# Mitigating Discrimination in Insurance with Wasserstein Barycenters

arXiv: https://arxiv.org/abs/2306.12912 · HTML: https://mlsanity.com/paper/2306.12912/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 52 references that are in the map.
- First posted on arXiv 22 Jun 2023.

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

- [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020)
- [Fairness in Machine Learning](https://mlsanity.com/paper/2012.15816/) (2020)
- [Wasserstein Fair Classification](https://mlsanity.com/paper/1907.12059/) (2019)
- [Fair Regression with Wasserstein Barycenters](https://mlsanity.com/paper/2006.07286/) (2020)
- [Fr'echet Regression for Random Objects with Euclidean Predictors](https://mlsanity.com/paper/1608.03012/) (2016)
- [Fairness in Multi-Task Learning via Wasserstein Barycenters](https://mlsanity.com/paper/2306.10155/) (2023)
- [The Fairness of Machine Learning in Insurance: New Rags for an Old Man?](https://mlsanity.com/paper/2205.08112/) (2022)

## Built on by (most cited first, 7 of 7)

- [A Sequentially Fair Mechanism for Multiple Sensitive Attributes](https://mlsanity.com/paper/2309.06627/) (2023)
- [Measuring and Mitigating Biases in Motor Insurance Pricing](https://mlsanity.com/paper/2311.11900/) (2023)
- [Geospatial Disparities: A Case Study on Real Estate Prices in Paris](https://mlsanity.com/paper/2401.16197/) (2024)
- [Federated Calculation of the Free-Support Transportation Barycenter by Single-Loop Dual Decomposition](https://mlsanity.com/paper/2507.19627/) (2025)
- [AIFS-SUBS: Extending Data-Driven Forecasting to Sub-Seasonal Timescales](https://mlsanity.com/paper/2607.05100/) (2026)
- [Fairness Explainability using Optimal Transport with Applications in Image Classification](https://mlsanity.com/paper/2308.11090/) (2023)
- [Fair Dataset Distillation via Cross-Group Barycenter Alignment](https://mlsanity.com/paper/2605.00185/) (2026)

## Abstract (arXiv)

The insurance industry is heavily reliant on predictions of risks based on characteristics of potential customers. Although the use of said models is common, researchers have long pointed out that such practices perpetuate discrimination based on sensitive features such as gender or race. Given that such discrimination can often be attributed to historical data biases, an elimination or at least mitigation is desirable. With the shift from more traditional models to machine-learning based predictions, calls for greater mitigation have grown anew, as simply excluding sensitive variables in the pricing process can be shown to be ineffective. In this article, we first investigate why predictions are a necessity within the industry and why correcting biases is not as straightforward as simply identifying a sensitive variable. We then propose to ease the biases through the use of Wasserstein barycenters instead of simple scaling. To demonstrate the effects and effectiveness of the approach we employ it on real data and discuss its implications.