Fair Decision Making using Privacy-Protected Data

Abstract

Data collected about individuals is regularly used to make decisions that impact those same individuals. We consider settings where sensitive personal data is used to decide who will receive resources or benefits. While it is well known that there is a tradeoff between protecting privacy and the accuracy of decisions, we initiate a first-of-its-kind study into the impact of formally private mechanisms (based on differential privacy) on fair and equitable decision-making. We empirically investigate novel tradeoffs on two real-world decisions made using U.S. Census data (allocation of federal funds and assignment of voting rights benefits) as well as a classic apportionment problem. Our results show that if decisions are made using an $\epsilon$-differentially private version of the data, under strict privacy constraints (smaller $\epsilon$), the noise added to achieve privacy may disproportionately impact some groups over others. We propose novel measures of fairness in the context of randomized differentially private algorithms and identify a range of causes of outcome disparities.

arXiv comment12 pages, 4 figures

References in corpus (4)

• An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices
• Differential Privacy Has Disparate Impact on Model Accuracy
• Differentially Private Bayesian Linear Regression
• Differentially Private Bayesian Inference for Exponential Families

Cited by in corpus (6)

• Differential Privacy Has Disparate Impact on Model Accuracy
• "What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairness
• Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data
• Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy
• On the Privacy Risks of Algorithmic Fairness
• Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence