Fetching the paper…
Reading the bibliography…
Adversarial training is a common approach for bias mitigation in natural language processing.
Concerning optimization of vector functionals. i. programming of optimal trajectories
M Ye Salukvadze. 1971 · 1971
Earlier work this paper cites.
Survey of multi-objective optimization methods for engineering
R Timothy Marler and Jasbir S Arora. 2004 · 2004
Earlier work this paper cites.
Certifying and removing disparate impact
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. 2015 · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederick P Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Demographic dialectal variation in social media: A case study of African-American English
Su Lin Blodgett, Lisa Green, and Brendan O’Connor. 2016 · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro. 2016 · 2016
Earlier work this paper cites.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova. 2017 · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Adversarial removal of demographic attributes from text data
Yanai Elazar and Yoav Goldberg. 2018 · 2018
Earlier work this paper cites.
What’s in a domain? learning domain-robust text representations using adversarial training
Yitong Li, Timothy Baldwin, and Trevor Cohn. 2018b · 2018
Cited alongside, same era.
Achieving fairness through adversarial learning: an application to recidivism prediction
Christina Wadsworth, Francesca Vera, and Chris Piech. 2018 · 2018
Cited alongside, same era.
Mitigating unwanted biases with adversarial learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell. 2018 · 2018
Cited alongside, same era.
Gender bias in coreference resolution: Evaluation and debiasing methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018 · 2018
Cited alongside, same era.
Stereotypical bias removal for hate speech detection task using knowledge-based generalizations
Pinkesh Badjatiya, Manish Gupta, and Vasudeva Varma. 2019 · 2019
Cited alongside, same era.
Fairness and Machine Learning
Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations
Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, and Vicente Ordonez. 2019 · 2019
Later among the works it cites.
Conditional learning of fair representations
Han Zhao, Amanda Coston, Tameem Adel, and Geoffrey J Gordon. 2019 · 2019
Later among the works it cites.
Fairness without demographics through adversarially reweighted learning
Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, and Ed Chi. 2020 · 2020
Later among the works it cites.
Null it out: Guarding protected attributes by iterative nullspace projection
Shauli Ravfogel, Yanai Elazar, Hila Gonen, Michael Twiton, and Yoav Goldberg. 2020 · 2020
Later among the works it cites.
Decoupling adversarial training for fair NLP
Xudong Han, Timothy Baldwin, and Trevor Cohn. 2021b · 2021
Later among the works it cites.
Fairbatch: Batch selection for model fairness
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Solon Barocas, Moritz Hardt, and Arvind Narayanan. 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.
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.
Balancing out bias: Achieving fairness through training reweighting
Xudong Han, Timothy Baldwin, and Trevor Cohn. 2021a
Cited in the paper.
Diverse adversaries for mitigating bias in training
Xudong Han, Timothy Baldwin, and Trevor Cohn. 2021c
Cited in the paper.
Towards robust and privacy-preserving text representations
Yitong Li, Timothy Baldwin, and Trevor Cohn. 2018a
Cited in the paper.
Yuji Roh, Kangwook Lee, Steven Euijong Whang, and Changho Suh. 2021 · 2021
Later among the works it cites.
Evaluating debiasing techniques for intersectional biases
Shivashankar Subramanian, Xudong Han, Timothy Baldwin, Trevor Cohn, and Lea Frermann. 2021 · 2021
Later among the works it cites.
fairlib: A unified framework for assessing and improving classification fairness
Xudong Han, Aili Shen, Yitong Li, Lea Frermann, Timothy Baldwin, and Trevor Cohn. 2022 · 2022
Closest in time.