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If our models are used in new or unexpected cases, do we know if they will make fair predictions? Previously, researchers developed ways to debias a model for a single problem domain.
Analysis of representations for domain adaptation
S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira · 2007
Earlier work this paper cites.
Learning from multiple sources
K. Crammer, M. Kearns, and J. Wortman · 2008
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Domain adaptation: Learning bounds and algorithms
Y. Mansour, M. Mohri, and A. Rostamizadeh · 2009
Earlier work this paper cites.
A theory of learning from different domains
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan · 2010
Earlier work this paper cites.
A survey on transfer learning
S. J. Pan, Q. Yang, et al · 2010
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A kernel two-sample test
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola · 2012
Earlier work this paper cites.
Learning transferable features with deep adaptation networks
M. Long, Y. Cao, J. Wang, and M. Jordan · 2015
Earlier work this paper cites.
Hidden technical debt in machine learning systems
D. Sculley, G. Holt, D. Golovin, E. Davydov, T. Phillips, D. Ebner, V. Chaudhary, M. Young, J.-F. Crespo, and D. Dennison · 2015
Earlier work this paper cites.
Domain separation networks
K. Bousmalis, G. Trigeorgis, N. Silberman, D. Krishnan, and D. Erhan · 2016
Earlier work this paper cites.
Censoring representations with an adversary
H. Edwards and A. J. Storkey · 2016
Earlier work this paper cites.
Domain-adversarial training of neural networks
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky · 2016
Earlier work this paper cites.
Satisfying real-world goals with dataset constraints
G. Goh, A. Cotter, M. Gupta, and M. P. Friedlander · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
M. Hardt, E. Price, N. Srebro, et al · 2016
Cited alongside, same era.
The variational fair autoencoder
C. Louizos, K. Swersky, Y. Li, M. Welling, and R. S. Zemel · 2016
Cited alongside, same era.
A survey of transfer learning
K. Weiss, T. M. Khoshgoftaar, and D. Wang · 2016
Cited alongside, same era.
Data decisions and theoretical implications when adversarially learning fair representations
A. Beutel, J. Chen, Z. Zhao, and E. H. Chi · 2017
Cited alongside, same era.
C. Lan and J. Huan · 2017
Cited alongside, same era.
M. R. Gupta, A. Cotter, M. M. Fard, and S. Wang · 2018
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Residual unfairness in fair machine learning from prejudiced data
N. Kallus and A. Zhou · 2018
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Towards robust and privacy-preserving text representations
Y. Li, T. Baldwin, and T. Cohn · 2018
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Learning adversarially fair and transferable representations
D. Madras, E. Creager, T. Pitassi, and R. Zemel · 2018
Later among the works it cites.
There is no free lunch in adversarial robustness (but there are unexpected benefits)
D. Tsipras, S. Santurkar, L. Engstrom, A. Turner, and A. Madry · 2018
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Fairness constraints: Mechanisms for fair classification
M. B. Zafar, I. Valera, M. Gomez-Rodriguez, and K. P. Gummadi · 2017
Cited alongside, same era.
A reductions approach to fair classification
A. Agarwal, A. Beygelzimer, M. Dudík, J. Langford, and H. M. Wallach · 2018
Cited alongside, same era.
Why is my classifier discriminatory?
I. Chen, F. D. Johansson, and D. Sontag · 2018
Cited alongside, same era.
Measuring and mitigating unintended bias in text classification
L. Dixon, J. Li, J. Sorensen, N. Thain, and L. Vasserman · 2018
Cited alongside, same era.
Later among the works it cites.
Mitigating unwanted biases with adversarial learning
B. H. Zhang, B. Lemoine, and M. Mitchell · 2018
Later among the works it cites.
Putting fairness principles into practice: Challenges, metrics, and improvements
A. Beutel, J. Chen, T. Doshi, H. Qian, A. Woodruff, C. Luu, P. Kreitmann, J. Bischof, and E. H. Chi · 2019
Closest in time.
Fairness under unawareness: Assessing disparity when protected class is unobserved
J. Chen, N. Kallus, X. Mao, G. Svacha, and M. Udell · 2019
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Fair transfer learning with missing protected attributes
A. Coston, K. N. Ramamurthy, D. Wei, K. R. Varshney, S. Speakman, Z. Mustahsan, and S. Chakraborty · 2019
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Assessing disparate impacts of personalized interventions: Identifiability and bounds
N. Kallus and A. Zhou · 2019
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