Category Archives: Conferences

Fairness in Multi-Task Learning via Wasserstein Barycenters, at ECML PKDD 2023

Toady, presentation of our paper Fairness in Multi-Task Learning via Wasserstein Barycenters, ECML PKDD, in Torino, by François. Slides are available online (and a poster can be found below)

The paper was actually published in Machine Learning and Knowledge Discovery in Databases: Research Track (295–312), available here.

Mitigating Discrimination in Insurance with Wasserstein Barycenters

Our new paper, with François Hu and Philipp Ratz, Mitigating Discrimination in Insurance with Wasserstein Barycenters is now available on ArXiv.

The insurance industry is 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 this article, we first investigate why predictions are a necessity within the industry and why correcting biases is not as straightforward as simply identifying a sensitive variable. We then propose to ease the biases 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.

(fictitious maps used in the article)

Actuarial Science Workshop on 2023 SSC Meeting in Ottawa

This Sunday, I will be presenting at the Actuarial Science Workshop, a 2023 SSC (Statistical Society of Canada) Annual Meeting in Ottawa. It is organized by Jun Cai (Waterloo), Ben Feng (Waterloo), Emiliano Valdez (University of Connecticut) and Xiaofei Shi (UofT) will also be there. It will be on optimal transport, in the context of fairness and discrimination, in insurance. Slides are now available.

Talk at the seminar, at Waterloo (Ontario, Canada)

This week, I will be working in Waterloo, and give a talk on on Causal Inference and Counterfactuals with Optimal Transport, With Applications in Fairness and Discrimination (based on our paper on Optimal Transport for Counterfactual Estimation, with Emmanuel Flachaire and Ewen Gallic, as well as a more recent one on Parametric Fair Projection with Statistical Guarantees, with François Hu and Philipp Ratz).


This week, Sam will be in Valencia (Spain) to present our work on Data Augmentation for Imbaladed Regression

In this work, we consider the problem of imbalanced data in a regression framework when the imbalanced phenomenon concerns continuous or discrete covariates. Such a situation can lead to biases in the estimates. In this case, we propose a data augmentation algorithm that combines a weighted resampling (WR) and a data augmentation (DA) procedure. In a first step, the DA procedure permits exploring a wider support than the initial one. In a second step, the WR method drives the exogenous distribution to a target one. We discuss the choice of the DA procedure through a numerical study that illustrates the advantages of this approach. Finally, an actuarial application is studied.

Workshop “Machine Learning and Data Mining in Insurance and Finance ” at the SSC 2023

At the end of May, on Sunday May 28th, I will participate to the workshop Machine Learning and Data Mining in Insurance and Finance, at the Statistical Society of Canada Annual Meeting

Machine learning and data mining are hot research topics in insurance and finance and are heavily used in the industry of insurance and finance as well. In this workshop, the four invited speakers will introduce methods of machine learning and data mining and discuss their applications in insurance and finance. Each of the four speakers has 60 minutes for the presentation. There in a ten-minute break after a presentation. The total duration of the workshop is four and half hours.

Quantifying fairness and discrimination in predictive models 

In about ten days, late in the evening (Montréal time), I will attend the 16th Annual Conference of Thailand Econometric Society (on Machine Learning for Econometrics and Related Topics), at Chiang Mai University (มหาวิทยาลัยเชียงใหม่). I will give a talk (introductionary talk, at 21:30 pm, with the jet lag) on quantifying fairness and discrimination in predictive models (the state-of-the-art paper I will present is online on arXiv) and the slides are now also available (I won’t be able to go to Chiang Mai, unfortunately, and I will be on zoom).

The analysis of discrimination has long interested economists and lawyers. In recent years, the literature in computer science and machine learning has become interested in the subject, offering an interesting re-reading of the topic. These questions are the consequences of numerous criticisms of algorithms used to translate texts or to identify people in images. With the arrival of massive data, and the use of increasingly opaque algorithms, it is not surprising to have discriminatory algorithms, because it has become easy to have a proxy of a sensitive variable, by enriching the data indefinitely. According to Kranzberg (1986), “technology is neither good nor bad, nor is it neutral”, and therefore, “machine learning won’t give you anything like gender neutrality `for free’ that you didn’t explicitely ask for”, as claimed by Kearns et a. (2019). In this article, we will come back to the general context, for predictive models in classification. We will present the main concepts of fairness, called group fairness, based on independence between the sensitive variable and the prediction, possibly conditioned on this or that information. We will finish by going further, by presenting the concepts of individual fairness. Finally, we will see how to correct a potential discrimination, in order to guarantee that a model is more ethical

Infectious Disease Modeling Colloquium

Save the date: Infectious Disease Modeling Colloquium, at Université de Montréal, on Jan 27, 2023, organized by CRM & Fields Institute. For registration, fill out the form (in French or in English). If you are a student/postdoc and want to give a talk/poster, pls fill out the form/submit an abstract. Partial financial support is available. Thanks Bouchra Nasri for the organization ! Jacques Bélair, Morgan Craig, Hélène Guérin (and myself) will also be involved.

Assurance collaborative, théorie des graphes et actuariat

Mardi prochain, j’interviendrais (en visio) au colloque SCOR sur le thème “Actuariat, effets réseaux et théorie des graphes

Mes slides (on m’a demandé de parler sur le thème assurance collaborative, théorie des graphes et actuariat) sont en ligne, je présenterai notre papier Collaborative Insurance Sustainability and Network Structure, mais je peux en profiter pour mentionner d’autres articles, dont modéliser la contagion, ou les réseaux pour réinventer l’assurance?