# Algorithmic Insurance

arXiv: https://arxiv.org/abs/2106.00839 · HTML: https://mlsanity.com/paper/2106.00839/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 101 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 15 of 15)

- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks](https://mlsanity.com/paper/1612.03242/) (2016)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [NIPS 2016 Tutorial: Generative Adversarial Networks](https://mlsanity.com/paper/1701.00160/) (2016)
- [Pros and Cons of GAN Evaluation Measures](https://mlsanity.com/paper/1802.03446/) (2018)
- [Model-Agnostic Interpretability of Machine Learning](https://mlsanity.com/paper/1606.05386/) (2016)
- [Quantifying Interpretability and Trust in Machine Learning Systems](https://mlsanity.com/paper/1901.08558/) (2019)
- [Imputation of Clinical Covariates in Time Series](https://mlsanity.com/paper/1812.00418/) (2018)
- [The Price of Interpretability](https://mlsanity.com/paper/1907.03419/) (2019)
- [Algorithmic Transparency with Strategic Users](https://mlsanity.com/paper/2008.09283/) (2020)
- [Artificial Intelligence and Legal Liability](https://mlsanity.com/paper/1802.07782/) (2018)

## Built on by (most cited first, 5 of 5)

- [Insuring Every Action: An Authority Frontier Framework for Runtime Actuarial Control of Autonomous AI Agents](https://mlsanity.com/paper/2605.25632/) (2026)
- [Foundations of a Time-Consistent Counterfactual Actuarial Runtime for Autonomous AI Agents](https://mlsanity.com/paper/2605.26508/) (2026)
- [Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences](https://mlsanity.com/paper/2403.19871/) (2024)
- [Decision-Aware Predictive Model Selection for Workforce Allocation](https://mlsanity.com/paper/2410.07932/) (2024)
- [Gaming-Resistant Insurance Contracts for Autonomous AI Agents: Strategy-Proof Toll Mechanism Design](https://mlsanity.com/paper/2606.16326/) (2026)

## Abstract (arXiv)

When AI systems make errors in high-stakes domains like medical diagnosis or autonomous vehicles, a single algorithmic flaw across varying operational contexts can generate highly heterogeneous losses that challenge traditional insurance assumptions. Algorithmic insurance constitutes a novel form of financial coverage for AI-induced damages, representing an emerging market that addresses algorithm-driven liability. However, insurers currently struggle to price these risks, while AI developers lack rigorous frameworks connecting system design with financial liability exposure. We analyze the connection between operational choices of binary classification performance to tail risk exposure. Using conditional value-at-risk (CVaR) to capture extreme losses, we prove that established approaches like maximizing accuracy can significantly increase worst-case losses compared to tail risk optimization, with penalties growing quadratically as thresholds deviate from optimal. We then propose a liability insurance contract structure that mandates risk-aware classification thresholds and characterize the conditions under which it creates value for AI providers. Our analysis extends to degrading model performance and human oversight scenarios. We validate our findings through a mammography case study, demonstrating that CVaR-optimal thresholds reduce tail risk up to 13-fold compared to accuracy maximization. This risk reduction enables insurance contracts to create 14-16% gains for well-calibrated firms, while poorly calibrated firms benefit up to 65% through risk transfer, mandatory recalibration, and regulatory capital relief. Unlike traditional insurance that merely transfers risk, algorithmic insurance can function as both a financial instrument and an operational governance mechanism, simultaneously enabling efficient risk transfer while improving AI safety.