A paper co-authored by Heng Xu and Nan Zhang, “Fairness of Ratemaking for Catastrophe Insurance: Lessons from Machine Learning,” published at Information Systems Research, has been honored with the INFORMS Information Systems Society’s Social Justice Best Paper Award 2024. According to the founders of this award - Drs. Ravi Bapna and Anindya Ghose, this award fosters and encourages high quality research that makes an impact towards improving social justice outcomes.

This work was highlighted as a pioneering example of a new paradigm in design research on AI for business, redefining the boundaries of traditional business research and opening new avenues for interdisciplinary exploration. Below are excerpts from an editorial in the June 2024 issue of Information Systems Research, summarizing the contributions of the paper and its impact on the broader business research community:

“Zhang and Xu (2024) formulated a new fairness assessment problem in catastrophe insurance using machine learning concepts. Fairness in actuarial contexts is challenging due to the balance between accurate risk assessment and equitable allocation across protected groups. In catastrophe insurance, policy dependencies can amplify biases, unfairly burdening lower-income regions. Zhang and Xu elegantly tackled this challenge by applying methods from fair data valuation in ML, demonstrating improved fairness over existing approaches.”