Ce mardi, je participe en visio à la conférence annuelle de l’Institut des Actuaires, à Paris, pour une session sur les modèles causaux en assurance, avec une introduction générale, avant qu’Aurélien Couloumy ne prenne la suite pour présenter des applications.
Cooperative game theory has become a cornerstone of post-hoc interpretability in machine learning, largely through the use of Shapley values. Yet, despite their widespread adoption, Shapley-based methods often rest on axiomatic justifications whose relevance to feature attribution remains debatable. In this paper, we revisit cooperative game theory from an interpretability perspective and argue for a broader and more principled use of its tools. We highlight two general families of efficient allocations, the Weber and Harsanyi sets, that extend beyond Shapley values and offer richer interpretative flexibility. We present an accessible overview of these allocation schemes, clarify the distinction between value functions and aggregation rules, and introduce a three-step blueprint for constructing reliable and theoretically-grounded feature attributions. Our goal is to move beyond fixed axioms and provide the XAI community with a coherent framework to design attribution methods that are both meaningful and robust to shifting methodological trends.
This morning, I will present at the “quatrième Journée d’Econometrie appliquée, en l’honneur de Michel Terraza”. I will present recent work with Agathe Fernandes Machado, Ewen Gallic, François Hu, and Emmanuel Flachaire. Slides are available. The talk is on “Calibration, ou interprétation probabiliste des scores de modèles boites noires” (Calibration, or probabilistic interpretationof black box model scores, but slides are in English).
Cooperative game theory methods, notably Shapley values, have significantly enhanced machine learning (ML) interpretability. However, existing explainable AI (XAI) frameworks mainly attribute average model predictions, overlooking predictive uncertainty. This work addresses that gap by proposing a novel, model-agnostic uncertainty attribution (UA) method grounded in conformal prediction (CP). By defining cooperative games where CP interval properties-such as width and bounds-serve as value functions, we systematically attribute predictive uncertainty to input features. Extending beyond the traditional Shapley values, we use the richer class of Harsanyi allocations, and in particular the proportional Shapley values, which distribute attribution proportionally to feature importance. We propose a Monte Carlo approximation method with robust statistical guarantees to address computational feasibility, significantly improving runtime efficiency. Our comprehensive experiments on synthetic benchmarks and real-world datasets demonstrate the practical utility and interpretative depth of our approach. By combining cooperative game theory and conformal prediction, we offer a rigorous, flexible toolkit for understanding and communicating predictive uncertainty in high-stakes ML applications.
Ce midi, je donne un exposé en visio, sur le thème de l’équité et de la discrimination en assurance, en lien avec les questions d’interprétabilité et d’explicabilité, pour l’Institut des Actuaires, en France. Les slides sont en ligne ici…
Note la vidéo de l’exposé est en ligne sur Youtube.
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.
"sendo l'intento mio scrivere cosa utile a chi la intende…"