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Controlling bias in training datasets is vital for ensuring equal treatment, or parity, between different groups in downstream applications.
A Survey on Bias and Fairness in Machine Learning
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Mitigating Information Leakage in Image Representations: A Maximum Entropy Approach
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Inherent Trade-Offs in the Fair Determination of Risk Scores
Kleinberg, J.; Mullainathan, S.; and Raghavan, M. 2017 · 2017
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McNamara, D.; Ong, C. S.; and Williamson, R. C. 2017 · 2017
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Controllable Invariance through Adversarial Feature Learning
Xie, Q.; Dai, Z.; Du, Y.; Hovy, E.; and Neubig, G. 2017 · 2017
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Fairness Constraints: Mechanisms for Fair Classification
Zafar, M. B.; Valera, I.; Rogriguez, M. G.; and Gummadi, K. P. 2017 · 2017
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Mutual Information Neural Estimation
Belghazi, M. I.; Baratin, A.; Rajeshwar, S.; Ozair, S.; Bengio, Y.; Courville, A.; and Hjelm, D. 2018 · 2018
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Learning Adversarially Fair and Transferable Representations
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Roy, P. C.; and Boddeti, V. N. 2019 · 2019
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Average Individual Fairness: Algorithms, Generalization and Experiments
Sharifi-Malvajerdi, S.; Kearns, M.; and Roth, A. 2019 · 2019
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Learning Controllable Fair Representations
Song, J.; Kalluri, P.; Grover, A.; Zhao, S.; and Ermon, S. 2019 · 2019
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Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning
Srivastava, M.; Heidari, H.; and Krause, A. 2019 · 2019
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Deep Graph Infomax
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Fairlearn: A toolkit for assessing and improving fairness in AI
Bird, S.; Dudík, M.; Edgar, R.; Horn, B.; Lutz, R.; Milan, V.; Sameki, M.; Wallach, H.; and Walker, K. 2020 · 2020
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A Geometric Solution to Fair Representations
He, Y.; Burghardt, K.; and Lerman, K. 2020 · 2020
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Invariant Representations through Adversarial Forgetting
Jaiswal, A.; Moyer, D.; Ver Steeg, G.; AbdAlmageed, W.; and Natarajan, P. 2020 · 2020
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GNU Parallel 20200522 (’Kraftwerk’)
Tange, Ole. 2020 · 2020
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A Theory of Usable Information under Computational Constraints
Xu, Y.; Zhao, S.; Song, J.; Stewart, R.; and Ermon, S. 2020 · 2020
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