---
title: "Decision Making with Differential Privacy under a Fairness Lens"
authors:
  - "Cuong Tran"
  - "Ferdinando Fioretto"
  - "Pascal Van Hentenryck"
  - "Zhiyan Yao"
doi: "10.24963/ijcai.2021/78"
url: "https://ijcai.org/proceedings/2021/78"
pdf: "https://ijcai.org/proceedings/2021/0078.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "560-566"
track: "Main Track"
keywords:
  - "Machine Learning: Classification"
  - "Data Mining: Privacy Preserving Data Mining"
  - "Multidisciplinary Topics and Applications: Security and Privacy"
  - "AI Ethics, Trust, Fairness: Fairness"
---

# Decision Making with Differential Privacy under a Fairness Lens

Cuong Tran, Ferdinando Fioretto, Pascal Van Hentenryck, Zhiyan Yao

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 560-566. DOI: [10.24963/ijcai.2021/78](https://doi.org/10.24963/ijcai.2021/78)

## Abstract

Many agencies release datasets and statistics about groups of individuals that are used as input to a number of critical decision processes. To conform with 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 datasets and analyzes their impact on some critical resource allocation tasks under a fairness perspective.  The paper shows that, when the decisions take as input differentially private data, the noise added to achieve privacy disproportionately impacts some groups over others. The paper analyzes the reasons for these disproportionate impacts and proposes guidelines to mitigate these effects. The proposed approaches are evaluated on critical decision problems that use differentially private census data.