Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey
Abstract
This paper surveys the recent work in the intersection of differential privacy (DP) and fairness. It focuses on surveying the work observing that DP systems may exacerbate bias and disparate impacts for different groups of individuals. The survey reviews the conditions under which privacy and fairness may be aligned or contrasting goals, analyzes how and why DP exacerbates bias and unfairness in decision problems and learning tasks, and reviews the available solutions to mitigate the fairness issues arising in DP systems. The survey provides a unified understanding of the main challenges and potential risks arising when deploying privacy-preserving machine learning or decisions making tasks under a fairness lens.
Cite
Text
Fioretto et al. "Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/766Markdown
[Fioretto et al. "Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/fioretto2022ijcai-differential/) doi:10.24963/IJCAI.2022/766BibTeX
@inproceedings{fioretto2022ijcai-differential,
title = {{Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey}},
author = {Fioretto, Ferdinando and Tran, Cuong and Van Hentenryck, Pascal and Zhu, Keyu},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {5470-5477},
doi = {10.24963/IJCAI.2022/766},
url = {https://mlanthology.org/ijcai/2022/fioretto2022ijcai-differential/}
}