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About this paper
Dimitris Bertsimas, Agni Orfanoudaki
A framework quantitatively links machine learning operational decisions, specifically classification threshold calibration, to financial liability exposure. The work proposes an algorithmic insurance contract structure that incentivizes risk-aware AI deployment, demonstrating that accuracy-maximizing models can incur significantly higher tail risks and that insurance offers substantial value through risk transfer, capital efficiency, and operational improvements.
Key insights
- Optimizing AI systems solely for average accuracy or expected loss can lead to substantially higher tail risk, with the financial penalty growing quadratically with threshold misalignment.
- Algorithmic insurance functions as both a risk transfer mechanism and an active governance tool, creating value through risk transfer, capital efficiency, and mandatory operational improvements (calibration value).
- Optimal insurance contract duration decreases with faster model drift rates, and investments in interpretability reduce residual tail risk, suggesting premium discounts for verifiable transparency features.
Summary by alphaXiv.
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