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In current hate speech datasets, there exists a high correlation between annotators' perceptions of toxicity and signals of African American English (AAE).
Empirical analysis of multi-task learning for reducing model bias in toxic comment detection
Ameya Vaidya, Feng Mai, and Yue Ning. 2019 · 1909
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Censoring representations with an adversary
Harrison Edwards and Amos Storkey. 2015 · 2015
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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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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro. 2016 · 2016
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Analyzing the targets of hate in online social media
Leandro Silva, Mainack Mondal, Denzil Correa, Fabrício Benevenuto, and Ingmar Weber. 2016 · 2016
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Are you a racist or am i seeing things? annotator influence on hate speech detection on twitter
Zeerak Waseem. 2016 · 2016
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Hateful symbols or hateful people? predictive features for hate speech detection on twitter
Zeerak Waseem and Dirk Hovy. 2016 · 2016
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Automated hate speech detection and the problem of offensive language
Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. 2017 · 2017
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Incorporating dialectal variability for socially equitable language identification
David Jurgens, Yulia Tsvetkov, and Dan Jurafsky. 2017 · 2017
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A measurement study of hate speech in social media
Mainack Mondal, Leandro Araújo Silva, and Fabrício Benevenuto. 2017 · 2017
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A survey on hate speech detection using natural language processing
Anna Schmidt and Michael Wiegand. 2017 · 2017
Cited alongside, same era.
Controllable invariance through adversarial feature learning
Qizhe Xie, Zihang Dai, Yulun Du, Eduard Hovy, and Graham Neubig. 2017 · 2017
Cited alongside, same era.
Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2017 · 2017
Cited alongside, same era.
Data statements for natural language processing: Toward mitigating system bias and enabling better science
Emily M Bender and Batya Friedman. 2018 · 2018
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Measuring and mitigating unintended bias in text classification
Lucas Dixon, John Li, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman. 2018 · 2018
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Adversarial removal of demographic attributes from text data
Adversarial removal of gender from deep image representations
Tianlu Wang, Jieyu Zhao, Kai-Wei Chang, Mark Yatskar, and Vicente Ordonez. 2018 · 2018
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Mind the gap: A balanced corpus of gendered ambiguous pronouns
Kellie Webster, Marta Recasens, Vera Axelrod, and Jason Baldridge. 2018 · 2018
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Mitigating unwanted biases with adversarial learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell. 2018 · 2018
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Topics to avoid: Demoting latent confounds in text classification
Sachin Kumar, Shuly Wintner, Noah A Smith, and Yulia Tsvetkov. 2019 · 2019
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Multiple-attribute text rewriting
Guillaume Lample, Sandeep Subramanian, Eric Smith, Ludovic Denoyer, Marc’Aurelio Ranzato, and Y-Lan Boureau. 2019 · 2019
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Yanai Elazar and Yoav Goldberg. 2018 · 2018
Cited alongside, same era.
A survey on automatic detection of hate speech in text
Paula Fortuna and Sérgio Nunes. 2018 · 2018
Cited alongside, same era.
Large scale crowdsourcing and characterization of twitter abusive behavior
Antigoni Maria Founta, Constantinos Djouvas, Despoina Chatzakou, Ilias Leontiadis, Jeremy Blackburn, Gianluca Stringhini, Athena Vakali, Michael Sirivianos, and Nicolas Kourtellis. 2018 · 2018
Cited alongside, same era.
Towards robust and privacy-preserving text representations
Yitong Li, Timothy Baldwin, and Trevor Cohn. 2018 · 2018
Cited alongside, same era.
Bias amplification in artificial intelligence systems
Kirsten Lloyd. 2018 · 2018
Cited alongside, same era.
Discovering and controlling for latent confounds in text classification using adversarial domain adaptation
Virgile Landeiro, Tuan Tran, and Aron Culotta. 2019 · 2019
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Spread of hate speech in online social media
Binny Mathew, Ritam Dutt, Pawan Goyal, and Animesh Mukherjee. 2019 · 2019
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The risk of racial bias in hate speech detection
Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, and Noah A Smith. 2019 · 2019
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Detection of abusive language: the problem of biased datasets
Michael Wiegand, Josef Ruppenhofer, and Thomas Kleinbauer. 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
Later among the works it cites.