Category Archives: Seminar

Balance and Calibration of Probabilistic Scores: From GLM to Machine Learning

Tomorrow, I will give a talk on “Balance and Calibration of Probabilistic Scores:“” From GLM to Machine Learning” at Singapore campus – ESSEC Asia-Pacific. The abstract is

This study evaluates binary classifier performance with a focus on calibration, which is often overlooked by traditional metrics like accuracy. In high-stakes domains such as finance and healthcare, well-calibrated probabilities are crucial. We highlight the limitations of standard calibration metrics, particularly under score distortions and heterogeneous distributions. To address this, we introduce the Local Calibration Score and advocate optimizing models using Kullback-Leibler (KL) divergence to better align predicted scores with true probabilities. Our approach emphasizes balancing global and local calibration, ensuring overall distributional alignment while maintaining reliability across different score ranges. Using Random Forest and XGBoost across diverse datasets, we show that KL-based tuning improves calibration without sacrificing performance. Our results reveal that relying solely on traditional metrics can mislead model assessment, especially in sensitive decision-making scenarios. This is some joint work with Agathe Fernandes Machado and Ewen Gallic.

Modeling and Understanding Indirect Discrimination in Algorithmic Fairness

In a couple of days, I will give a talk on “Modeling and Understanding Indirect Discrimination in Algorithmic Fairness” at Singapore campus – ESSEC Asia-Pacific. The abstract is

Observed disparities between groups in algorithmic decisions (whether in hiring, credit approval, or risk prediction) do not necessarily imply direct discrimination. They may also stem from legitimate differences in the distribution of explanatory attributes. Understanding and quantifying which components of these gaps are “explained” versus those that reflect direct or indirect discrimination lies at the core of modern causal approaches to algorithmic fairness. This talk will begin with an accessible introduction to group-gap decomposition, building on the classical Kitagawa–Oaxaca–Blinder econometric framework. This approach separates differences attributable to observable characteristics from residual components that may signal discriminatory effects. The second part will introduce recent developments leveraging optimal transport to construct individual-level counterfactuals, enabling estimation of direct and indirect causal effects for each observation. In particular, we will show how sequential transport mappings aligned with a causal graph can disentangle pathways and quantify the contribution of each mediator. This methodology overcomes limitations of traditional linear models, introduced by Kitagawa, Oaxaca and Blinder, provides interpretable counterfactuals, and is well suited to complex empirical settings. The presentation will combine intuitive motivation, illustrative examples, and recent research insights, with the goal of making these tools accessible and useful to researchers in management science, applied economics, and data science.

Talk at NTU (Nanyang) in Singapore

Tomorrow, I will be at Nanyang Technological University to give a talk at an internal seminar, “Fairness and discrimination in insurance

What’s unique about insurance is that even statistical discrimination, which by definition is devoid of malicious intent, poses significant challenges. Because, on the one hand, policymakers would like insurers to treat their policyholders equally, without discrimination based on race, gender, age or other characteristics, even if it could make (statistical) sense to (indirectly) discriminate. On the other hand, at the core of actuaries’ activities lies discrimination, between risky and non-risky policyholders. And this risk is often statistically correlated with sensitive characteristics that regulation would like to prohibit insurers from taking into account. The analysis of possible discrimination in decision rules, whether human or algorithmic, is an old subject. Most of the concepts date back at least to the 50s, but recent developments in artificial intelligence have brought these issues back into the spotlight. Massive data facilitate statistical or proxy discrimination, and black-box algorithms do not facilitate understanding. Not to mention the various regulations that make it difficult to collect sensitive information, and ultimately test whether decisions can be discriminated against, especially indirectly.

The talk is based on the textbook Insurance, Biases, Discrimination and Fairness, as well as recent papers, arXiv:2511.11294 (AAAI’26), arXiv:2408.03425 (AAAI’25), arXiv:2309.06627 (AAAI’24) and arXiv:2306.12912  (ECML’24).

Talk at the JFLI, at the NII (国立情報学研究所) in Tokyo (東京)

On my way to Tokyo (東京) for a couple of days, where I was invited to give a talk by the JFLI (Japanese-French Laboratory for Informatics) at the National Institute of Informatics (国立情報学研究所), Room 1810, NII. The talk will be on “Counterfactual and Transport-Based Methods for Understanding Indirect Discrimination in Algorithmic Systems

Understanding disparities between demographic groups in algorithmic predictions remains a central challenge in responsible AI. Classical decomposition methods such as the Kitagawa–Oaxaca–Blinder framework, recently extended to nonlinear and machine-learning settings by Tierney et al. (AAAI 2026), show that observed gaps may arise either from legitimate differences in feature distributions or from structural bias. However, such aggregate decompositions provide limited insight into individual-level counterfactual behaviour. In this talk, I will present recent methodological advances that combine causal reasoning with optimal transport to characterize direct and indirect discriminatory pathways in modern predictive systems. Building on transport-based counterfactuals (Fernandes Machado et al., AAAI 2025; IJCAI 2025), we obtain individual-level counterfactual mediators that respect a given causal graph, including both continuous and categorical variables. This enables a fine-grained decomposition of model disparities into components attributable to causal pathways, beyond what is possible with standard fairness metrics or feature-removal strategies. The presentation will emphasize: the connection between decomposition-based fairness analyses and causal mediation; the construction of transport-based counterfactuals aligned with probabilistic graphical models; and applications showing how indirect discrimination can propagate through proxy variables even when sensitive features are not used. The goal is to give a concise and technically grounded overview of how optimal transport and counterfactual inference can provide interpretable tools for understanding fairness issues in machine-learning models. This talk is intended for researchers interested in causal ML, fairness analysis, and transport-based generative methods.

Exposé Chaire PARI, les trois piliers de l’équité en tarification

Mercredi matin (en France), après-midi (à Kyoto), nuit (à Montréal), je vais donner un exposé pour le séminaire mensuel de la chaire PARI, intitulé Un cadre de gouvernance à trois piliers pour une tarification équitable de l’assurance. J’y présenterais notre récent travail, publié justement par la Chaire PARI (document de travail 37), et également associé à un rapport publié par la Casualty Actuarial Society aux États-Unis (et présenté la semaine dernière par Olivier), A Scalable toolbox for exposing indirect discrimination in insurance rates”. Les slides sont en ligne.

SCOR Foundation for Science Webinar, ML and Econometrics

This week, I will give a talk at the SCOR Foundation for Science webinar (slides are available online). and I was asked to give a talk on econometrics vs IA (or machine learning),

Of course, the two concepts are related, and there is a continuum between them.

As we wrote it Charpentier et al. (2017)

Econometrics and machine learning seem to have one common goal: to construct a predictive model, for a variable of interest, using explanatory variables (or features).

For the purposes of this presentation, we will begin by contrasting the two, emphasizing the differences, and then showing the connections that exist.

Long story short, in between, we have computation statistics, or statistical learning, corresponding to computational techniques with mathematical probabilistic guarantees.

Continue reading SCOR Foundation for Science Webinar, ML and Econometrics

Talk with CCR and chaire PARI, in Paris, on government intervention and welfare

This afternoon, I will give a brief talk on welfare and optimal policies for government intervention, at CCR, in Paris. I have some slides to present. The presentation is based on a papers we wrote a few years ago, Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach

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.

In this paper, we used the concept of Marginal Value of Public Funds, or “MVPF, introduced in 2020 by Amy Finkelstein, Nathaniel Hendren and Ben Sprung-Keyser, in “Welfare Analysis Meets Causal Inference“, and “A Unified Welfare Analysis of Government Policies“. See some slides online, or the website https://policyinsights.org/.

Talk, Chaire ACTIONS, CNAM, Paris

Today, I will give a joint presentation with Olivier Côté at the CNAM (Conservatoire National des Arts et Métiers) in Paris, for the chaire ACTIONS. My talk will be an introduction based on our two first joint papers, “A fair price to pay: Exploiting causal graphs for fairness in insurance” that was published early that year, and “Selection Bias in Insurance: Why Portfolio-Specific Fairness Fails to Extend Market-Wide“. Slides are now available. Olivier will present more recent work, to visualize the fairness spectrum on real data from an insurance company. Slides are available.

Continue reading Talk, Chaire ACTIONS, CNAM, Paris

Talks at Milliman R&D, Paris

Tomorrow, I will attend the Milliman R&D Seminar, for a joint presentation with Olivier Côté, who will spend the week in Paris. My talk will be an introduction based on our two first joint papers, “A fair price to pay: Exploiting causal graphs for fairness in insurance” that was published early that year, and “Selection Bias in Insurance: Why Portfolio-Specific Fairness Fails to Extend Market-Wide“. Slides are now available. Olivier will present more recent work, to visualize the fairness spectrum on real data from an insurance company. Slides are available.

Les lundis de l’IA et de la finance

Dans dix jours, je participerai (en ligne) aux lundis de l’IA et de la finance, avec comme thème “mesurer et corriger les biais dans les systèmes d’IA”.  Co-organisés par l’Autorité de Contrôle Prudentiel et de Résolution (ACPR/Banque de France) et Télécom Paris, les « Lundis de l’IA et de la Finance » forment un cycle de conférences autour de la réglementation de l’IA dans le secteur financier. Dans ce cadre, régulateurs, chercheurs et autres acteurs de l’industrie financière se sont réunis tous les deux mois pour échanger autour de différentes thématiques du domaine.

Le programme est incroyable

  • 17h – 17h10 : Introduction, Olivier Fliche (ACPR/Banque de France) et/ou Winston Maxwell (Télécom Paris)
  • 17h10 – 17h30 : Arthur Charpentier (UQAM Montréal) : présentation de travaux sur l’équité dans le domaine de l’assurance (où le cœur du métier est précisément de « discriminer » les risques), y compris une présentation des métriques d’équité et de leurs implications [les slides sont disponibles]
  • 17h30- 17h50 : Benoît Rottembourg et Jean-Michel Loubes (Inria) : méthodes d’identification des biais sur un cas concret (impayés en téléphonie)
  • 17h50-18h10 : David Cortés (AI-vidence) et/ou Stephan Clémençon (Télécom Paris) : présentation d’une méthode empirique de correction des biais directement dans les données d’entrée
  • 18h10-18h25 : Questions / discussions
  • 18h25-18h30 : Remarques de clôture, O. Fliche et/ou D. Bounie

 

On my way to Toronto

Tomorrow, I will be on my way to Toronto (by train, as always). I will give a seminar on Monday, at the University of Toronto. The long title is Using optimal transport to mitigate unfair predictions and quantify counterfactual fairness (slides are available)

Many industries are heavily reliant on predictions of risks based on characteristics of potential customers. Although the use of said models is common, researchers have long pointed out that such practices perpetuate discrimination based on sensitive features such as gender or race. Given that such discrimination can often be attributed to historical data biases, an elimination or at least mitigation, is desirable. With the shift from more traditional models to machine-learning based predictions, calls for greater mitigation have grown anew, as simply excluding sensitive variables in the pricing process can be shown to be ineffective.  In  the first part of this seminar, we propose to mitigate possible discrimination (related to so call « group fairness », related to discrepancies in score distributions) through the use of Wasserstein barycenters instead of simple scaling. To demonstrate the effects and effectiveness of the approach we employ it on real data and discuss its implications. This part will be based on recent work with François Hu and Philipp Ratz (2310.205082309.066272306.12912 and 2306.10155). In the second part, we will focus on another aspect of discrimination usually called « counterfactual fairness », where the goal is to quantify a potential discrimination « if that person had not been Black » or « if that person had not been a woman ». The standard approach, called « ceteris paribus » (everything remains unchanged) is not sufficient to take into account indirect discrimination, and therefore, we consider a « mutates mutants » approach based on optimal transport. With multiple features, optimal transport becomes more challenging and we suggest a sequential approach based on probabilistic graphical models. This part will be based on recent work with Agathe Fernandes Machado and Ewen Gallic (2408.03425 and 2501.15549).

Mathematics Colloquium at CIMAT

Wednesday afternoon, I am invited to give a talk at the Mathematics Colloquium at Centro de Investigación en Matemáticas (CIMAT). I will present an overview of recent work. The long title is Using optimal transport to mitigate unfair predictions and quantify counterfactual fairness (slides are available)

Many industries are heavily reliant on predictions of risks based on characteristics of potential customers. Although the use of said models is common, researchers have long pointed out that such practices perpetuate discrimination based on sensitive features such as gender or race. Given that such discrimination can often be attributed to historical data biases, an elimination or at least mitigation, is desirable. With the shift from more traditional models to machine-learning based predictions, calls for greater mitigation have grown anew, as simply excluding sensitive variables in the pricing process can be shown to be ineffective.  In  the first part of this seminar, we propose to mitigate possible discrimination (related to so call « group fairness », related to discrepancies in score distributions) through the use of Wasserstein barycenters instead of simple scaling. To demonstrate the effects and effectiveness of the approach we employ it on real data and discuss its implications. This part will be based on recent work with François Hu and Philipp Ratz (2310.205082309.066272306.12912 and 2306.10155). In the second part, we will focus on another aspect of discrimination usually called « counterfactual fairness », where the goal is to quantify a potential discrimination « if that person had not been Black » or « if that person had not been a woman ». The standard approach, called « ceteris paribus » (everything remains unchanged) is not sufficient to take into account indirect discrimination, and therefore, we consider a « mutates mutants » approach based on optimal transport. With multiple features, optimal transport becomes more challenging and we suggest a sequential approach based on probabilistic graphical models. This part will be based on recent work with Agathe Fernandes Machado and Ewen Gallic (2408.03425 and 2501.15549).

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Assurance, IA, biais et équité

Pour compléter mon exposé auprès de l’Association des Masters d’Actuariat, je vais poster un billet, qui reprend les slides de mon intervention, avec un peu du discours qui va avec. Tout d’abord, mille excuses, mais les slides sont en anglais. Et ce que je raconte est très librement basé sur des travaux récents qu’on a pu mener, avec pas mal de monde, dont Laurence Barry (de la chaire PARI), Marie-Pier Côté (professeure à l’université Laval, à Québec), Olivier Côté (doctorant que je co-supervise avec Marie-Pier), Agathe Fernandes Machado (doctorante à l’UQAM), Ewen Gallic, (maître de conférences, à Marseille), François Hu (ancien stagiaire postdoctoral et aujourd’hui consultant en France), Philipp Ratz (ancien stagiaire postdoctoral et aujourd’hui consultant en Suisse), ainsi qu’Ana Patron Pinerez (ancienne stagiaire qui venait de Colombie) et Mulah Moriah (doctorant en France, que j’ai suivi pendant son mémoire de fin de maîtrise).

Continue reading Assurance, IA, biais et équité