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.
Hier, Olivier Côté avait le droit à une jolie entrevue dans les nouvelles de l’Université Laval (merci au passage à l’université Laval de mettre en avant ses étudiants dans ses pages de nouvelles).
Demain, on présentera avec Olivier et Marie-Pier nos travaux sur l’équité et la discrimination, à l’Autorité des Marchés Financiers, à Québec. L’exposé sera largement inspiré de a fair price to pay: exploiting causal graphs for fairness in insurance
The first newsletter, related to the SCOR research project is now available. It is a brief summary of the first six months,
After four year without it, the WIM is back. Last time, it was in February 2020. Tomorrow, Agathe and Olivier will present posters at the Workshop in Insurance Mathematics, a Concordia University.
Both posters will be on fairness and discrimination. Olivier Côté will present a poster on “Fairness in insurance enigma: exploring the maze of regulation”
Agathe will present a poster on Equipy, “A Python Package for Sequential Fairness using Optimal Transport with Applications in Insurance”
Our paper, a fair price to pay: exploiting causal graphs for fairness in insurance, writen with Olivier Côté and Marie-Pier Côté is now available, on SSRN
In many jurisdictions, insurance companies must not discriminate on some given policyholder characteristics. Omission of prohibited variables from models prevents direct discrimination, but fails to address proxy discrimination, a phenomenon especially prevalent when powerful predictive algorithms are fed with an abundance of acceptable covariates. The lack of formal definition for key fairness concepts, in particular indirect discrimination, hinders the fairness assessment of methodologies. We review causal inference notions and introduce a causal graph tailored for fairness in insurance. Exploiting these, we discuss potential sources of bias, formally define direct and indirect discrimination, and study the properties of fairness methodologies. A novel categorization of fair methodologies into five families (best-estimate, unaware, aware, hyperaware, and corrective) is constructed based on their expected fairness properties. A comprehensive pedagogical example illustrates the practical implications of our findings: the interplay between our fair score families, group fairness criteria, and sources of discrimination.
This morning, I will be giving a talk for the Thelem-ILB Chaire (Thelem – historically compagnie d’assurance mutuelle contre l’incendie dans le département du Loiret – founded in 1820, one of France’s oldest companies), on fairness and discrimination in insurance, and more specifically on counterfactual fairness, and causal graphs. It is based on recent work with Olivier Côté, our PhD student in Laval (Québec), co-supervised with Marie-Pier,
Slides are available online. I can mention here that we obtained a grant from the Thelem-ILB Chaire to sponsor a trip in Europe for Olivier, to attend the Insurance Data Science conference in London, and the 26th International Congress on Insurance: Mathematics and Economics in Edinburgh.
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 !
Tonight (Montréal time), Marie-Pier Côté will give a talk on “a fair price to pay: exploiting directed acyclic graphs for fairness in insurance” based on recent joint work with our PhD student, Olivier Côté, in Melbourne, Australia
Many jurisdictions have laws or guidelines stipulating that insurance companies must not discriminate on some specified policyholder characteristics. Omission of the prohibited variables from the models removes direct discrimination, but does not prevent proxy discrimination — a phenomenon especially prevalent when powerful predictive algorithms are fed with an abundance of allowed covariates. In the actuarial literature, there remains some confusion on the definition of indirect discrimination: this impedes the understanding of the goals of each fairness methodology and their comparison. In the causal inference literature, many tools, such as directed acyclic graphs (DAGs), help uncover various types of biases. A DAG describes the causal relationships between variables of interest and has clear dependence implications. We exploit this tool for fairness to formally define direct and indirect discrimination, to discuss potential sources of bias, and to understand the properties of different fairness methodologies. Four families of fair scores (best-estimate, unaware, aware and corrective) are placed in the DAG representing the insurance pricing problem. This allows us to study their behaviour in terms of direct and indirect discrimination. A comprehensive pedagogical example illustrates our findings.
More to come soon…
Next week, the Insurance Data Science Conference will take place at the Bayes Business School. The conference programme and abstract booklet are now online. Philipp and Olivier will present some recent work (unfortunately, I will not be able to attend, I was in already in London last month)
Olivier will present some recent work on Causal Inference and Fairness in Insurance Pricing
Thursday and Friday, we organize, with Olivier, Debrah and Christoph a small workshop, at Université de Rennes 1, on Multiattribute Utility and Risk. The workshop will take place place Hoche, at the Economics faculty. Speakers are
In June, with Olivier L’Haridon, we will organize a (small) conference, in Rennes, on risk models in a multi-attribute framework. In order to fully enjoy the workshop (more to come on the blog), we organized this year an internal workshop on that topic. A gave an oveview in September on multivariate distributions, with an emphasis on spherical / elliptical distributions, distributions on the simplex, and copulas. This time, following recent presentations made by Olivier, I will present Ali E. Abbas (recent) contributions on copula-type multriattribute utility functions. Slides are online, and the presentation will be this Thursday
As discussed in the introduction, one (nice) application can be the choice of a seat in a theatre, see
As discussed already, in June 2016, with Olivier L’Haridon, we will organize a (small) conference, in Rennes, on risk models in a multi-attribute framework. Related to that conference, we have a working group on related topics. At the end of September, I gave a brief survey on multivariate distributions. And yesterday, Olivier gave the first part of survey on multivariate decision making. The second part will be in two weeks,
In June 2016, with Olivier L’Haridon, we will organize a (small) conference, in Rennes, on risk models in a multi-attribute framework. In order to fully enjoy the workshop (more to come on the blog), we will organize every month an internal workshop on that topic. We will start tomorrow afternoon, 13:00-14:30, and I will give a brief talk on multivariate distributions, with an emphasis on spherical / elliptical distributions, distributions on the simplex, and copulas. Slides are now online,
