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

• URL: http://arxiv.org/abs/2408.08471v1
• Date: Fri, 16 Aug 2024 01:13:36 GMT
• Title: Fairness Issues and Mitigations in (Differentially Private) Socio-demographic Data Processes
• Authors: Joonhyuk Ko, Juba Ziani, Saswat Das, Matt Williams, Ferdinando Fioretto,
• Abstract summary: This paper shows that surveys of important societal relevance introduce sampling errors that unevenly impact group-level estimates. To address these issues, this paper introduces an optimization approach modeled on real-world survey design processes. Privacy-preserving methods used to determine sampling rates can further impact these fairness issues.
• Score: 43.07159967207698
• License: http://creativecommons.org/licenses/by/4.0/
• 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. The paper explores the impact of differential privacy on the statistics informing the sampling process, revealing a surprising effect: not only the expected negative effect from the addition of noise for differential privacy is 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.

Related papers

• A Systematic and Formal Study of the Impact of Local Differential Privacy on Fairness: Preliminary Results [5.618541935188389]
  Differential privacy (DP) is the predominant solution for privacy-preserving Machine learning (ML) algorithms. Recent experimental studies have shown that local DP can impact ML prediction for different subgroups of individuals. We study how the fairness of the decisions made by the ML model changes under local DP for different levels of privacy and data distributions.
  arXiv  Detail & Related papers  (2024-05-23T15:54:03Z)
• Lazy Data Practices Harm Fairness Research [49.02318458244464]
  We present a comprehensive analysis of fair ML datasets, demonstrating how unreflective practices hinder the reach and reliability of algorithmic fairness findings. Our analyses identify three main areas of concern: (1) a textbflack of representation for certain protected attributes in both data and evaluations; (2) the widespread textbf of minorities during data preprocessing; and (3) textbfopaque data processing threatening the generalization of fairness research. This study underscores the need for a critical reevaluation of data practices in fair ML and offers directions to improve both the sourcing and usage of datasets.
  arXiv  Detail & Related papers  (2024-04-26T09:51:24Z)
• Post-processing of Differentially Private Data: A Fairness Perspective [53.29035917495491]
  This paper shows that post-processing causes disparate impacts on individuals or groups. It analyzes two critical settings: the release of differentially private datasets and the use of such private datasets for downstream decisions. It proposes a novel post-processing mechanism that is (approximately) optimal under different fairness metrics.
  arXiv  Detail & Related papers  (2022-01-24T02:45:03Z)
• Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach [131.20444904674494]
  We tackle the problem of measuring group fairness under unawareness of sensitive attributes. We show that quantification approaches are particularly suited to tackle the fairness-under-unawareness problem.
  arXiv  Detail & Related papers  (2021-09-17T13:45:46Z)
• Non-parametric Differentially Private Confidence Intervals for the Median [3.205141100055992]
  This paper proposes and evaluates several strategies to compute valid differentially private confidence intervals for the median. We also illustrate that addressing both sources of uncertainty--the error from sampling and the error from protecting the output--should be preferred over simpler approaches that incorporate the uncertainty in a sequential fashion.
  arXiv  Detail & Related papers  (2021-06-18T19:45:37Z)
• Decision Making with Differential Privacy under a Fairness Lens [65.16089054531395]
  The U.S. Census Bureau releases data sets and statistics about groups of individuals that are used as input to a number of critical decision processes. To conform to privacy and confidentiality requirements, these agencies are often required to release privacy-preserving versions of the data. This paper studies the release of differentially private data sets and analyzes their impact on some critical resource allocation tasks under a fairness perspective.
  arXiv  Detail & Related papers  (2021-05-16T21:04:19Z)
• RDP-GAN: A R\'enyi-Differential Privacy based Generative Adversarial Network [75.81653258081435]
  Generative adversarial network (GAN) has attracted increasing attention recently owing to its impressive ability to generate realistic samples with high privacy protection. However, when GANs are applied on sensitive or private training examples, such as medical or financial records, it is still probable to divulge individuals' sensitive and private information. We propose a R'enyi-differentially private-GAN (RDP-GAN), which achieves differential privacy (DP) in a GAN by carefully adding random noises on the value of the loss function during training.
  arXiv  Detail & Related papers  (2020-07-04T09:51:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.