Tomorrow, we organize our workshop Confidence and Fairness: Scientific Foundations in AI and Risk, at the SCOR headquarters, in Paris. I’m going to give the keynote address for the day, presenting the work we’ve been able to carry out over the past 18 months (over the 3 years of funding), while laying the foundations for the concepts we’ll be discussing throughout the day.
9:00 – Registration
9:20 – Introduction speech
9:30 – Arthur Charpentier – “Fairness of predictive models: an application to insurance markets”
10:15 – Coffee break
10:45 – Toon Calders – “Unfair, You Say? Explain Yourself!”
11:30 – Isabel Valera – “Society-centered AI: An Integrative Perspective on Algorithmic Fairness”
12:15 – Lunch break
13:15 – Jean Michel Loubes – “Beyond fairness measures, discovering the bias in the algorithm”
14:00 – Evgeny Chzhen – “An optimization approach to post-processing for classification with system constraints”
14:45 – Michele Loi – “From Facts to Fairness: Diagnostic Models in Algorithmic Decision-Making”
15:30 – Coffee break
16:00 – Aurélie Lemmens – “Fair Active Learning for Personalized Policies”
16:45 – François Hu and Antoine Ly – “Fairness and Confidence in Insurance Markets, a Practitioners Perspective”
17:30 – Closing cocktail
This paper designs a sequential repeated game of a micro-founded society with three types of agents: individuals, insurers, and a government. Nascent to economics literature, we use Reinforcement Learning (RL), closely related to multi-armed bandit problems, to learn the welfare impact of a set of proposed policy interventions per $1 spent on them. The paper rigorously discusses the desirability of the proposed interventions by comparing them against each other on a case-by-case basis. The paper provides a framework for algorithmic policy evaluation using calibrated theoretical models which can assist in feasibility studies.
After a few days in Switzerland, I am on my way to Luxembourg. Tonight, I will be talking at the Institut Luxembourgeois des actuaires (ILAC), on “discrimination et interprétabilité des modèles prédictifs”. Slides (in English) are available here.
This afternoon, after a short visit at ETH Zürich yesterday, I will be at the département de sciences actuarielles, at the Université de Lausanne. I will be talking about using optimal transport to mitigate unfair predictions and quantify counterfactual fairness. Slides are now online.
Recently, optimal transport-based approaches have gained attention for deriving counterfactuals, e.g., to quantify algorithmic discrimination. However, in the general multivariate setting, these methods are often opaque and difficult to interpret. To address this, alternative methodologies have been proposed, using causal graphs combined with iterative quantile regressions (Plečko and Meinshausen (2020)) or sequential transport (Fernandes Machado et al. (2025)) to examine fairness at the individual level, often referred to as “counterfactual fairness.” Despite these advancements, transporting categorical variables remains a significant challenge in practical applications with real datasets. In this paper, we propose a novel approach to address this issue. Our method involves (1) converting categorical variables into compositional data and (2) transporting these compositions within the probabilistic simplex of \mathbb{R}^d. We demonstrate the applicability and effectiveness of this approach through an illustration on real-world data, and discuss limitations.