Category Archives: Conferences

Mesurer et corriger les biais dans les systèmes d’IA

Ce midi, 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”, dans le cadre d’un séminaire co-organisé par l’Autorité de Contrôle Prudentiel et de Résolution (ACPR/Banque de France) et Télécom Paris. Mon intervention, en ouverture, reviendra sur l’équité dans le contexte de l’assurance [les slides sont disponibles].

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Confidence and Fairness: Scientific Foundations in AI and Risk (mid-May in Paris)

Mid-May, we organize, with the SCOR Foundation for Science a one-day workshop on Confidence and Fairness, Scientific Foundations in AI and Risk. Registrations are now open ! The agenda will be

9:00 registration
9:20 – introduction speach
9:30 – Arthur Charpentier
10:15 coffee break
10:45 – Toon Calders
11:30 – Isabel Valera
12:15 lunch break
13:15 – Jean Michel Loubes
14:00 – Evgeny Chzhen
14:45 – Michele Loi
15:30 coffee break
16:00 – Aurélie Lemmens
16:45 – François Hu and Antoine Ly
17:30 – closing cocktail

Conference at CIMAT, D3

After the colloquium yesterday and our conference diner in a beautiful restaurant, downtown, we are back to work at the “Montréal – Guanajuato Workshop on Probability and Machine Learning“. Third day, with a focus on machine learning. Courtney Paquette (McGill) was our first plenary speaker on ‘High-dimensional Optimization with Applications to Compute-Optimal Neural Scaling Laws’, then Emilien Joly Joly (CIMAT) second plenary speaker on ‘GROS: A Unified Framework for Robust Aggregation in Metric Spaces with Applications to Machine Learning and Statistics’ , Marouane Il Idrissi (UQAM); Wilson Zuniga Galingo (Texas); Juan Jiminez (Ottawa); James Melbourne (CIMAT), our third plenary speaker on ‘Towards optimal privacy mechanisms under estimated sensitivity’; and finally Imanol Nuñez Morales (CIMAT). It is a great, workshop, thanks again to our sponsors (Quantact, SCOR Foundation for Science, CIMAT and the probability lab of Centre de recherches mathématiques (CRM)), thanks to the organizing team (mainly Dante Mata López, without whom nothing would have been possible), the great and enthusiastic speakers we had, and a terrific location…

Agathe at AAAI’25, Philadelphia

Agathe Fernandes Machado is currently in Philadelphia, to present our paper Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness, written with Ewen Gallic

In this paper, we link two existing approaches to derive counterfactuals: adaptations based on a causal graph, and optimal transport. We extend “Knothe’s rearrangement” and “triangular transport” to probabilistic graphical models, and use this counterfactual approach, referred to as sequential transport, to discuss fairness at the individual level. After establishing the theoretical foundations of the proposed method, we demonstrate its application through numerical experiments on both synthetic and real datasets.

Conference at CIMAT, D1

Day 1 at our conference at CIMAT, Guanajauto (Mexico), on “Probability and Machine Learning“. Everyone is here, with Alma Sarai Hernandez Torres (UNAM), first plenary talk on ‘Uniform spanning trees: theory and applications’, Benjamin Côté (Waterloo), Elliot Paquette (McGill), second plenary talk on ‘From magic squares, through random matrices, and to the multiplicative chaos’, Tulio Gaxiola (UAS), Arturo jaramillo Jaramillo (CIMAT), third plenary talk on ‘High-Frequency Statistics for Lévy Processes: A Stein’s Method Perspective’, and Sayle Sigarreta Ricardo (BUAP). Great talks, great day. After flash presentations of posters, and short break before the tacos welcome evening (with posters). A big thank you to the presenters, and to the audience, the room was full all day, with active participation !

Conference at CIMAT, D-1

Pre-conference day at CIMAT, Guanajauto (Mexico), “Probability and Machine Learning“. Finalizing the last details, with the incredible local team, financial services, etc. So far, no last-minute imponderables, fingers crossed. At lunchtime, Hélène Guérin gives a presentation at the probability seminar, and tomorrow morning, the conference begins. Thanks again to our sponsors, SCOR Foundation for Science CIMAT the probability lab of Centre de recherches mathématiques (CRM) and the actuarial science lab, Quantact.

Workshop on Probability and Machine Learning, in Guanajuato, Mexico

Next week, we organize the first Montréal – Guanajuato Workshop on Probability and Machine Learning, at CIMAT, Centro de Investigación en Matemáticas.

with Arturo Jaramillo Gil (CIMAT), Saraí Hernández-Torres (UNAM), Emilien Joly (CIMAT), Sandra Palau (UNAM), Courtney Paquette (McGill), Elliot Paquette (McGill), José Luis Pérez (CIMAT), James Melbourne (CIMAT), and Jean-François Renaud (UQAM), among invited speakers.

The Centro de Investigación en Matemáticas (CIMAT), located in Guanajuato, Mexico, is a leading research institution focused on mathematics, statistics, and computer science. Part of Mexico’s National System of Public Research Centers (CONACYT), CIMAT excels in both theoretical and applied research, fostering innovation and solving complex real-world problems. Its vibrant academic environment supports advanced studies, offering master’s and doctoral programs, while promoting interdisciplinary collaboration. Housed in the picturesque city of Guanajuato, a UNESCO World Heritage Site, CIMAT attracts top researchers and students from around the globe, contributing significantly to scientific and technological advancement in Mexico and beyond.

The first goal of the workshop is to bring together researchers, and scholars from Québec and Mexico in the field of probability theory, and Machine learning. With an emphasis on both theoretical foundations and practical applications, the conference will feature research presentations from speakers who represent a range of career stages at the faculty level. This will foster the exchange of ideas and opportunities for new collaborations.

The second goal was to encourage and promote student mobility between Mexico and Québec. The workshop will feature short talks by graduate students and postdoc fellows, who will have opportunity to present their work, but also to exchange with different researchers. This will enable them to enrich their academic network, and may well open up mobility opportunities for them in the future. Yet, Dante Mata Lopez is sharing the office with the team (Agathe, Marouane), and this summer, two interns will join our team: Allison Lara Nieva, from the Universidad Nacional Autónoma de México (Agathe Fernandes Machado will be involved in the supervision) and Fabian Dominguez Lopez, from the Universidad de Guanajuato, working with Hélène Guérin and Arsène Brice Zotsa Ngoufack, who will be involved in the supervision.

Picture credit for the poster: Yuko Nishikawa (Yuko Nishikawa is a Brooklyn-based Japanese multidisciplinary artist and designer, known for her organic, dreamlike works. She grew up in the seaside town of Chigasaki (茅ヶ崎市), south of Tokyo).

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,

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.

Talk at the 27th International Congress on Insurance: Mathematics and Economics

On Wednesday morning, I will be chairing our session “Discrimination-free Insurance Pricing” at the Insurance: Mathematics & Insurance Conference, in Chicago. With Olivier Côté, Lydia Gabric and Hong Beng Lim, we will be four speaker, just before lunch time. My talk will be a mix of recent work on quantifying and mitigating discrimination in scores (in insurance). Slides are available online.

 

Talk on collaborative insurance, unfairness and discrimination

Monday, I will be giving a short course at the workshop on Decentralized Insurance and Risk Sharing (SAC 161), in Chicago

  • Decentralized Finance and Blockchain: Implications for the Insurance Industry, by Marco Mirabella
  • Decentralized risk sharing: definitions, properties, and characterizations, by Jan Dhaene
  • Collaborative insurance, unfairness, and discrimination, by Arthur Charpentier
  • Decentralized insurance: bridging the gap between industry practice and academic theory, by Runhuan Feng

My slides are available online.

In this course, we will get back to mathematical properties of risk sharing on networks, with reciprocal contrats. We will discuss conditions about stochastic dominance, proving that policyholers might have interest in sharing risks with “friends”.
Then, we will try to adress fairness issues, for such risk sharing mechanisms. If fairness has been recently intensively studied, either through group or individual fairness, there are yet not much litterature about fairness on networks. It is important to adress those issues, since perceived discrimination is usually associated with networks. We will see why the topology of the network is important, both to design peer-to-peer schemes to share risks, but also to see if perceived discrimination is associated with global disparate treatement.