Jeudi 7 décembre, je participerai (en distanciel) à une “table ronde” sur le risque sécheresse en France, à l’École Normale Supérieure de Ker Lann, intitulée “enjeux actuels et futurs des sécheresses“. Comme on m’a demandé de présenter nos travaux récents, j’ai prévu quelques slides rapides…
Our paper, Measuring and Mitigating Biases in Motor Insurance Pricing, with Mulah Moriah and Franck Vermet, is now available on Arxiv.
The non-life insurance sector operates within a highly competitive and tightly regulated framework, confronting a pivotal juncture in the formulation of pricing strategies. Insurers are compelled to harness a range of statistical methodologies and available data to construct optimal pricing structures that align with the overarching corporate strategy while accommodating the dynamics of market competition. Given the fundamental societal role played by insurance, premium rates are subject to rigorous scrutiny by regulatory authorities. These rates must conform to principles of transparency, explainability, and ethical considerations. Consequently, the act of pricing transcends mere statistical calculations and carries the weight of strategic and societal factors. These multifaceted concerns may drive insurers to establish equitable premiums, taking into account various variables. For instance, regulations mandate the provision of equitable premiums, considering factors such as policyholder gender or mutualist group dynamics in accordance with respective corporate strategies. Age-based premium fairness is also mandated. In certain insurance domains, variables such as the presence of serious illnesses or disabilities are emerging as new dimensions for evaluating fairness. Regardless of the motivating factor prompting an insurer to adopt fairer pricing strategies for a specific variable, the insurer must possess the capability to define, measure, and ultimately mitigate any ethical biases inherent in its pricing practices while upholding standards of consistency and performance. This study seeks to provide a comprehensive set of tools for these endeavors and assess their effectiveness through practical application in the context of automobile insurance.
Avec , on vient de finaliser un article, paru dans les documents de travail de la chaire PARI, sur le thème “Intelligence artificielle et individualisation des garanties en assurance: échec ou retard à l’allumage ?“
Derrière l’engouement suscité par l’utilisation de l’intelligence artificielle dans le secteur de l’assurance se cache une réalité plus nuancée. Prenons par exemple l’assurance automobile et l’assurance santé. Les tentatives d’usage de l’intelligence artificielle à des fins de tarification n’y ont pour le moment pas engendré le « changement de paradigme » pourtant annoncé. Pourquoi ? Plusieurs raisons peuvent être invoquées, qui vont des fondamentaux de l’assurance au choix assumé de certains assureurs de ne pas toucher à la mutualisation des risques, dans un contexte marqué par deux tendances opposées : la recherche par les consommateurs de services et produits toujours plus personnalisés et le refus sociétal de solutions qui laisseraient certains individus au bord du chemin de l’assurabilité.
(pour tenir compte des contraintes esthétiques des documents de travail de la chaire (“illustration d’un peintre flamand”), rien de très exotique, et donc merci à l’alchimiste de Jan Havickszoon Steen)
I just want to mention the very nice work of Naël Shiab, with a lovely interactive notebook created to help journalists understand math concepts often used in news stories.
A revised version of our paper “Fairness Explainability using Optimal Transport with Applications in Image Classification” is now online, with more discussion about conterfactuals
Ensuring trust and accountability in Artificial Intelligence systems demands explainability of its outcomes. Despite significant progress in Explainable AI, human biases still taint a substantial portion of its training data, raising concerns about unfairness or discriminatory tendencies. Current approaches in the field of Algorithmic Fairness focus on mitigating such biases in the outcomes of a model, but few attempts have been made to try to explain why a model is biased. To bridge this gap between the two fields, we propose a comprehensive approach that uses optimal transport theory to uncover the causes of discrimination in Machine Learning applications, with a particular emphasis on image classification. We leverage Wasserstein barycenters to achieve fair predictions and introduce an extension to pinpoint bias-associated regions. This allows us to derive a cohesive system which uses the enforced fairness to measure each features influence on the bias.Taking advantage of this interplay of enforcing and explaining fairness, our method hold significant implications for the development of trustworthy and unbiased AI systems, fostering transparency, accountability, and fairness in critical decision-making scenarios across diverse domains.
Our paper Parametric Fairness with Statistical Guarantees is now available on ArXiv.
Algorithmic fairness has gained prominence due to societal and regulatory concerns about biases in Machine Learning models. Common group fairness metrics like Equalized Odds for classification or Demographic Parity for both classification and regression are widely used and a host of computationally advantageous post-processing methods have been developed around them. However, these metrics often limit users from incorporating domain knowledge. Despite meeting traditional fairness criteria, they can obscure issues related to intersectional fairness and even replicate unwanted intra-group biases in the resulting fair solution. To avoid this narrow perspective, we extend the concept of Demographic Parity to incorporate distributional properties in the predictions, allowing expert knowledge to be used in the fair solution. We illustrate the use of this new metric through a practical example of wages, and develop a parametric method that efficiently addresses practical challenges like limited training data and constraints on total spending, offering a robust solution for real-life applications.
Insurance, Biases, Discrimination and Fairness is officially annonced for February 2024… (ISBN 978-3-031-49782-7)
Since I will use it for teaching in January and February, we will use the v2, mentioned in a previous post (pdf available on my blog).
This Thursday, I will give a talk on fairness and ethics at the TD Insurance IA/ML annuel conference. Slides are available (it is an overview of Insurance, biaises, discrimination and fairness v2, emphasizing the use of optimal transport techniques).
This Thursday, I will present at the CIREQ Séminaire Marcel-Dagenais en Économétrie at Université de Montréal, ou paper Optimal Transport for Counterfactual Estimation: A Method for Causal Inference, written with Emmanuel Flachaire and Ewen Gallic.
Many problems ask a question that can be formulated as a causal question: “what would have happened if…?” For example, “would the person have had surgery if he or she had been Black?” To address this kind of questions, calculating an average treatment effect (ATE) is often uninformative, because one would like to know how much impact a variable (such as skin color) has on a specific individual, characterized by certain covariates. Trying to calculate a conditional ATE (CATE) seems more appropriate. In causal inference, the propensity score approach assumes that the treatment is influenced by x, a collection of covariates. Here, we will have the dual view: doing an intervention, or changing the treatment (even just hypothetically, in a thought experiment, for example by asking what would have happened if a person had been Black) can have an impact on the values of x. We will see here that optimal transport allows us to change certain characteristics that are influenced by the variable we are trying to quantify the effect of. We propose here a mutatis mutandis version of the CATE, which will be done simply in dimension one by saying that the CATE must be computed relative to a level of probability, associated to the proportion of x (a single covariate) in the control population, and by looking for the equivalent quantile in the test population. In higher dimension, it will be necessary to go through transport, and an application will be proposed on the impact of some variables on the probability of having an unnatural birth (the fact that the mother smokes, or that the mother is Black).
Slides are available online. I will try to mention additional papers published this year, such as Fairness in Multi-Task Learning via Wasserstein Barycenters, Mitigating Discrimination in Insurance with Wasserstein Barycenters or more recently A Sequentially Fair Mechanism for Multiple Sensitive Attributes.
Demain matin, avec Olivier et Marie-Pier Côté, on sera chez l’assureur Intact pour parler équité et discrimination. Olivier présentera ses travaux récents sur l’utilisation de modèles causaux pour proposer des modèles “équitables” en assurance. Le papier (a fair price to pay: exploiting directed acyclic graphs for fairness in insurance) sera bientôt disponible !
Début 2010, Nicolas Sarkozy, suivit par Jérôme Cahuzac, entrait “en guerre contre les fraudeurs”. En mars 2020, la France (par la voix de son président) entrait en “guerre sanitaire” contre un coronavirus. Et plus récemment, le sociologue Dominique Méda reprenait l’expression de l’économiste Christian Gollier qui commençait son livre par la phrase « dans ce livre, j’exprime mes espoirs et mes doutes quant à la possibilité de gagner la guerre mondiale contre les dérèglements du climat ». Évoquer la guerre permet probablement de marquer les esprits, mais à force d’être en guerre contre tout on peut se demander si l’association a encore du sens.
Continue reading Est-il nécessaire (et utile) d’être en guerre contre tout ?
Le 16 mai 2024, avec Marie-Pier Côté, on organise la seconde journée sur l’équité et la discrimination en assurance, à l’Université Laval. Plus d’information dans les mois à venir.
On Monday, December 18th, 2023, we organize, at UQAM, a workshop on “Networks, Games and Risk“.
Decentralized risk-sharing markets are markets for risk exchange in which a pool of individuals agree to mutually insurer each other, without recourse to a centralized insurance provider. Some important problems to examine in these markets are the following:
- The coalitional stability of the pool, or the formation of risk-sharing networks (subcoalitions) within the pool.
- The Pareto-efficiency of allocations along risk-sharing networks.
- The structure of allocation mechanisms within networks, that is, the mappings that transform feasible allocations into other feasible allocations within networks.
Examining these problems requires an interdisciplinary approach, drawing from economic theory, insurance and actuarial science, game theory, and related fields of applications. It is the aim of this workshop to bring together researchers from various fields, to discuss open problems in the theory of decentralized risk-sharing along networks, as well as potential interdisciplinary approaches to tackle these problems.