- Conference Paper
As machine learning algorithms are deployed on sensitive data in critical decision making processes, it is becoming increasingly important that they are also private and fair. In this paper, we show that, when the data has a long-tailed structure, it is not possible to build accurate learning algorithms that are both private and results in higher accuracy on minority subpopulations. We further show that relaxing overall accuracy can lead to good fairness even with strict privacy requirements. To corroborate our theoretical results in practice, we provide an extensive set of experimental results using a variety of synthetic, vision (CIFAR-10 and CelebA), and tabular (Law School) datasets and learning algorithms. Show more
Book titleProceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022)
Journal / seriesProceedings of Machine Learning Research
Pages / Article No.
Organisational unit09652 - Yang, Fan / Yang, Fan
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