SCOR Project Newsletter #2

The second newsletter, related to the SCOR research project is now available. It is a brief summary of the second six months block, from April till the end of September (the first one is available here).

As explained, over the past six months, we have had several interns, including Noé Bosc-Haddad, Florent Crouzet, Julien Siharath, Ana María Patrón Piñerez and Cassandra Mussard, the visit of Laurence Barry and Fei Huang, Philipp Ratz and Samuel Stocksieker defended their PhD, François Hu finished his postdoctoral fellowship, while Marouane Il Idrissi and Arsene Zotsa just arrived, Agathe Fernandes Machado and Olivier Côté (co-supervised with Ewen Gallic and Marie-Pier Côté) finished their PhD courses and are now 100% on their research… We wrote papers, gave talks… Thank you to all those who have supported us, and continue to support us. At least two more years to work on insurance and predictive models, fairness, calibration, discrimination, trust, explainability, interpretability, market equilibria, competition, generative models, and so much more… We’ve still got a lot of work to do, and plenty of enthusiasm!

C’est la rentrée… des chaires parisiennes

Quelques nouvelles de France, avec la rentrée de deux programmes de recherche, avec l’inauguration d’un nouveau cycle de recherche pour la Chaire PARI

Après cinq années de travaux consacrés à l’impact des big data sur le secteur de l’assurance, la chaire PARI va continuer à mettre à profit les compétences pluridisciplinaires de ses chercheurs associés (sociologie, épistémologie, histoire, actuariat…) pour questionner l’implication des assureurs dans la gestion des catastrophes. Le programme de recherche 2024-2027 est en ligne sur le site de la https://www.chaire-pari.fr/.

Sinon, la semaine prochaine, c’est la conférence inaugurale de la Chaire Actions, sur Paris

Là encore, beaucoup de belles choses prévues, on en reparlera !

Risques, big data, réputation… au château de Cerisy, l’assurance face aux défis de son époque

Article d’Esther Attias sur le colloque de la semaine dernière à Cerisy, dans le dernier numéro de Challenges

Au château de Cerisy-la-Salle, dans la Manche, chercheurs et professionnels de l’assurance se sont réunis pour croiser le fer autour du thème « l’assurance au défi des ruptures ». Récit d’un colloque de haute volée dans un lieu hors du temps [à suivre]

(photos empruntées à Sandrine Lemery et Michael Donio)

Summer School in Warszawa

In a couple of days, I will be in Warszawa for a Summer School, to give a short course on Insurance, biases, discrimination and fairness, invited by Łukasz Delong. Slides are now available.

This course will provide a state-of-the-art, on fairness and discrimination, in the context of insurance pricing (and more
generally, predictive models). As explained by Avraham et al. (2014) “insurance companies are in the business of discrimination. Insurers attempt to segregate insureds into separate risk pools based on the differences in their risk profiles, first, so that different premiums can be charged to the different groups based on their differing risks and, second, to incentivize risk reduction by insureds. This is why we let insurers discriminate. There are limits, however, to the types of discrimination that are permissible for insurers. But what exactly are those limits and how are they justified“. First, we will come back to the specificities of predictive models in insurance. We will come back to the different places where a potential discrimination can intervene, by insisting on the possible biases in the data, in the models. We will present in particular the regulations in Europe and North America. In a second step, we will see how to quantify a possible discrimination, insisting on the main measures of “group-fairness”, before discussing the individual approach, in particular in relation with the causal approaches. Indeed, the central question of discrimination is “would the price have been different if this person had been a man instead of a woman“. We will see how to build a counterfactual allowing to quantify a possible discrimination. Finally, we will see how to correct a discrimination, insisting on the in-processing (throught penalized models) and post-processing approaches (using optimal transport). This course will be based on the recent textbook, Charpentier (2024) Insurance, biases, discrimination and fairness. Springer.

Colloque, au Centre Culturel International de Cerisy, en France

Je serai pour les 6 prochains jours au Centre Culturel International de Cerisy, pour un colloque sur “l’assurance face à ses ruptures“. Au programme (13/9) histoire de l’assurance, (14/9) la financiarisation, (15/9) les données massives, (16/9) le changement climatique et (17/9) le rôle de l’État. Je ferais une intervention dimanche matin, sur le thème Certitudes collective et incertitudes individuelles, les données massives changent-elles la donne ?

Dans cette intervention, nous commencerons par un détour historique et sociologique en revenant sur les travaux d’Émile Durkheim et Max Weber. Ces chercheurs ont montré que bien que les actions individuelles puissent être imprévisibles, les comportements collectifs suivent des schémas réguliers. En agrégeant les actions individuelles, nous observons des régularités au sein de groupes plus larges, une idée illustrée par la prédictibilité du suicide dans le contexte des données massives. Ensuite, nous aborderons les réflexions de Herbert Simon et sa théorie de la rationalité limitée, suggérant que malgré les limites cognitives des individus, les rendant individuellement imprévisibles, des modèles prévisibles émergent dans la prise de décision globale. Nous explorerons ensuite des questions épistémologiques en discutant l’interprétation fréquentiste des probabilités, laquelle requiert la répétition pour être quantifiée, et la difficulté d’associer une probabilité à un événement unique. La réponse bayésienne, qui interprète les probabilités comme des croyances ou des scores, complique la relation avec l’équité actuarielle. Nous questionnerons la calibration des modèles et l’interprétation des scores à travers des exemples simples, tels que “avoir 70% de chances qu’une opération militaire réussisse” ou “avoir 70% de chances de pluie entre 14 et 15 heures”. Enfin, nous conclurons en prenant en compte le caractère temporel de la prévision: prévoir un accident 15 minutes avant qu’il ne survienne n’est pas équivalent à le prévoir un an à l’avance.

Les slides sont en ligne. Sinon Le Monde, quotidien français, publiait un portrait des colloques (organisés depuis 1952) cet été, il y a un mois,

“Mathematical Foundations of AI” day at the Sorbonne center for artificial intelligence

On Thursday 12th, I will attend the Mathematical Foundations of AI day, organized by the DATAIA Institute and SCAI (Sorbonne Center for Artificial Intelligence), in association with several scientific societies (namely, the Fondation Mathématique Jacques Hadamard (FMJH), the Fondation Sciences Mathématiques de Paris-FSMP, the MALIA group of the Société Française de Statistique and the Société Savante Francophone d’Apprentissage Machine (SSFAM)).

Slides are now online.

In this talk, we present two complementary approaches to addressing fairness in algorithmic decision-making through the lens of counterfactual reasoning and optimal transport, both in individual and group fairness. First, we introduce a novel method that links two existing counterfactual approaches: causal graph-based adaptations (Plečko and Meinshausen, 2020) and optimal transport (De Lara et al., 2024). By extending “Knothe’s rearrangement” (Bonnotte, 2013) and “triangular transport” (Zech and Marzouk, 2022) to probabilistic graphical models, we propose a new group framework, termed sequential transport, which we apply to the problem of individual fairness. Theoretical foundations are established, followed by numerical demonstrations on synthetic and real datasets. Building on this, we extend the discussion to algorithmic fairness in the presence of multiple sensitive attributes. While traditional fairness frameworks focus on eliminating bias with respect to a single sensitive variable, their effectiveness diminishes with multiple sensitive characteristics. To address this, we propose a sequential fairness framework based on multi-marginal Wasserstein barycenters, generalizing Strong Demographic Parity to handle multiple sensitive features. Our method provides a closed-form solution for the optimal, sequentially fair predictor, enabling interpretation of correlations between sensitive attributes. Furthermore, we introduce an approximate fairness framework that balances risk and unfairness, allowing for prioritization of fairness across specific attributes. Both approaches are supported by comprehensive numerical experiments on synthetic and real-world datasets, showcasing the practical efficacy of these methods in promoting fair decision-making. Together, they provide a robust framework for addressing fairness in complex, multi-attribute settings while preserving interpretability and flexibility.

References are given below

  1. we will discuss further counterfactual fairness, initiated in Optimal Transport for Counterfactual Estimation: A Method for Causal Inference, and the more recent paper Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness 
  2. we will discuss Wasserstein barycenters with multiple sensistive attributes, A Sequentially Fair Mechanism for Multiple Sensitive Attributes

[added on Sept 13th] Thanks (on the right) for this nice picture with .

European Actuarial Journal Conference (in Lisbon)

Next week, I will be at the European Actuarial Journal Conference, at the Lisbon School of Economics and Business, EAJ’24.

I will give a talk on calibration of actuarial models, based on our recent paper with Agathe Fernandes Machado, Emmanuel Flachaire, Ewen Gallic and François Hu, mainly “Probabilistic Scores of Classifiers, Calibration is not Enough” (as well as recent work on recalibration). Slides are available.

In binary classification tasks, accurate representation of probabilistic predictions is essential for various real-world applications such as predicting payment defaults or assessing medical risks. The model must then be well-calibrated to ensure alignment between predicted probabilities and actual outcomes. However, when score heterogeneity deviates from the underlying data probability distribution, traditional calibration metrics lose reliability, failing to align score distribution with actual probabilities. In this study, we highlight approaches that prioritize optimizing the alignment between predicted scores and true probability distributions over minimizing traditional performance or calibration metrics. When employing tree-based models such as Random Forest and XGBoost, our analysis emphasizes the flexibility these models offer in tuning hyperparameters to minimize the Kullback-Leibler (KL) divergence between predicted and true distributions. Through extensive empirical analysis across 10 UCI datasets and simulations, we demonstrate that optimizing tree-based models based on KL divergence yields superior alignment between predicted scores and actual probabilities without significant performance loss. In real-world scenarios, the reference probability is determined a priori as a Beta distribution estimated through maximum likelihood. Conversely, minimizing traditional calibration metrics may lead to suboptimal results, characterized by notable performance declines and inferior KL values. Our findings reveal limitations in traditional calibration metrics, which could undermine the reliability of predictive models for critical decision-making.