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Bias in AI/ML-based systems is a ubiquitous problem and bias in AI/ML systems may negatively impact society.
Adam Kortylewski,Bernhard Egger, Andreas Schneider, Thomas Gerig Andreas, Morel-Forster, Thomas Vetter. Analyzing and Reducing the Damage of Dataset Bias to Face Recognition with Synthetic Data, 2017
2017
Earlier work this paper cites.
Brian Hu Zhang, Blake Lemoine, Margaret Mitchell. Mitigating Unwanted Biases with Adversarial Learning, 2018
2018
Earlier work this paper cites.
Alexander Amini, Wilko Schwarting, Guy Rosman, Brandon Araki, Sertac Karaman,Daniela Rus. Variational Autoencoder for End-to-End Control of Autonomous Driving with Novelty Detection and Training De-biasing, 2018
2018
Earlier work this paper cites.
Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, Vicente Ordonez. Balanced Datasets Are Not Enough:Estimating and Mitigating Gender Bias in Deep Image Representations, 2019
2019
Cited alongside, same era.
Alexander Amini, Ava P. Soleimany, Wilko Schwarting, Sangeeta N. Bhatia, Daniela Rus. Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure, Proceedings of the 2019 AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES) 2019
2019
Cited alongside, same era.
Sixue Gong, Xiaoming Liu, Anil K.Jain. DebFace: De-biasing Face Recognition, 2019
2019
Cited alongside, same era.
Vikram V. Ramaswamy, Sunnie S. Y. Kim, Olga Russakovsky. Fair Attribute Classification through Latent Space De-biasing, 2021
2021
Later among the works it cites.
Puspita Majumdar, Richa Singh, Mayank Vatsa. Attention Aware Debiasing for Unbiased Model Prediction, 2021
2021
Later among the works it cites.
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