Discrimination-free Insurance Pricing with Privatized Sensitive Attributes

• URL: http://arxiv.org/abs/2504.11775v1
• Date: Wed, 16 Apr 2025 05:29:11 GMT
• Title: Discrimination-free Insurance Pricing with Privatized Sensitive Attributes
• Authors: Tianhe Zhang, Suhan Liu, Peng Shi,
• Abstract summary: Insurance is one sector that merits attention in this regard.<n> achieving fairness according to established notions does not automatically ensure fair pricing in insurance.<n>We propose an efficient method for constructing fair models that are tailored to the insurance domain.
• Score: 13.511945406946602
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fairness has emerged as a critical consideration in the landscape of machine learning algorithms, particularly as AI continues to transform decision-making across societal domains. To ensure that these algorithms are free from bias and do not discriminate against individuals based on sensitive attributes such as gender and race, the field of algorithmic bias has introduced various fairness concepts, along with methodologies to achieve these notions in different contexts. Despite the rapid advancement, not all sectors have embraced these fairness principles to the same extent. One specific sector that merits attention in this regard is insurance. Within the realm of insurance pricing, fairness is defined through a distinct and specialized framework. Consequently, achieving fairness according to established notions does not automatically ensure fair pricing in insurance. In particular, regulators are increasingly emphasizing transparency in pricing algorithms and imposing constraints on insurance companies on the collection and utilization of sensitive consumer attributes. These factors present additional challenges in the implementation of fairness in pricing algorithms. To address these complexities and comply with regulatory demands, we propose an efficient method for constructing fair models that are tailored to the insurance domain, using only privatized sensitive attributes. Notably, our approach ensures statistical guarantees, does not require direct access to sensitive attributes, and adapts to varying transparency requirements, addressing regulatory demands while ensuring fairness in insurance pricing.

Related papers

• Understanding Accuracy-Fairness Trade-offs in Re-ranking through Elasticity in Economics [96.68144350976637]
  Fairness is an increasingly important factor in re-ranking tasks. The accuracy-fairness trade-off parallels the coupling of the commodity tax transfer process. We introduce the Elastic Fairness Curve (EF-Curve) as an evaluation framework. We also propose ElasticRank, a fair re-ranking algorithm that employs elasticity calculations to adjust inter-item distances.
  arXiv  Detail & Related papers  (2025-04-21T09:41:08Z)
• Peer-induced Fairness: A Causal Approach for Algorithmic Fairness Auditing [0.0]
  The European Union's Artificial Intelligence Act takes effect on 1 August 2024. High-risk AI applications must adhere to stringent transparency and fairness standards. We propose a novel framework, which combines the strengths of counterfactual fairness and peer comparison strategy.
  arXiv  Detail & Related papers  (2024-08-05T15:35:34Z)
• Measuring and Mitigating Biases in Motor Insurance Pricing [1.2289361708127877]
  The non-life insurance sector operates within a highly competitive and tightly regulated framework. Age-based premium fairness is also mandated in certain insurance domains. In certain insurance domains, variables such as the presence of serious illnesses or disabilities are emerging as new dimensions for evaluating fairness.
  arXiv  Detail & Related papers  (2023-11-20T16:34:48Z)
• AI and ethics in insurance: a new solution to mitigate proxy discrimination in risk modeling [0.0]
  Driven by the growing attention of regulators on the ethical use of data in insurance, the actuarial community must rethink pricing and risk selection practices. Equity is a philosophy concept that has many different definitions in every jurisdiction that influence each other without currently reaching consensus. We propose an innovative method, not yet met in the literature, to reduce the risks of indirect discrimination thanks to mathematical concepts of linear algebra.
  arXiv  Detail & Related papers  (2023-07-25T16:20:56Z)
• Causal Fairness for Outcome Control [68.12191782657437]
  We study a specific decision-making task called outcome control in which an automated system aims to optimize an outcome variable $Y$ while being fair and equitable. In this paper, we first analyze through causal lenses the notion of benefit, which captures how much a specific individual would benefit from a positive decision. We then note that the benefit itself may be influenced by the protected attribute, and propose causal tools which can be used to analyze this.
  arXiv  Detail & Related papers  (2023-06-08T09:31:18Z)
• Group Fairness with Uncertainty in Sensitive Attributes [34.608332397776245]
  A fair predictive model is crucial to mitigate biased decisions against minority groups in high-stakes applications. We propose a bootstrap-based algorithm that achieves the target level of fairness despite the uncertainty in sensitive attributes. Our algorithm is applicable to both discrete and continuous sensitive attributes and is effective in real-world classification and regression tasks.
  arXiv  Detail & Related papers  (2023-02-16T04:33:00Z)
• Fairness in Matching under Uncertainty [78.39459690570531]
  algorithmic two-sided marketplaces have drawn attention to the issue of fairness in such settings. We axiomatize a notion of individual fairness in the two-sided marketplace setting which respects the uncertainty in the merits. We design a linear programming framework to find fair utility-maximizing distributions over allocations.
  arXiv  Detail & Related papers  (2023-02-08T00:30:32Z)
• Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes [70.6326967720747]
  It is important to guarantee that machine learning algorithms deployed in the real world do not result in unfairness or unintended social consequences. We introduce FairCOCCO, a fairness measure built on cross-covariance operators on reproducing kernel Hilbert Spaces. We empirically demonstrate consistent improvements against state-of-the-art techniques in balancing predictive power and fairness on real-world datasets.
  arXiv  Detail & Related papers  (2022-11-11T11:28:46Z)
• A Discussion of Discrimination and Fairness in Insurance Pricing [0.0]
  Group fairness concepts are proposed to'smooth out' the impact of protected characteristics in the calculation of insurance prices. We present a statistical model that is free of proxy discrimination, thus, unproblematic from an insurance pricing point of view. We find that the canonical price in this statistical model does not satisfy any of the three most popular group fairness axioms.
  arXiv  Detail & Related papers  (2022-09-02T07:31:37Z)
• 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)
• VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback [104.06766271716774]
  We study a multi-round welfare-maximising mechanism design problem in instances where agents do not know their values. We first define three notions of regret for the welfare, the individual utilities of each agent and that of the mechanism. Our framework also provides flexibility to control the pricing scheme so as to trade-off between the agent and seller regrets.
  arXiv  Detail & Related papers  (2020-04-19T18:00:58Z)

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.