Category Archives: Seminar

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).

Continue reading Mathematics Colloquium at CIMAT

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

Talk at the Financial Conduct Authority, UK

This morning (Montréal time), I will give a talk for the Financial Conduct Authority in London, in the UK, on “Demystify fairness and discrimination in insurance, and avoid some pitfalls“.

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.

Perspective causale sur les discriminations directes et indirectes liées aux caractéristiques sensibles dans les modèles prédictifs en assurance

Olivier Côté donnera un exposé “Perspective causale sur les discriminations directes et indirectes liées aux caractéristiques sensibles dans les modèles prédictifs en assurance” au séminaire étudiant de l’IID intitulé modèles causaux et inférence causale en médecine et assurance, cet après midi

L’équité envers les assurés est un enjeu central pour le secteur de l’assurance. Les assureurs reposent largement sur les données personnelles pour plusieurs décisions automatisées. Dans ce travail, nous explorons la tarification équitable en assurance à travers une perspective causale, en mettant l’accent sur les discriminations directes et indirectes liées aux variables sensibles définies et observées. Nous analysons les mécanismes par lesquels ces variables influencent la tarification et proposons une catégorisation des méthodologies équitables en cinq familles, chacune présentant des propriétés claires en matière d’équité. Le raisonnement causal permet de représenter les biais, de clarifier les hypothèses concernant le mécanisme générateur de données, et de distinguer le signal de chaque variable par rapport aux variables sensibles.

Les outils développés guideront les spécialistes du secteur de l’assurance vers de meilleures pratiques en équité algorithmique.

Algorithmic fairness with optimal transport: quantifying counterfactual fairness and mitigating group fairness

This Friday, I will be in Laval University, in Québec, to give a talk at the Statlab annual day.

In this talk, we present two complementary approaches to addressing fairness in algorithmic decision-making, regarding individual and group fairness. First, we use Wasserstein barycenters to obtain (strong Demographic Parity) with one or multiple sensitive features. Our method provides a closed-form solution for the optimal, sequentially fair predictor, enabling possible interpretation of correlations between sensitive attributes. Then, 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.

Slides are available online.

“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 .