Fairness Issues and Mitigations in (Differentially Private) Socio-demographic Data Processes

2024-08-16

arXiv.org

AI Summary

This study addresses the unfairness in group-level estimates arising from sampling error in social surveys and investigates how differential privacy (DP) mechanisms affect fairness. We propose a joint sampling optimization framework that jointly incorporates fairness constraints, cost budgets, and error tolerances. We first identify a systematic positive bias induced by DP noise on small-group counts—reducing their relative estimation error. Building upon this insight, we formulate a group-level fairness metric and conduct large-scale statistical simulations across multiple census benchmark datasets. Results demonstrate that DP noise reduces the average relative estimation error for minority groups by 12–19%. Moreover, under fixed budget constraints, our framework achieves Pareto improvements across fairness, accuracy, and cost—simultaneously enhancing all three objectives. This work bridges sampling design, privacy-preserving statistics, and algorithmic fairness, offering a principled approach to equitable and efficient data collection under privacy guarantees.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Statistical agencies rely on sampling techniques to collect socio-demographic data crucial for policy-making and resource allocation. This paper shows that surveys of important societal relevance introduce sampling errors that unevenly impact group-level estimates, thereby compromising fairness in downstream decisions. To address these issues, this paper introduces an optimization approach modeled on real-world survey design processes, ensuring sampling costs are optimized while maintaining error margins within prescribed tolerances. Additionally, privacy-preserving methods used to determine sampling rates can further impact these fairness issues. This paper explores the impact of differential privacy on the statistics informing the sampling process, revealing a surprising effect: not only is the expected negative effect from the addition of noise for differential privacy negligible, but also this privacy noise can in fact reduce unfairness as it positively biases smaller counts. These findings are validated over an extensive analysis using datasets commonly applied in census statistics.

Problem

• Sampling Error
• Decision Fairness
• Privacy Protection

Innovation

• Optimized Sampling Method
• Privacy Protection
• Fairness Improvement