
Shauli Ravfogel, Michael Twiton, Yoav Goldberg, and Ryan D Cotterell. 2022 · 2022
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Under the morphosyntactic lens: A multifaceted evaluation of gender bias in speech translation
Beatrice Savoldi, Marco Gaido, Luisa Bentivogli, Matteo Negri, and Marco Turchi. 2022 · 2022
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Gold doesn’t always glitter: Spectral removal of linear and nonlinear guarded attribute information
Shun Shao, Yftah Ziser, and Shay B Cohen. 2023 · 2023
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. 2016 · 2030
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