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Fairness-aware learning is increasingly important in data mining.
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Certifying and removing disparate impact. In KDD
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The Variational Fair Autoencoder. In ICLR
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard Zemel. 2016 · 2016
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Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations. In FAT/ML
Alex Beutel, Jilin Chen, Zhe Zhao, and Ed H. Chi. 2017 · 2017
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Fairness in Machine Learning: Lessons from Political Philosophy
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Generating Multi-label Discrete Patient Records using Generative Adversarial Networks. In MLHC
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UCI Machine Learning Repository
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Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai. 2016 · 2016
Cited alongside, same era.
Equality of Opportunity in Supervised Learning. In NIPS
Moritz Hardt, Eric Price, and Nathan Srebro. 2016 · 2016
Cited alongside, same era.
Fairness in Learning: Classic and Contextual Bandits. In NIPS
Matthew Joseph, Michael Kearns, Jamie H Morgenstern, and Aaron Roth. 2016 · 2016
Cited alongside, same era.
Achieving Non-Discrimination in Data Release. In KDD . 1335–1344
Lu Zhang, Yongkai Wu, and Xintao Wu. 2017a
Cited in the paper.
A Causal Framework for Discovering and Removing Direct and Indirect Discrimination (IJCAI’17) . 3929–3935
Lu Zhang, Yongkai Wu, and Xintao Wu. 2017b
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Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P. Gummadi. 2017 · 2017
Later among the works it cites.
Learning Adversarially Fair and Transferable Representations
David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel. 2018 · 2018
Closest in time.
Mitigating Unwanted Biases with Adversarial Learning. In AAAI Conference on AI, Ethics and Society
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell. 2018 · 2018
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