Exploring the Unfairness of DP-SGD Across Settings

Abstract

End users and regulators require private and fair artificial intelligence models, but previous work suggests these objectives may be at odds. We use the CivilComments to evaluate the impact of applying the {\em de facto} standard approach to privacy, DP-SGD, across several fairness metrics. We evaluate three implementations of DP-SGD: for dimensionality reduction (PCA), linear classification (logistic regression), and robust deep learning (Group-DRO). We establish a negative, logarithmic correlation between privacy and fairness in the case of linear classification and robust deep learning. DP-SGD had no significant impact on fairness for PCA, but upon inspection, also did not seem to lead to private representations.

arXiv comment6 pages, 3 figures, https://aaai-ppai22.github.io/

References in corpus (10)

• Deep Learning with Differential Privacy
• The Measure and Mismeasure of Fairness
• Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization
• Fairness Constraints: Mechanisms for Fair Classification
• WILDS: A Benchmark of in-the-Wild Distribution Shifts
• Fairness Without Demographics in Repeated Loss Minimization
• A General Approach to Adding Differential Privacy to Iterative Training Procedures
• Diffprivlib: The IBM Differential Privacy Library
• Fairness risk measures
• Assessing Fairness in Classification Parity of Machine Learning Models in Healthcare