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Conference Paper

Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers

MPS-Authors
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Zietlow,  Dominik
Max Planck Research Group Autonomous Learning, Max Planck Institute for Intelligent Systems, Max Planck Society;
External Organizations;

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Schölkopf,  Bernhard       
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;
External Organizations;

External Resource

https://doi.org/10.1109/CVPR52688.2022.01016
(Publisher version)

https://doi.org/10.48550/arXiv.2203.04913
(Preprint)

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Citation

Zietlow, D., Lohaus, M., Balakrishnan, G., Kleindessner, M., Locatello, F., Schölkopf, B., et al. (2023). Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022) (pp. 10400-10411). Piscataway, NJ: IEEE. doi:10.1109/CVPR52688.2022.01016.


Cite as: https://hdl.handle.net/21.11116/0000-0010-3034-D
Abstract
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