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Algorithmic fairness, the research field of making machine learning (ML) algorithms fair, is an established area in ML.
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2020
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2020
Cited alongside, same era.
Xuejian Wang, Wenbin Zhang, Aishwarya Jadhav, and Jeremy Weiss, ‘Harmonic-mean cox models: A ruler for equal attention to risk’, in Survival Prediction-Algorithms, Challenges and Applications
2021
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2021
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Wenbin Zhang and Jeremy Weiss, ‘Fair decision-making under uncertainty’, in 2021 IEEE International ICDM
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Wenbin Zhang, Liming Zhang, Dieter Pfoser, and Liang Zhao, ‘Disentangled dynamic graph deep generation’, in Proceedings of the SIAM International Conference on Data Mining (SDM)
2021
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2022
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Wenbin Zhang and Jeremy Weiss, ‘Longitudinal fairness with censorship’, in Proceedings of the AAAI Conference
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Nripsuta Ani Saxena, Wenbin Zhang, and Cyrus Shahabi, ‘Missed opportunities in fair ai’, in Proceedings of the 2023 SIAM International Conference on Data Mining (SDM)
2023
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Zichong Wang, Giri Narasimhan, Xin Yao, and Wenbin Zhang, ‘Mitigating multisource biases in graph neural networks via real counterfactual samples’, in Proceedings of the 23rd IEEE International Conference on Data Mining (ICDM)
2023
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Zichong Wang, Charles Wallace, Albert Bifet, Xin Yao, and Wenbin Zhang, ‘FG 2 AN: Fairness-aware graph generative adversarial networks’, in Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)
2023
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2023
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Wenbin Zhang, Tina Hernandez-Boussard, and Jeremy Weiss, ‘Censored fairness through awareness’, in Proceedings of the AAAI conference on artificial intelligence
2023
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Wenbin Zhang and Jeremy C Weiss, ‘Fairness with censorship and group constraints’, Knowledge and Information Systems
2023
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