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Most approaches aiming to ensure a model's fairness with respect to a protected attribute (such as gender or race) assume to know the true value of the attribute for every data point.
Fairness through awareness
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel · 2012
Earlier work this paper cites.
Data preprocessing techniques for classification without discrimination
F. Kamiran and T. Calders · 2012
Earlier work this paper cites.
Fairness-aware classifier with prejudice remover regularizer
T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma · 2012
Earlier work this paper cites.
Classification with asymmetric label noise: Consistency and maximal denoising
C. Scott, G. Blanchard, and G. Handy · 2013
Earlier work this paper cites.
R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork · 2013
Earlier work this paper cites.
The five factor model of personality and evaluation of drug consumption risk
E. Fehrman, A. K. Muhammad, E. M. Mirkes, V. Egan, and A. N. Gorban · 2015
Earlier work this paper cites.
Certifying and removing disparate impact
M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian · 2015
Earlier work this paper cites.
COMPAS risk scales: Demonstrating accuracy equity and predictive parity
W. Dieterich, C. Mendoza, and T. Brennan · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
M. Hardt, E. Price, and N. Srebro · 2016
Earlier work this paper cites.
The variational fair autoencoder
C. Louizos, K. Swersky, Y. Li, M. Welling, and R. Zemel · 2016
Earlier work this paper cites.
Fair clustering through fairlets
F. Chierichetti, R. Kumar, S. Lattanzi, and S. Vassilvitskii · 2017
Earlier work this paper cites.
Inherent trade-offs in the fair determination of risk scores
J. Kleinberg, S. Mullainathan, and M. Raghavan · 2017
Earlier work this paper cites.
On fairness and calibration
G. Pleiss, M. Raghavan, F. Wu, J. Kleinberg, and K. Q. Weinberger · 2017
Earlier work this paper cites.
Learning non-discriminatory predictors
B. Woodworth, S. Gunasekar, M. I. Ohannessian, and N. Srebro · 2017
Cited alongside, same era.
A reductions approach to fair classification
A. Agarwal, A. Beygelzimer, M. Dudík, J. Langford, and H. Wallach · 2018
Cited alongside, same era.
Fairness and Machine Learning
S. Barocas, M. Hardt, and A. Narayanan · 2018
Cited alongside, same era.
Hierarchical VampPrior variational fair auto-encoder
P. Botros and J. M. Tomczak · 2018
Cited alongside, same era.
Fair and diverse DPP-based data summarization
L. E. Celis, V. Keswani, D. Straszak, A. Deshpande, T. Kathuria, and N. K. Vishnoi · 2018
Cited alongside, same era.
The measure and mismeasure of fairness: A critical review of fair machine learning
S. Corbett-Davies and S. Goel · 2018
Cited alongside, same era.
Crowdsourcing with arbitrary adversaries
M. Kleindessner and P. Awasthi · 2018
Later among the works it cites.
The cost of fairness in binary classification
A. K. Menon and R. C. Williamson · 2018
Later among the works it cites.
The price of fair PCA: One extra dimension
S. Samadi, U. Tantipongpipat, J. Morgenstern, M. Singh, and S. Vempala · 2018
Later among the works it cites.
Fair coresets and streaming algorithms for fair k-means clustering
M. Schmidt, C. Schwiegelshohn, and C. Sohler · 2018
Later among the works it cites.
Fairness definitions explained
S. Verma and J. Rubin · 2018
Later among the works it cites.
Fairgan: Fairness-aware generative adversarial networks
D. Xu, S. Yuan, L. Zhang, and X. Wu · 2018
Later among the works it cites.
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Empirical risk minimization under fairness constraints
M. Donini, L. Oneto, S. Ben-David, J. Shawe-Taylor, and M. Pontil · 2018
Cited alongside, same era.
Individual fairness under composition
C. Dwork and C. Ilvent · 2018
Cited alongside, same era.
M. Gupta, A. Cotter, M. M. Fard, and S. Wang · 2018
Cited alongside, same era.
Fairness without demographics in repeated loss minimization
T. Hashimoto, M. Srivastava, H. Namkoong, and P. Liang · 2018
Cited alongside, same era.
Calibration for the (computationally-identifiable) masses
Ú. Hébert-Johnson, M. P. Kim, O. Reingold, and G. N. Rothblum · 2018
Cited alongside, same era.
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
M. Kearns, S. Neel, and Z. S. Roth, A. Wu · 2018
Cited alongside, same era.
Fairness under unawareness: Assessing disparity when protected class is unobserved
J. Chen, N. Kallus, X. Mao, G. Svacha, and M. Udell · 2019
Closest in time.
Fair transfer learning with missing protected attributes
A. Coston, K. N. Ramamurthy, D. Wei, K. Varshney, S. Speakman, Z. Mustahsan, and S. Chakraborty · 2019
Closest in time.
UCI machine learning repository, 2019
D. Dua and C. Graff · 2019
Closest in time.
The fairness of risk scores beyond classification: Bipartite ranking and the XAUC metric
N. Kallus and A. Zhou · 2019
Closest in time.
An empirical study of rich subgroup fairness for machine learning
M. Kearns, S. Neel, and Z. S. Roth, A. Wu · 2019
Closest in time.
Noise-tolerant fair classification
A. L. Lamy, Z. Zhong, A. K. Menon, and N. Verma · 2019
Closest in time.
Multi-criteria dimensionality reduction with applications to fairness
U. Tantipongpipat, S. Samadi, M. Singh, J. Morgenstern, and S. Vempala · 2019
Closest in time.