Next week, I will be at Tsinghua University in Beijing. On Tuesday, in the early afternoon, I will give three lectures for undergraduate students on the theme: ‘Three lectures on AI and its implications for actuarial (and/or financial) professions.’
These lectures explore the relationship between artificial intelligence and insurance. They begin from the observation that insurance has long relied on prediction, classification, and decision-making under uncertainty, well before the recent rise of AI. AI therefore does not introduce these issues from scratch, but changes their scale, granularity, and practical consequences. The lectures review the insurance foundations of pricing and pooling, then examine the main challenges raised by AI, including personalization, selection, causality, bias, fairness, governance, and trust. They finally turn to the concrete uses of AI across the insurance value chain, emphasizing that a good system should not be judged by accuracy alone, but also by its calibration, its fairness, and its ability to support real decisions in practice.
In the evening, I will give a talk at the seminar, at Renmin University of China, on the theme: ‘Using optimal transport to mitigate unfair predictions and quantify counterfactual fairness.’ The first part will revisit topics that I presented in greater detail in the lectures notes of my course this autumn at Kyoto University, particularly the price to be paid in terms of accuracy in order to achieve fairness. The second part will discuss the paper ‘Sequential Transport for Causal Mediation Analysis,’ which was posted online a few days ago.
On Wednesday, I will have in-depth academic exchange session with students from the Tsinghua Actuarial Science Association, at Tsinghua University.
