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

arXiv: https://arxiv.org/abs/2408.08471 · HTML: https://mlsanity.com/paper/2408.08471/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 16 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [Fair Decision Making using Privacy-Protected Data](https://mlsanity.com/paper/1905.12744/) (2019)
- [Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey](https://mlsanity.com/paper/2202.08187/) (2022)
- [Decision Making with Differential Privacy under a Fairness Lens](https://mlsanity.com/paper/2105.07513/) (2021)
- [Post-processing of Differentially Private Data: A Fairness Perspective](https://mlsanity.com/paper/2201.09425/) (2022)

## Built on by (most cited first, 2 of 2)

- [Optimal Allocation of Privacy Budget on Hierarchical Data Release](https://mlsanity.com/paper/2505.10871/) (2025)
- [Optimal Fairness under Local Differential Privacy](https://mlsanity.com/paper/2511.16377/) (2025)

## Abstract (arXiv)

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.