On the Compatibility of Privacy and Fairness

By Rachel Cummings, Varun Gupta, Dhamma Kimpara, Jamie H. Morgenstern. 2019

181 citations recorded by the source corpus.

In this work, we investigate whether privacy and fairness can be simultaneously achieved by a single classifier in several different models. Some of the earliest work on fairness in algorithm design defined fairness as a guarantee of similar outputs for "similar'' input data, a notion with tight technical connections to differential privacy. We study whether tensions exist between differential privacy and statistical notions of fairness, namely Equality of False Positives and Equality of False Negatives (EFP/EFN). We show that even under full distributional access, there are cases where the constraint of differential privacy precludes exact EFP/EFN. We then turn to ask whether one can learn a differentially private classifier which approximately satisfies EFP/EFN, and show the existence of a PAC learner which is private and approximately fair with high probability. We conclude by giving an efficient algorithm for classification that maintains utility and satisfies both privacy and approximate fairness with high probability.

Subfields: Artificial Intelligence, Safety Research, Privacy & Security, AI Ethics, Fairness & Society

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Citations in the corpus

Advances and Open Problems in Federated Learning (2019, background): “hip in sensitive groups in order to measure or ensure fair predictions are being made. While the trade-off between differential privacy and fairness has been investigated in the non-federated setting [214, 127], there has been little work on how (or whether) FL may be able to uniquely address concer”
Differential Privacy Has Disparate Impact on Model Accuracy (2019, background): “Models that generate artiﬁcial data points [11] from the existing data are incompatible with DP. Recent research [10, 20] aims to add fairness and DP to post-processing [17] and in-processing [2] algorithms.”
Randomness In Neural Network Training: Characterizing The Impact of Tooling (2021, background): “, 2020) and privacy (Cummings et al., 2019) can impact disparate impact on sensitive attributes.”
Towards Fair and Privacy-Preserving Federated Deep Models (2019, background): “Existing research on fairness mostly focuses on protecting sensitive attributes or reducing the variance of the prediction distribution across participants [6], [7].”
A Review on Fairness in Machine Learning (2022, background): “Most of these studies have investigated the case of a centralized setting, in which all the dataset is held by a single party [10, 52, 100, 106, 153, 210].”