Fetching the paper…
Reading the bibliography…
Encoded text representations often capture sensitive attributes about individuals (e.g., race or gender), which raise privacy concerns and can make downstream models unfair to certain groups.
Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid
Ron Kohavi. 1996 · 1996
Earlier work this paper cites.
Removing disparate impact of differentially private stochastic gradient descent on model accuracy
Depeng Xu, Wei Du, and Xintao Wu. 2020 · 2003
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam D. Smith. 2006 · 2006
Earlier work this paper cites.
On the privacy risks of algorithmic fairness
Hongyan Chang and Reza Shokri. 2020 · 2011
Earlier work this paper cites.
What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam D. Smith. 2011 · 2011
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth. 2014 · 2014
Earlier work this paper cites.
Rappor: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova. 2014 · 2014
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky. 2015 · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. 2015 · 2015
Earlier work this paper cites.
Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov. 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.
Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y. Zou, Venkatesh Saligrama, and Adam Tauman Kalai. 2016 · 2016
Earlier work this paper cites.
Building a RAPPOR with the unknown: Privacy-preserving learning of associations and data dictionaries
Giulia Fanti, Vasyl Pihur, and Úlfar Erlingsson. 2016 · 2016
Earlier work this paper cites.
Collecting telemetry data privately
Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin. 2017 · 2017
Earlier work this paper cites.
UCI machine learning repository
Dheeru Dua and Casey Graff. 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.
Fader networks: Manipulating images by sliding attributes
Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic Denoyer, and Marc’Aurelio Ranzato. 2017 · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017 · 2017
Cited alongside, same era.
The us census bureau adopts differential privacy
John M Abowd. 2018 · 2018
Cited alongside, same era.
Privacy-preserving neural representations of text
Maximin Coavoux, Shashi Narayan, and Shay B. Cohen. 2018 · 2018
Cited alongside, same era.
Adversarial removal of demographic attributes from text data
Yanai Elazar and Yoav Goldberg. 2018 · 2018
Cited alongside, same era.
Examining gender and race bias in two hundred sentiment analysis systems
Svetlana Kiritchenko and Saif Mohammad. 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.
Hiring algorithms: An ethnography of fairness in practice
Elmira van den Broek, Anastasia Sergeeva, and Marleen Huysman. 2019 · 2019
Later among the works it cites.
On convexity and bounds of fairness-aware classification
Yongkai Wu, Lu Zhang, and Xintao Wu. 2019 · 2019
Later among the works it cites.
Fair differential privacy can mitigate the disparate impact on model accuracy
Wenyan Liu, Xiangfeng Wang, Xingjian Lu, Junhong Cheng, Bo Jin, Xiaoling Wang, and Hongyuan Zha. 2020 · 2020
Later among the works it cites.
Too relaxed to be fair
Michael Lohaus, Michael Perrot, and Ulrike Von Luxburg. 2020 · 2020
Later among the works it cites.
Differentially private representation for NLP: formal guarantee and an empirical study on privacy and fairness
Lingjuan Lyu, Xuanli He, and Yitong Li. 2020 · 2020
Later among the works it cites.
Fair decision making using privacy-protected data
David Pujol, Ryan McKenna, Satya Kuppam, Michael Hay, Ashwin Machanavajjhala, and Gerome Miklau. 2020 · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
To reverse the gradient or not: an empirical comparison of adversarial and multi-task learning in speech recognition
Yossi Adi, Neil Zeghidour, Ronan Collobert, Nicolas Usunier, Vitaliy Liptchinsky, and Gabriel Synnaeve. 2019 · 2019
Cited alongside, same era.
Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov. 2019 · 2019
Cited alongside, same era.
On the compatibility of privacy and fairness
Rachel Cummings, Varun Gupta, Dhamma Kimpara, and Jamie Morgenstern. 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 M. Wallach, Jennifer T. Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Cem 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.
Later among the works it cites.
Mitigating bias in algorithmic hiring: evaluating claims and practices
Manish Raghavan, Solon Barocas, Jon M. Kleinberg, and Karen Levy. 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.
Information leakage in embedding models
Congzheng Song and Ananth Raghunathan. 2020 · 2020
Later among the works it cites.
Information-theoretic probing with minimum description length
Elena Voita and Ivan Titov. 2020 · 2020
Later among the works it cites.
Double-hard debias: Tailoring word embeddings for gender bias mitigation
Tianlu Wang, Xi Victoria Lin, Nazneen Fatema Rajani, Bryan McCann, Vicente Ordonez, and Caiming Xiong. 2020 · 2020
Later among the works it cites.
Adversarial scrubbing of demographic information for text classification
Somnath Basu Roy Chowdhury, Sayan Ghosh, Yiyuan Li, Junier Oliva, Shashank Srivastava, and Snigdha Chaturvedi. 2021 · 2021
Later among the works it cites.
When differential privacy meets NLP: the devil is in the detail
Ivan Habernal. 2021 · 2021
Later among the works it cites.
Diverse adversaries for mitigating bias in training
Xudong Han, Timothy Baldwin, and Trevor Cohn. 2021 · 2021
Later among the works it cites.
ADePT: Auto-encoder based differentially private text transformation
Satyapriya Krishna, Rahul Gupta, and Christophe Dupuy. 2021 · 2021
Later among the works it cites.
Cape: Context-aware private embeddings for private language learning
Richard Plant, Dimitra Gkatzia, and Valerio Giuffrida. 2021 · 2021
Later among the works it cites.
Disparate vulnerability to membership inference attacks
Bogdan Kulynych, Mohammad Yaghini, Giovanni Cherubin, Michael Veale, and Carmela Troncoso. 2022 · 2022
Closest in time.