Category Archives: Books

Insurance, Biases, Discrimination and Fairness

Insurance, Biases, Discrimination and Fairness was published a few weeks ago. I still plan to spend some time this summer on the R package, including data and some functions…

This book offers an introduction to the technical foundations of discrimination and equity issues in insurance models, catering to undergraduates, postgraduates, and practitioners. It is a self-contained resource, accessible to those with a basic understanding of probability and statistics. Designed as both a reference guide and a means to develop fairer models, the book acknowledges the complexity and ambiguity surrounding the question of discrimination in insurance. In insurance, proposing differentiated premiums that accurately reflect policyholders’ true risk—termed “actuarial fairness” or “legitimate discrimination”—is economically and ethically motivated. However, such segmentation can appear discriminatory from a legal perspective. By intertwining real-life examples with academic models, the book incorporates diverse perspectives from philosophy, social sciences, economics, mathematics, and computer science. Although discrimination has long been a subject of inquiry in economics and philosophy, it has gained renewed prominence in the context of “big data,” with an abundance of proxy variables capturing sensitive attributes, and “artificial intelligence” or specifically “machine learning” techniques, which often involve less interpretable black box algorithms.

The book distinguishes between models and data to enhance our comprehension of why a model may appear unfair. It reminds us that while a model may not be inherently good or bad, it is never neutral and often represents a formalization of a world seen through potentially biased data. Furthermore, the book equips actuaries with technical tools to quantify and mitigate potential discrimination, featuring dedicated chapters that delve into these methods.

Quantifying Fairness and Discrimination in Predictive Models

The article Quantifying Fairness and Discrimination in Predictive Models was just published in Machine Learning for Econometrics and Related Topics, Springer.

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 [69], “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 [61]. 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.

Fresh from the oven…

14 litres d’encre de chine, 30 pinceaux, 62 crayons à mine grasse, 1 crayon à mine dure, 27 gommes à effacer, 38 kilos de papier, 16 rubans de machine à écrire, 2 machines à écrire, 67 litres de bière ont été nécessaires à la réalisation de cette aventure…

(Goscinny and Uderzo (1965*), Astérix et Cléopâtre)

Almost better than hot, freshly baked bagels…

the textbook Insurance, Biases, Discrimination and Fairness is now out, and just arrived today ! Even though I’ve spent so much time re-reading it, getting nauseous, checking references, quotes, reworking graphics, re-launching codes, etc., it’s still an immense feeling of pride to open your book for the very first time.

Astérix et Cléopâtre est le dernier Astérix de la fameuse Collection Pilote, comme me le rappelait Michel Bera (professeur émérite du CNAM, rattaché à la Chaire de modélisation statistique du risque, et mémoire de la bande dessinée francophone, le “B” du fameux “BDM”, Trésors de la bande dessinée). “Lorsque la collection Pilote a basculé en éditions avec les titres des seuls Asterix dans le menhir, je pense que la phrase a disparu“… C’était la version qui était chez mes grands parents, et que je (re)dévorais, tous les ans, quand j’étais petit.

Non Life Insurance Mathematics

A long time ago, with Michel Denuit, we published a long textbook (in two volumes), in French, entitled “Mathématiques de l’Assurance Non-Vie”, or “Non Life Insurance Mathematics” in English. We finally decided to translate everything in English, and our textbook is now available at https://nonlifemaths.github.io/. It is a simple plain translation of those populare textbooks… the next step is maybe to update them a little bit…

Discussions autour du Manuel d’Assurance, partie 3

Après la discussion il y a deux semaines, avec Gilles, et la semaine avant, avec Patrick, je mets en ligne un dernier billet, reprenant un échange que nous avions, tous les trois, avec Patrick Thourot et Gilles Bénéplanc, l’automne dernier, et l’équipe d’Annie (qui s’occupe de formation au sein du groupe Séroni, et News Assurances Pro) pour parler du Manuel d’Assurance,

Le livre est toujours en ligne sur le site des PUF.

Discussions autour du Manuel d’Assurance, partie 2

La semaine passée, j’avais mis en ligne un premier post, reprenant une discussion que nous avions, avec Patrick Thourot et Gilles Bénéplanc, l’automne dernier, avec l’équipe d’Annie (qui s’occupe de formation au sein du groupe Séroni, et News Assurances Pro) pour parler du Manuel d’Assurance,

Petite information au passage… les PUF ont mis en vente la version électronique (non disponible dans les premiers mois qui ont suivi la parution du livre, étrangement…)

Insurance, biases, discrimination and fairness, v2

In the Summer 2022, my report Insurance, biaises, discrimination and fairness (v1) was officially uploaded on the website of the Institut Louis Bachelier. I have spent another year to add illustrations and examples, and I sent the manuscript to the publisher at the beginning of the Summer 2023. Because of delays, the book is not out yet, but the publisher allowed me to upload Insurance, biaises, discrimination and fairness v2 of the document. Note that it will be the lecture notes of the doctoral course I will give this Winter at ENSAE, in Paris, France.

The R functions (and package) will be uploaded on https://github.com/freakonometrics/InsurFair soon.

Le site du Manuel d’Assurance

Le Manuel d’Assurance, annoncé pour la rentrée, est officiellement publié 🍾 Le manuel a été co-écrit avec Gilles Bénéplanc et Patrick Thourot, et le site associé est en ligne, manueldassurance.github.io

On a mise en ligne les figures du livres (les images qui apparaissent sont en faible résolution, et en cliquant, la figure en (très haute) résolution est disponible – avec un fond transparent, pour s’adapter facilement sur des slides)

et on va rajouter du matériel au fur et à mesure

Méthodes de prévision en finance (et copules)

Le livre Méthodes de prévision en finance est sorti, en France, cette semaine. L’application que je propose pour montrer la modélisation par copules (par opposition aux modèles GARCH multivariés en particulier) est basée sur un vieux billet sur le blog (ou les codes permettent de reproduire l’analyse que j’en fait), remis en forme dans un court article, Prévision avec des copules en finance