# Insights From Insurance for Fair Machine Learning

arXiv: https://arxiv.org/abs/2306.14624 · HTML: https://mlsanity.com/paper/2306.14624/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 159 references that are in the map.
- First posted on arXiv 26 Jun 2023.

## Builds on (most cited first, 22 of 22)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Performative Prediction](https://mlsanity.com/paper/2002.06673/) (2020)
- [Avoiding Discrimination through Causal Reasoning](https://mlsanity.com/paper/1706.02744/) (2017)
- [Delayed Impact of Fair Machine Learning](https://mlsanity.com/paper/1803.04383/) (2018)
- [Fairness in Learning: Classic and Contextual Bandits](https://mlsanity.com/paper/1605.07139/) (2016)
- [On the Apparent Conflict Between Individual and Group Fairness](https://mlsanity.com/paper/1912.06883/) (2019)
- [50 Years of Test (Un)fairness: Lessons for Machine Learning](https://mlsanity.com/paper/1811.10104/) (2018)
- [A Clarification of the Nuances in the Fairness Metrics Landscape](https://mlsanity.com/paper/2106.00467/) (2021)
- [A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunity](https://mlsanity.com/paper/1809.03400/) (2018)
- [A Short-term Intervention for Long-term Fairness in the Labor Market](https://mlsanity.com/paper/1712.00064/) (2017)
- [Performative Power](https://mlsanity.com/paper/2203.17232/) (2022)
- [What's Sex Got To Do With Fair Machine Learning?](https://mlsanity.com/paper/2006.01770/) (2020)
- [Algorithmic Collective Action in Machine Learning](https://mlsanity.com/paper/2302.04262/) (2023)
- [Group Fairness: Independence Revisited](https://mlsanity.com/paper/2101.02968/) (2021)
- [Autocalibration and Tweedie-dominance for Insurance Pricing with Machine Learning](https://mlsanity.com/paper/2103.03635/) (2021)
- [Algorithmic Fairness and Structural Injustice: Insights from Feminist Political Philosophy](https://mlsanity.com/paper/2206.00945/) (2022)
- [The Long Arc of Fairness: Formalisations and Ethical Discourse](https://mlsanity.com/paper/2203.06038/) (2022)
- [Distributive Justice and Fairness Metrics in Automated Decision-making: How Much Overlap Is There?](https://mlsanity.com/paper/2105.01441/) (2021)
- [On the Richness of Calibration](https://mlsanity.com/paper/2302.04118/) (2023)
- [Better Together? How Externalities of Size Complicate Notions of Solidarity and Actuarial Fairness](https://mlsanity.com/paper/2103.00347/) (2021)
- [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, 2 of 2)

- [Scoring Rules and Calibration for Imprecise Probabilities](https://mlsanity.com/paper/2410.23001/) (2024)
- [A Unifying Perspective on Probabilities as Model Predictions](https://mlsanity.com/paper/2609.09855/) (2026)

## Abstract (arXiv)

We argue that insurance can act as an analogon for the social situatedness of machine learning systems, hence allowing machine learning scholars to take insights from the rich and interdisciplinary insurance literature. Tracing the interaction of uncertainty, fairness and responsibility in insurance provides a fresh perspective on fairness in machine learning. We link insurance fairness conceptions to their machine learning relatives, and use this bridge to problematize fairness as calibration. In this process, we bring to the forefront two themes that have been largely overlooked in the machine learning literature: responsibility and aggregate-individual tensions.