Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey

• URL: http://arxiv.org/abs/2202.08187v1
• Date: Wed, 16 Feb 2022 16:50:23 GMT
• Title: Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey
• Authors: Ferdinando Fioretto, Cuong Tran, Pascal Van Hentenryck, Keyu Zhu
• Abstract summary: It reviews the conditions under which privacy and fairness may have aligned or contrasting goals. It analyzes how and why DP may exacerbate bias and unfairness in decision problems and learning tasks.
• Score: 50.90773979394264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper surveys recent work in the intersection of differential privacy (DP) and fairness. It reviews the conditions under which privacy and fairness may have aligned or contrasting goals, analyzes how and why DP may exacerbate bias and unfairness in decision problems and learning tasks, and describes available mitigation measures for the fairness issues arising in DP systems. The survey provides a unified understanding of the main challenges and potential risks arising when deploying privacy-preserving machine-learning or decisions-making tasks under a fairness lens.

Related papers

• Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning [72.80902932543474]
  Understanding human behavior from observed data is critical for transparency and accountability in decision-making. Consider real-world settings such as healthcare, in which modeling a decision-maker's policy is challenging. We propose a data-driven representation of decision-making behavior that inheres transparency by design, accommodates partial observability, and operates completely offline.
  arXiv  Detail & Related papers  (2023-10-28T13:06:14Z)
• Privacy and Fairness in Federated Learning: on the Perspective of Trade-off [58.204074436129716]
  Federated learning (FL) has been a hot topic in recent years. As two crucial ethical notions, the interactions between privacy and fairness are comparatively less studied.
  arXiv  Detail & Related papers  (2023-06-25T04:38:19Z)
• Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities [24.471814126358556]
  We compare and discuss work across two major subsets of this literature: algorithmic fairness, and ethical decision making. We explore how each of these settings has articulated its normative concerns, the viability of different techniques for these different settings, and how ideas from each setting may have utility for the other.
  arXiv  Detail & Related papers  (2023-01-13T20:33:14Z)
• Survey on Fairness Notions and Related Tensions [4.257210316104905]
  Automated decision systems are increasingly used to take consequential decisions in problems such as job hiring and loan granting. However, objective machine learning (ML) algorithms are prone to bias, which results in yet unfair decisions. This paper surveys the commonly used fairness notions and discusses the tensions among them with privacy and accuracy.
  arXiv  Detail & Related papers  (2022-09-16T13:36:05Z)
• Causal Fairness Analysis [68.12191782657437]
  We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings. The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms. Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
  arXiv  Detail & Related papers  (2022-07-23T01:06:34Z)
• Differentially Private Deep Learning under the Fairness Lens [34.28936739262812]
  Differential Privacy (DP) is an important privacy-enhancing technology for private machine learning systems. It allows to measure and bound the risk associated with an individual participation in a computation. It was recently observed that DP learning systems may exacerbate bias and unfairness for different groups of individuals.
  arXiv  Detail & Related papers  (2021-06-04T19:10:09Z)
• 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)
• A survey of algorithmic recourse: definitions, formulations, solutions, and prospects [24.615500469071183]
  We focus on algorithmic recourse, which is concerned with providing explanations and recommendations to individuals who are unfavourably treated by automated decision-making systems. We perform an extensive literature review, and align the efforts of many authors by presenting unified definitions, formulations, and solutions to recourse.
  arXiv  Detail & Related papers  (2020-10-08T15:15:34Z)
• Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach [54.32266555843765]
  This paper studies a model that protects the privacy of the individuals sensitive information while also allowing it to learn non-discriminatory predictors. The method relies on the notion of differential privacy and the use of Lagrangian duality to design neural networks that can accommodate fairness constraints.
  arXiv  Detail & Related papers  (2020-09-26T10:50:33Z)

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.