Built on

L. Neuberg, “Causality: models, reasoning, and inference, by judea pearl, cambridge university press, 2000,” Econometric Theory , vol. 19, pp. 675–685, 2003
2003
Earlier work this paper cites.
D. Pedreshi, S. Ruggieri, and F. Turini, “Discrimination-aware data mining,” in KDD’08 , 2008, pp. 560–568
2008
Earlier work this paper cites.
T. Calders, F. Kamiran, and M. Pechenizkiy, “Building classifiers with independency constraints,” in ICDM Workshops . IEEE, 2009, pp. 13–18
2009
Earlier work this paper cites.
J. Pearl et al. , “Causal inference in statistics: An overview,” Statistics surveys , vol. 3, pp. 96–146, 2009
2009
Earlier work this paper cites.
I.-C. Yeh and C.-h. Lien, “The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients,” Expert Syst. Appl. , vol. 36, no. 2, pp. 2473–2480, Mar. 2009
2009
Earlier work this paper cites.
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel, “Fairness through awareness,” in ITCS’12 , 2012, pp. 214–226
2012
Earlier work this paper cites.
F. Kamiran and T. Calders, “Data preprocessing techniques for classification without discrimination,” Knowledge and Informatoin Systems , vol. 33, no. 1, pp. 1–33, 2012
2012
Earlier work this paper cites.
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola, “A kernel two-sample test,” Journal of Machine Learning Research , vol. 13, no. Mar, pp. 723–773, 2012
2012
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114 , 2013
Original
2013
Earlier work this paper cites.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative Adversarial Networks,” pp. 1–9, 2014
2014
Earlier work this paper cites.
S. Moro, P. Cortez, and P. Rita, “A data-driven approach to predict the success of bank telemarketing,” Decision Support Systems , vol. 62, 06 2014
2014
Earlier work this paper cites.
M. B. Zafar, I. Valera, M. G. Rodriguez, and K. P. Gummadi, “Fairness constraints: Mechanisms for fair classification,” arXiv preprint arXiv:1507.05259 , 2015
Original
2015
Earlier work this paper cites.
S. R. Bowman, L. Vilnis, O. Vinyals, A. M. Dai, R. Jozefowicz, and S. Bengio, “Generating sentences from a continuous space,” arXiv:1511.06349 , 2015
Original
2015
Earlier work this paper cites.
A. Makhzani, J. Shlens, N. Jaitly, and I. J. Goodfellow, “Adversarial autoencoders,” CoRR , vol. abs/1511.05644, 2015. [Online]. Available: http://arxiv.org/abs/1511.05644
Original
2015
Earlier work this paper cites.
C. Louizos, K. Swersky, Y. Li, M. Welling, and R. Zemel, “The variational fair autoencoder,” arXiv preprint arXiv:1511.00830 , 2015
Original
2015
Earlier work this paper cites.

Similar

T. Bolukbasi, K.-W. Chang, J. Y. Zou, V. Saligrama, and A. T. Kalai, “Man is to computer programmer as woman is to homemaker? debiasing word embeddings,” in Advances in neural information processing systems , 2016, pp. 4349–4357
2016
Cited alongside, same era.
J. Angwin, J. Larson, S. Mattu, and L. Kirchner, “Machine bias. ProPublica, May 23, 2016,” 2016
2016
Cited alongside, same era.
M. Hardt, E. Price, and N. Srebro, “Equality of opportunity in supervised learning,” in Advances in neural information processing systems , 2016, pp. 3315–3323
2016
Cited alongside, same era.
S. D. Team et al. , “Rstan: the r interface to stan,” R package version , vol. 2, no. 1, 2016
2016
Cited alongside, same era.
X. Chen, D. P. Kingma, T. Salimans, Y. Duan, P. Dhariwal, J. Schulman, I. Sutskever, and P. Abbeel, “Variational lossy autoencoder,” arXiv preprint arXiv:1611.02731 , 2016
Original

Then

J. H. Hinnefeld, P. Cooman, N. Mammo, and R. Deese, “Evaluating fairness metrics in the presence of dataset bias,” arXiv preprint arXiv:1809.09245 , 2018
Original
2018
Later among the works it cites.
B. H. Zhang, B. Lemoine, and M. Mitchell, “Mitigating unwanted biases with adversarial learning,” in AAAI’18 , 2018, pp. 335–340
2018
Later among the works it cites.
R. K. Bellamy, K. Dey, M. Hind, S. C. Hoffman, S. Houde, K. Kannan, P. Lohia, J. Martino, S. Mehta, A. Mojsilovic et al. , “Ai fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias,” arXiv preprint arXiv:1810.01943 , 2018
Original
2018
Later among the works it cites.
C. Wadsworth, F. Vera, and C. Piech, “Achieving fairness through adversarial learning: an application to recidivism prediction,” arXiv:1807.00199 , 2018
Original
2018
Later among the works it cites.

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alphaXiv searches the wider corpus for related work and actual follow-ups.

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2020

Adversarial Learning for Counterfactual Fairness

Grari, Vincent, Lamprier, Sylvain, Detyniecki, Marcin

Understand

In recent years, fairness has become an important topic in the machine learning research community.

In particular, counterfactual fairness aims at building prediction models which ensure fairness at the most individual level.
Rather than globally considering equity over the entire population, the idea is to imagine what any individual would look like with a variation of a given attribute of interest, such as a different gender or race for instance.
Existing approaches rely on Variational Auto-encoding of individuals, using Maximum Mean Discrepancy (MMD) penalization to limit the statistical dependence of inferred representations with their corresponding sensitive attributes.

Built on

L. Neuberg, “Causality: models, reasoning, and inference, by judea pearl, cambridge university press, 2000,” Econometric Theory , vol. 19, pp. 675–685, 2003
2003
Earlier work this paper cites.
D. Pedreshi, S. Ruggieri, and F. Turini, “Discrimination-aware data mining,” in KDD’08 , 2008, pp. 560–568
2008
Earlier work this paper cites.
T. Calders, F. Kamiran, and M. Pechenizkiy, “Building classifiers with independency constraints,” in ICDM Workshops . IEEE, 2009, pp. 13–18
2009
Earlier work this paper cites.
J. Pearl et al. , “Causal inference in statistics: An overview,” Statistics surveys , vol. 3, pp. 96–146, 2009
2009
Earlier work this paper cites.
I.-C. Yeh and C.-h. Lien, “The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients,” Expert Syst. Appl. , vol. 36, no. 2, pp. 2473–2480, Mar. 2009
2009
Earlier work this paper cites.
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel, “Fairness through awareness,” in ITCS’12 , 2012, pp. 214–226
2012
Earlier work this paper cites.
F. Kamiran and T. Calders, “Data preprocessing techniques for classification without discrimination,” Knowledge and Informatoin Systems , vol. 33, no. 1, pp. 1–33, 2012
2012
Earlier work this paper cites.
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola, “A kernel two-sample test,” Journal of Machine Learning Research , vol. 13, no. Mar, pp. 723–773, 2012
2012
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114 , 2013
Original
2013
Earlier work this paper cites.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative Adversarial Networks,” pp. 1–9, 2014
2014
Earlier work this paper cites.
S. Moro, P. Cortez, and P. Rita, “A data-driven approach to predict the success of bank telemarketing,” Decision Support Systems , vol. 62, 06 2014
2014
Earlier work this paper cites.
M. B. Zafar, I. Valera, M. G. Rodriguez, and K. P. Gummadi, “Fairness constraints: Mechanisms for fair classification,” arXiv preprint arXiv:1507.05259 , 2015
Original
2015
Earlier work this paper cites.
S. R. Bowman, L. Vilnis, O. Vinyals, A. M. Dai, R. Jozefowicz, and S. Bengio, “Generating sentences from a continuous space,” arXiv:1511.06349 , 2015
Original
2015
Earlier work this paper cites.
A. Makhzani, J. Shlens, N. Jaitly, and I. J. Goodfellow, “Adversarial autoencoders,” CoRR , vol. abs/1511.05644, 2015. [Online]. Available: http://arxiv.org/abs/1511.05644
Original
2015
Earlier work this paper cites.
C. Louizos, K. Swersky, Y. Li, M. Welling, and R. Zemel, “The variational fair autoencoder,” arXiv preprint arXiv:1511.00830 , 2015
Original
2015
Earlier work this paper cites.

Similar

T. Bolukbasi, K.-W. Chang, J. Y. Zou, V. Saligrama, and A. T. Kalai, “Man is to computer programmer as woman is to homemaker? debiasing word embeddings,” in Advances in neural information processing systems , 2016, pp. 4349–4357
2016
Cited alongside, same era.
J. Angwin, J. Larson, S. Mattu, and L. Kirchner, “Machine bias. ProPublica, May 23, 2016,” 2016
2016
Cited alongside, same era.
M. Hardt, E. Price, and N. Srebro, “Equality of opportunity in supervised learning,” in Advances in neural information processing systems , 2016, pp. 3315–3323
2016
Cited alongside, same era.
S. D. Team et al. , “Rstan: the r interface to stan,” R package version , vol. 2, no. 1, 2016
2016
Cited alongside, same era.
X. Chen, D. P. Kingma, T. Salimans, Y. Duan, P. Dhariwal, J. Schulman, I. Sutskever, and P. Abbeel, “Variational lossy autoencoder,” arXiv preprint arXiv:1611.02731 , 2016
Original

Then

J. H. Hinnefeld, P. Cooman, N. Mammo, and R. Deese, “Evaluating fairness metrics in the presence of dataset bias,” arXiv preprint arXiv:1809.09245 , 2018
Original
2018
Later among the works it cites.
B. H. Zhang, B. Lemoine, and M. Mitchell, “Mitigating unwanted biases with adversarial learning,” in AAAI’18 , 2018, pp. 335–340
2018
Later among the works it cites.
R. K. Bellamy, K. Dey, M. Hind, S. C. Hoffman, S. Houde, K. Kannan, P. Lohia, J. Martino, S. Mehta, A. Mojsilovic et al. , “Ai fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias,” arXiv preprint arXiv:1810.01943 , 2018
Original
2018
Later among the works it cites.
C. Wadsworth, F. Vera, and C. Piech, “Achieving fairness through adversarial learning: an application to recidivism prediction,” arXiv:1807.00199 , 2018
Original
2018
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

2016

Cited alongside, same era.

C. K. Sønderby, T. Raiko, L. Maaløe, S. K. Sønderby, and O. Winther, “Ladder variational autoencoders,” in NIPS’16 , 2016, pp. 3738–3746

2016

Cited alongside, same era.

M. J. Kusner, J. Loftus, C. Russell, and R. Silva, “Counterfactual fairness,” in Advances in Neural Information Processing Systems , 2017, pp. 4066–4076

2017

Cited alongside, same era.

F. P. Calmon, D. Wei, K. N. Ramamurthy, and K. R. Varshney, “Optimized data pre-processing for discrimination prevention,” arXiv preprint arXiv:1704.03354 , 2017

Original

2017

Cited alongside, same era.

G. Louppe, M. Kagan, and K. Cranmer, “Learning to pivot with adversarial networks,” in Advances in neural information processing systems , 2017, pp. 981–990

2017

Cited alongside, same era.

M. Kearns, S. Neel, A. Roth, and Z. S. Wu, “Preventing fairness gerrymandering: Auditing and learning for subgroup fairness,” arXiv preprint arXiv:1711.05144 , 2017

Original

2017

Cited alongside, same era.

C. Louizos, U. Shalit, J. M. Mooij, D. Sontag, R. Zemel, and M. Welling, “Causal effect inference with deep latent-variable models,” in Advances in Neural Information Processing Systems , 2017, pp. 6446–6456

2017

Cited alongside, same era.

C. Russell, M. J. Kusner, J. Loftus, and R. Silva, “When worlds collide: integrating different counterfactual assumptions in fairness,” in Advances in Neural Information Processing Systems , 2017, pp. 6414–6423

2017

Cited alongside, same era.

S. Chiappa, “Path-specific counterfactual fairness,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 7801–7808

2019