Professor Arthur Charpentier (Université du Québec à Montréal) will deliver the Mathematics and Mathematical Sciences Global Special Lecture ６ as follows.
If you wish to attend, please register using the Google form below.

Date and Time:
November 4 (Tue), 5 (Wed), 10 (Mon), 11 (Tue), and 13 (Thu), 9:30–11:30 each day

Venue:
Room 127, Building No. 3, Graduate School of Science

Title:Fairness and distribution in insurance, an actuarial perspective

Abstract:
This course explores the interplay between biases, discrimination, and fairness in insurance, a field where the very logic of risk classification relies on differential treatment of policyholders. Insurance companies segment individuals into risk pools in order to assign premiums that reflect expected costs and incentivize risk reduction. While such segmentation is intrinsic to the industry, it raises critical questions: which forms of discrimination are permissible, where do biases in data and models intervene, and how do regulations around the world define acceptable practices? We will examine these questions by studying how discrimination may arise in predictive modeling for insurance, the measures used to detect it, and the methods available to mitigate unfairness—ranging from regulatory frameworks to statistical corrections. Lecture notes will be provided, based on selected chapters from Charpentier (2024).

On the mathematical side, the course emphasizes the role of modern quantitative tools in addressing fairness. After reviewing statistical and machine learning approaches to insurance pricing, we will introduce frameworks for assessing group fairness (such as demographic parity and equalized odds) and for analyzing counterfactual fairness, grounded in causal reasoning. A central mathematical focus will be on optimal transport and related concepts, which provide a versatile toolkit for measuring and mitigating disparities, both at the group and individual level. We will explore their use in post-processing adjustments of models, as well as in constructing counterfactuals for fairness evaluation.

References
Charpentier, A. (2024). Insurance: Biases, Discrimination, and Fairness. Springer. (Main course reference; lecture notes will be based on selected parts.)

Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and Machine Learning. fairmlbook.org. (A standard reference on fairness definitions and methods in ML.)

Villani, C. (2009). Optimal Transport: Old and New. Springer. (Foundational text for mathematical background on optimal transport.)

Pearl, J., & Mackenzie, D. (2018). The Book of Why: The New Science of Cause and Effect. Basic Books. (Reference on causal inference and counterfactual reasoning, central to counterfactual fairness.)
Registration Required:
Those who wish to attend the lecture are requested to register via the Google form. Please access the form through the QR code or the URL below.
URL: https://forms.gle/3PEuSmPu2Bjy4dpA7

Deadline: October 27 (Mon)

Mathematics and Mathematical Sciences Global Lecture IV is a core subject of the Advanced Program for Fostering Innovation in Mathematics and Mathematical Sciences.

To take Global Lecture IV for credit, registration through KULASIS is required. The registration period for first-semester courses is from October 10 (Fri) to October 14 (Mon).

Students of Kyoto University may attend the lecture without course registration, but registration via the Google form is still required.

For abstracts and further details of each special lecture, please visit the program website:
https://www.math.kyoto-u.ac.jp/ja/ktgu/courses