Abstract: Measuring and managing the risks associated with artificial intelligence (AI) is increasingly critical as AI systems are integrated into high-stakes decision-making environments, such as healthcare. Algorithmic insurance offers a scalable financial solution for quantifying, pricing, and managing the risks inherent in AI deployment, complementary to regulation. It provides a structured mechanism for transferring the risks associated with AI systems from developers and users to insurers, creating a financial buffer that incentivizes responsible AI use and mitigates liability. Our work formalizes the concept of algorithmic insurance and proposes quantitative frameworks to estimate the risk exposure of insurance contracts for machine-driven financial risk. 

Bio: Agni Orfanoudaki is an Associate Professor of Operations Management at the Saïd Business School of Oxford University. Alongside her role, Agni is a Management Studies Fellow at Exeter College and a visiting scholar at the Harvard Kennedy School as a Harvard Data Science Initiative Fellow. She leads the Data-Driven Decisions Lab (3DL) at Oxford, conducting theoretical and empirical research with machine learning, optimization, and stochastic processes with applications to healthcare and insurance. Prior to joining Oxford, Agni received a PhD in Operations Research from the Massachusetts Institute of Technology. She has collaborated with numerous institutions, including a major medical society, two international reinsurance companies, and more than eight hospitals in the US and Europe.

References:

Bertsimas, D. and Orfanoudaki, A., 2023. Pricing algorithmic insurance. arXiv preprint arXiv:2106.00839. https://arxiv.org/pdf/2106.00839.pdf

Singh S, Sarna N, Li Y, Li Y, Orfanoudaki A, Berger M. Distribution-free risk assessment of regression-based machine learning algorithms. arXiv preprint arXiv:2310.03545. https://arxiv.org/pdf/2310.03545