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Algorithmic fairness has attracted significant attention in recent years, with many quantitative measures suggested for characterizing the fairness of different machine learning algorithms.
A survey on bias and fairness in machine learning
Mehrabi, N.; Morstatter, F.; Saxena, N.; Lerman, K.; and Galstyan, A. 2019 · 1908
Earlier work this paper cites.
Poisoning Attacks on Algorithmic Fairness
Solans, D.; Biggio, B.; and Castillo, C. 2020 · 2004
Earlier work this paper cites.
Statistical Equity: A Fairness Classification Objective
Mehrabi, N.; Huang, Y.; and Morstatter, F. 2020 · 2005
Earlier work this paper cites.
Differential privacy: A survey of results
Dwork, C. 2008 · 2008
Earlier work this paper cites.
Poisoning Attacks against Support Vector Machines
Biggio, B.; Nelson, B.; and Laskov, P. 2012 · 2012
Earlier work this paper cites.
Fairness through awareness
Dwork, C.; Hardt, M.; Pitassi, T.; Reingold, O.; and Zemel, R. 2012 · 2012
Earlier work this paper cites.
Data preprocessing techniques for classification without discrimination
Kamiran, F.; and Calders, T. 2012 · 2012
Earlier work this paper cites.
Fairness-aware classifier with prejudice remover regularizer
Kamishima, T.; Akaho, S.; Asoh, H.; and Sakuma, J. 2012 · 2012
Earlier work this paper cites.
Evasion attacks against machine learning at test time
Biggio, B.; Corona, I.; Maiorca, D.; Nelson, B.; Šrndić, N.; Laskov, P.; Giacinto, G.; and Roli, F. 2013 · 2013
Earlier work this paper cites.
Explaining and Harnessing Adversarial Examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2015 · 2015
Earlier work this paper cites.
Learning fair classifiers
Zafar, M. B.; Valera, I.; Rodriguez, M. G.; and Gummadi, K. P. 2015 · 2015
Earlier work this paper cites.
Equality of opportunity in supervised learning
Hardt, M.; Price, E.; and Srebro, N. 2016 · 2016
Earlier work this paper cites.
Compas analysis
Larson, J.; Mattu, S.; Kirchner, L.; and Angwin, J. 2016 · 2016
Cited alongside, same era.
Deepfool: a simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S.-M.; Fawzi, A.; and Frossard, P. 2016 · 2016
Cited alongside, same era.
UCI Machine Learning Repository
Dua, D.; and Graff, C. 2017 · 2017
Cited alongside, same era.
The five factor model of personality and evaluation of drug consumption risk
Fehrman, E.; Muhammad, A. K.; Mirkes, E. M.; Egan, V.; and Gorban, A. N. 2017 · 2017
Cited alongside, same era.
Understanding black-box predictions via influence functions
Koh, P. W.; and Liang, P. 2017 · 2017
Cited alongside, same era.
Counterfactual Fairness
Kusner, M. J.; Loftus, J.; Russell, C.; and Silva, R. 2017 · 2017
Cited alongside, same era.
The accuracy, fairness, and limits of predicting recidivism
Dressel, J.; and Farid, H. 2018 · 2018
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Stronger data poisoning attacks break data sanitization defenses
Koh, P. W.; Steinhardt, J.; and Liang, P. 2018 · 2018
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Security matters: A survey on adversarial machine learning
Li, G.; Zhu, P.; Li, J.; Yang, Z.; Cao, N.; and Chen, Z. 2018 · 2018
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Invariant representations without adversarial training
Moyer, D.; Gao, S.; Brekelmans, R.; Galstyan, A.; and Ver Steeg, G. 2018 · 2018
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Poison frogs! targeted clean-label poisoning attacks on neural networks
Shafahi, A.; Huang, W. R.; Najibi, M.; Suciu, O.; Studer, C.; Dumitras, T.; and Goldstein, T. 2018 · 2018
Later among the works it cites.
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On Fairness and Calibration
Pleiss, G.; Raghavan, M.; Wu, F.; Kleinberg, J.; and Weinberger, K. Q. 2017 · 2017
Cited alongside, same era.
Certified Defenses for Data Poisoning Attacks
Steinhardt, J.; Koh, P. W. W.; and Liang, P. S. 2017 · 2017
Cited alongside, same era.
Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Zafar, M. B.; Valera, I.; Gomez Rodriguez, M.; and Gummadi, K. P. 2017 · 2017
Cited alongside, same era.
Achieving non-discrimination in data release
Zhang, L.; Wu, Y.; and Wu, X. 2017 · 2017
Cited alongside, same era.
Adversarial attacks and defences: A survey
Chakraborty, A.; Alam, M.; Dey, V.; Chattopadhyay, A.; and Mukhopadhyay, D. 2018 · 2018
Cited alongside, same era.
Fairness Definitions Explained
Verma, S.; and Rubin, J. 2018 · 2018
Later among the works it cites.
Fair Regression: Quantitative Definitions and Reduction-Based Algorithms
Agarwal, A.; Dudik, M.; and Wu, Z. S. 2019 · 2019
Later among the works it cites.
Differential privacy has disparate impact on model accuracy
Bagdasaryan, E.; Poursaeed, O.; and Shmatikov, V. 2019 · 2019
Later among the works it cites.
Differentially Private Fair Learning
Jagielski, M.; Kearns, M.; Mao, J.; Oprea, A.; Roth, A.; Malvajerdi, S. S.; and Ullman, J. 2019 · 2019
Later among the works it cites.
Fairness without Harm: Decoupled Classifiers with Preference Guarantees
Ustun, B.; Liu, Y.; and Parkes, D. 2019 · 2019
Later among the works it cites.
Fair decision making using privacy-protected data
Pujol, D.; McKenna, R.; Kuppam, S.; Hay, M.; Machanavajjhala, A.; and Miklau, G. 2020 · 2020
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