Fetching the paper…
Reading the bibliography…
The Right to be Forgotten is a core principle outlined by regulatory frameworks such as the EU's General Data Protection Regulation (GDPR).
Certified Data Removal from Machine Learning Models, August 2020
Chuan Guo, Tom Goldstein, Awni Hannun, and Laurens van der Maaten · 1911
Earlier work this paper cites.
Machine Unlearning, December 2020
Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 1912
Earlier work this paper cites.
Uci machine learning repository, 2007
Arthur Asuncion and David Newman · 2007
Earlier work this paper cites.
Building classifiers with independency constraints
Toon Calders, Faisal Kamiran, and Mykola Pechenizkiy · 2009
Earlier work this paper cites.
High school longitudinal study of 2009 (hsls: 09): Base-year data file documentation. nces 2011-328
Steven J Ingels, Daniel J Pratt, Deborah R Herget, Laura J Burns, Jill A Dever, Randolph Ottem, James E Rogers, Ying Jin, and Steve Leinwand · 2011
Earlier work this paper cites.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
Earlier work this paper cites.
Towards Making Systems Forget with Machine Unlearning
Yinzhi Cao and Junfeng Yang · 2015
Earlier work this paper cites.
Certifying and removing disparate impact
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
Earlier work this paper cites.
On the relation between accuracy and fairness in binary classification
Indre Zliobaite · 2015
Earlier work this paper cites.
Regulation (eu) 2016/679 of the european parliament and of the council of 27 april 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46/ec (general data protection regulation)
European Commission · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
Earlier work this paper cites.
Machine bias
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
Earlier work this paper cites.
A convex framework for fair regression
Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth · 2017
Earlier work this paper cites.
Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2017
Earlier work this paper cites.
Optimized pre-processing for discrimination prevention
Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R Varshney · 2017
Cited alongside, same era.
California consumer privacy act (ccpa), 2018
CCPA · 2018
Cited alongside, same era.
The measure and mismeasure of fairness: A critical review of fair machine learning
Sam Corbett-Davies and Sharad Goel · 2018
Cited alongside, same era.
Multicalibration: Calibration for the (computationally-identifiable) masses
Ursula Hébert-Johnson, Michael Kim, Omer Reingold, and Guy Rothblum · 2018
Cited alongside, same era.
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2018
Cited alongside, same era.
A reductions approach to fair classification
Approximate Data Deletion from Machine Learning Models
Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, and James Zou · 2021
Later among the works it cites.
Algorithm fairness in ai for medicine and healthcare
Richard J Chen, Tiffany Y Chen, Jana Lipkova, Judy J Wang, Drew FK Williamson, Ming Y Lu, Sharifa Sahai, and Faisal Mahmood · 2021
Later among the works it cites.
Blueprint for an AI Bill of Rights
OSTP · 2022
Later among the works it cites.
A Stochastic Optimization Framework for Fair Risk Minimization, September 2022
Andrew Lowy, Sina Baharlouei, Rakesh Pavan, Meisam Razaviyayn, and Ahmad Beirami · 2022
Later among the works it cites.
PUMA: Performance Unchanged Model Augmentation for Training Data Removal
Ga Wu, Masoud Hashemi, and Christopher Srinivasa · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach · 2018
Cited alongside, same era.
Fairness in recommendation ranking through pairwise comparisons
Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H Chi, et al · 2019
Cited alongside, same era.
Degenerate feedback loops in recommender systems
Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, and Pushmeet Kohli · 2019
Cited alongside, same era.
Making AI Forget You: Data Deletion in Machine Learning, November 2019
Antonio Ginart, Melody Y. Guan, Gregory Valiant, and James Zou · 2019
Cited alongside, same era.
Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov · 2019
Cited alongside, same era.
On the compatibility of privacy and fairness
Rachel Cummings, Varun Gupta, Dhamma Kimpara, and Jamie Morgenstern · 2019
Cited alongside, same era.
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning, July 2020
Seth Neel, Aaron Roth, and Saeed Sharifi-Malvajerdi · 2020
Cited alongside, same era.
Wael Alghamdi, Hsiang Hsu, Haewon Jeong, Hao Wang, Peter Michalak, Shahab Asoodeh, and Flavio Calmon · 2022
Later among the works it cites.
Disparate impact in differential privacy from gradient misalignment
Maria S Esipova, Atiyeh Ashari Ghomi, Yaqiao Luo, and Jesse C Cresswell · 2022
Later among the works it cites.
Understanding instance-level impact of fairness constraints
Jialu Wang, Xin Eric Wang, and Yang Liu · 2022
Later among the works it cites.
Fairness without imputation: A decision tree approach for fair prediction with missing values
Haewon Jeong, Hao Wang, and Flavio P Calmon · 2022
Later among the works it cites.
Happymap: A generalized multi-calibration method
Zhun Deng, Cynthia Dwork, and Linjun Zhang · 2023
Closest in time.
Towards bridging the gaps between the right to explanation and the right to be forgotten
Satyapriya Krishna, Jiaqi Ma, and Himabindu Lakkaraju · 2023
Closest in time.
To be forgotten or to be fair: Unveiling fairness implications of machine unlearning methods
Dawen Zhang, Shidong Pan, Thong Hoang, Zhenchang Xing, Mark Staples, Xiwei Xu, Lina Yao, Qinghua Lu, and Liming Zhu · 2023
Closest in time.
No matter how you slice it: Machine unlearning with sisa comes at the expense of minority classes
Korbinian Koch and Marcus Soll · 2023
Closest in time.
Cheng-Long Wang, Mengdi Huai, and Di Wang · 2023
Closest in time.