# A Discussion of Discrimination and Fairness in Insurance Pricing

arXiv: https://arxiv.org/abs/2209.00858 · HTML: https://mlsanity.com/paper/2209.00858/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 2 Sep 2022.

## Builds on (most cited first, 7 of 7)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Fair Regression: Quantitative Definitions and Reduction-based Algorithms](https://mlsanity.com/paper/1905.12843/) (2019)
- [A Fair Pricing Model via Adversarial Learning](https://mlsanity.com/paper/2202.12008/) (2022)
- [Model selection with Gini indices under auto-calibration](https://mlsanity.com/paper/2207.14372/) (2022)
- [A multi-task network approach for calculating discrimination-free insurance prices](https://mlsanity.com/paper/2207.02799/) (2022)

## Built on by (most cited first, 4 of 4)

- [Measuring and Mitigating Biases in Motor Insurance Pricing](https://mlsanity.com/paper/2311.11900/) (2023)
- [Discrimination-free Insurance Pricing with Privatized Sensitive Attributes](https://mlsanity.com/paper/2504.11775/) (2025)
- [OptiGrad: A Fair and more Efficient Price Elasticity Optimization via a Gradient Based Learning](https://mlsanity.com/paper/2404.10275/) (2024)
- [When fairness metrics fail: A utility-based perspective on varepsilon-fairness](https://mlsanity.com/paper/2405.09360/) (2024)

## Abstract (arXiv)

Indirect discrimination is an issue of major concern in algorithmic models. This is particularly the case in insurance pricing where protected policyholder characteristics are not allowed to be used for insurance pricing. Simply disregarding protected policyholder information is not an appropriate solution because this still allows for the possibility of inferring the protected characteristics from the non-protected ones. This leads to so-called proxy or indirect discrimination. Though proxy discrimination is qualitatively different from the group fairness concepts in machine learning, these group fairness concepts are proposed to 'smooth out' the impact of protected characteristics in the calculation of insurance prices. The purpose of this note is to share some thoughts about group fairness concepts in the light of insurance pricing and to discuss their implications. We present a statistical model that is free of proxy discrimination, thus, unproblematic from an insurance pricing point of view. However, we find that the canonical price in this statistical model does not satisfy any of the three most popular group fairness axioms. This seems puzzling and we welcome feedback on our example and on the usefulness of these group fairness axioms for non-discriminatory insurance pricing.