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Natural disasters, avoidable or unpredictable?

We’ve all seen the images. And they are incredible. Like many natural disasters.

On 25 January, Niscemi, a small Sicilian town, split in two, after the torrential rains associated with Storm Harry, leaving a scar several kilometres long. We saw roads collapsing, cars vanishing, houses left hanging on the edge of a void. More than 1,600 people were evacuated. In the Guardian article, we are reminded that this was not the first time. We are told that the very same area had already slid in the 18th century, and again in 1997, and yet construction continued, especially from the 1950s and 1960s onwards.

Could we have known? Did we already know? And if we did, why does it still happen? Almost inevitably, after every natural disaster, I find that these questions come back too, again and again… I thought I could write a short post to recall that, on the one hand, there are striking regularities in the way some disasters unfold. But on the other hand, “predictable” does not mean “avoidable”, because disaster is also a social fact, a story of vulnerability, exposure, and public choices (at the risk of repeating myself, I know).
Continue reading Natural disasters, avoidable or unpredictable?

A Three-Pillar Governance Framework for Fair Insurance Pricing

Our joint paper, with Olivier Côté and Marie-Pier Côté, A Three-Pillar Governance Framework for Fair Insurance Pricing, is now available on SSRN,

Insurance is built on balancing individual risk with collective protection, yet contemporary black box data-driven pricing models raise pressing questions of fairness with respect to pre-specified protected attributes. While actuarial standards target risk-based accuracy, stakeholders increasingly demand ethical accountability, social solidarity, and protection against hidden unfair discrimination. We situate these pillars within broader debates in business ethics and algorithmic fairness, linking them to traditions of distributive justice (Rawls, 1971; Sen, 1992), the ethics of information (Floridi, 2016; Nissenbaum, 2009), and risk-sharing theory (Arrow, 1963). We argue that the three pillars make transparent and explicit the ethical trade-offs actuaries and insurers face when deploying predictive models. No single fairness principle can dominate without undermining others: actuarial fairness may exacerbate socioeconomic disparities, solidarity may impair market efficiency, and causality, while seeking genuine risk effects apart from solidarity or actuarial fairness, relies on unverifiable assumptions that may hinder predictive power. By articulating this three-dimensional framework, we shift fairness from an implicit assumption to an explicit governance objective, thereby providing a normative perspective for corporate governance, regulatory design, and stakeholder accountability in the insurance industry. Beyond actuarial science, these three pillars offer a generalizable framework for assessing fairness in other areas of risk-based algorithmic decision-making, from credit scoring to healthcare pricing.

Catastrophes naturelles, catastrophes évitables ou imprévisibles ?

On a tous vu les images. Et elles sont incroyables. Comme de nombreuses catastrophes naturelles.

Le 25 janvier, Niscemi, petite ville sicilienne, s’est ouvert en deux, suite aux pluies torrentielles associées au cyclone Harry, avec une cicatrice de plusieurs kilomètres. On a vu les routes qui s’effondrent, les voitures qui disparaissent, les maisons qui restent suspendues au bord d’un vide. Plus de 1 600 personnes ont été évacuées. Dans l’article du Guardian, on nous rappelle que ce n’est pas la première fois. On nous explique que le même secteur avait déjà glissé au XVIIIe siècle, puis à nouveau en 1997, et pourtant les constructions ont continué, notamment à partir des années 1950 et 1960.

Est-ce qu’on aurait pu savoir ? Est-ce qu’on savait déjà ? Et si on savait, pourquoi ça arrive quand même ? Mine de rien, à chaque catastrophe naturelle, j’ai souvent que ces questions reviennent elles aussi, invariablement… Je me disais que je pourrais écrire un court billet, rappelant que d’un côté, il existe des régularités étonnantes dans la façon dont certaines catastrophes se déclenchent. Mais pour autant, de l’autre, “prévisible” ne veut pas dire “évitable”, parce que la catastrophe est aussi un fait social, une histoire de vulnérabilité, d’exposition et de décisions publiques (au risque de me répéter, je sais).
Continue reading Catastrophes naturelles, catastrophes évitables ou imprévisibles ?

From Premium to Contribution: Recovering Solidarity

This post was originally written and published in French, De la prime à la cotisation: retrouver la solidarité

Well, let’s start with something everyone can observe. Insurance has a bad image, a bad press. It is often suspected of being a cold bureaucracy, a paperwork industry, a partner that looks for loopholes precisely when you need it. We talk about premiums the way we talk about a price, and we end up judging insurance the way we judge a purchase. Did I “get my money’s worth” this year. Did I “lose” money if I had no claim. Was I a good customer if I kept quiet. With questions like these, the very idea of solidarity quickly feels out of place.

And yet, if we set aside the forms and the marketing campaigns, insurance is first and foremost a social technology. It makes a simple reality livable. Some events are rare, hit hard, and cannot be financed individually without tipping into ruin. Insurance says the following. We do not know when, we do not know who, we only know that one day someone will have an accident, fall ill, see their home damaged, or cause harm to someone else. And because we cannot decide in advance who that person will be, we choose to be many to carry the associated financial burden. We pool a small share of our resources, and we agree on rules so that, when the day comes, the burden is bearable. For everyone. It is not only a service, it is a common.

I had already tried to look at the issue from a concrete angle, the angle of claims settlement, and the way it can disenchant a tool that is, in principle, virtuous, in an earlier post When insurance falls apart, the silent crisis of claims settlement. And there are other posts too, revolving around pricing, perception, and what we truly expect from an insurance contract, such as The value of life, The paradoxes of segmentation and discrimination in insurance, Cheaper personalized insurance premiums thanks to AI, or Insurance, a zero sum game. But what was missing was a broader lens, more sociological, and probably more political too. And as often, going back to classic works in the social sciences helps clarify what is at stake.

Continue reading From Premium to Contribution: Recovering Solidarity

De la prime à la cotisation: retrouver la solidarité

Bon, commençons par un constat que tout le monde peut faire. L’assurance a mauvaise image, mauvaise presse. On la soupçonne d’être une bureaucratie froide, une industrie de paperasse, un partenaire qui cherche l’échappatoire au moment où l’on en a besoin. On parle de primes comme on parlerait d’un prix, et on finit par juger l’assurance comme on juge un achat. Ai-je “rentabilisé” mon contrat cette année ? Ai-je “perdu” de l’argent si je n’ai pas eu de sinistre. Ai-je été un bon client si je suis resté discret ? Avec ce genre de questions, l’idée même de solidarité paraît vite hors sujet.

Pourtant, si l’on met de côté les formulaires et les campagnes marketing, l’assurance est d’abord une technologie sociale. Elle sert à rendre vivable une réalité simple. Certains événements arrivent rarement, frappent fort, et ne sont pas finançables individuellement sans basculer dans la ruine. L’assurance dit ceci. On ne sait pas quand, on ne sait pas qui, on sait seulement qu’un jour quelqu’un aura un accident, tombera malade, verra sa maison endommagée, ou causera un tort à autrui. Et comme on ne peut pas décider à l’avance qui sera cette personne, on décide d’être plusieurs à porter le poids financier associé. On met en commun une petite part de nos moyens, et on s’accorde sur des règles pour que, le jour venu, la charge soit supportable. Pour toutes et tous. Ce n’est pas seulement un service, c’est un commun.

J’avais déjà essayé de regarder le problème par un angle concret, l’angle de l’indemnisation, et de la manière dont elle peut désenchanter un outil a priori vertueux, dans mon ancien billet Quand l’assurance se défait, la crise silencieuse de l’indemnisation. Et puis il y a d’autres billets, qui tournent autour de la tarification, de la perception, et de ce que l’on attend vraiment d’un contrat d’assurance, comme La valeur de la vie, Les paradoxes de la segmentation et de la discrimination en assurance, Des primes d’assurance personnalisées moins chères grâce à l’IA ?, ou encore L’assurance, un jeu à somme nulle?. Mais il manquait une grille de lecture plus large, plus sociologique, plus politique probablement. Et comme souvent, se replonger dans des classiques des sciences sociales aide à voir ce qui est en jeu.
Continue reading De la prime à la cotisation: retrouver la solidarité

Criminal Records, the Right to be Forgotten, and Inclusive Insurance

I just uploaded an article, “Criminal Records, the Right to be Forgotten, and Inclusive Insurance: Risk, Rehabilitation, and Data Governance in Underwriting“, written with David Schraub,

Criminal history information (CHx) is increasingly used in insurance screening and underwriting, yet it remains under-theorized in the inclusive insurance literature despite its potential to widen protection gaps through quote-gating, opaque third-party data pipelines, and limited avenues for correction. This article argues that criminalhistory underwriting is a joint problem of risk governance and information governance. Criminal justice information is time-sensitive and legally structured, but automated decision systems and vendor supply chains can operationalize it as durable stigma, particularly when records persist across brokers, archives, and derivative products that fail to reflect status changes such as sealing, expungement, or spentness. The article maps where CHx can enter the insurance lifecycle (pre-quote funnels, underwriting/pricing, claims and renewal) and identifies inclusion-relevant failure modes linked to provenance, data quality, update lag, and weak procedural safeguards. It then develops a conceptual framework distinguishing defensible risk relevance from digital punishment, and translates that framework into implementable mechanisms for more inclusive practice: redemption-by-design (time windows and decaying weights), data minimization and purpose limitation grounded in marginal predictive value, auditable vendor governance, and due-process safeguards providing actionable notice, explanation, and rapid correction. The article concludes with a governance standard for regulators and insurers: the key question is not whether CHx is used, but whether any use is demonstrably incremental, time-bounded, contestable, and governable across the data supply chain.

Correlation, Causation, Circumstance, Context

This post was originally written and published in French, Corrélation et causalité, circonstance et contexte

Let’s imagine we collected a few pieces of information, for a clearly identified individuals, via connected devices,

  • Thursday 18:30: a €45 purchase at a bar-tabac
  • Friday 13:15, 1 hour above Porte Saint-Martin
  • Saturday 14:00, 1 hour near Place de la République

Trying to make the data “speak”, we might hesitate between a first version

  • Thursday 18:30: a €45 purchase of cigarettes
  • Friday 13:15, 1 hour at the mosque, at prayer time
  • Saturday 14:00, 1 hour in a demonstration that started from Place de la République in the early afternoon

and a second version

  • Thursday 18:30: a €45 purchase of tax stamps
  • Friday 13:15, 1 hour at the gym
  • Saturday 14:00, 1 hour at the hairdresser’s, Place de la République

In other words: with the very same raw signal, we can build two perfectly coherent stories. This is not an artistic trick à la Sophie Calle; it is the standard situation as soon as we work with traces (geolocation, timestamps, spending, calls, driving). Data do not necessarily lie. But they underdetermine the narrative, because they allow several possible worlds. In insurance (and more broadly in the economy of prediction), we are used to taking correlations and turning them into decisions—not only to “understand”, but to price, classify, accept, refuse. The point is not to say this is illegitimate in principle. The point is to recall what we do when we only correlate, and what we forget when we have no context.

Continue reading Correlation, Causation, Circumstance, Context

Corrélation et causalité, circonstance et contexte

Imaginons qu’on ait collecté, pour une personne clairement identifiée, quelques informations sur Paris, via des objets connectés,

  • Jeudi 18:30 : achat pour 45€ dans un bar-tabac
  • Vendredi 13:15, 1h au dessus de la Porte Saint Martin
  • Samedi 14:00, 1h  proche de la Place de la République

En essayant de faire parler les données, on hésite entre une première version

  • Jeudi 18:30 : achat pour 45€ de cigarettes
  • Vendredi 13:15, 1h à la mosquée, à l’heure de la prière
  • Samedi 14:00, 1h  dans une manifestation qui partait de la Place de la République en début d’après midi

et une seconde version

  • Jeudi 18:30 : achat pour 45€ de timbres fiscaux
  • Vendredi 13:15, 1h au club de sport
  • Samedi 14:00, 1h  chez le coiffeur, place de la République

Autrement dit, avec un même signal brut, on a deux histoires parfaitement cohérentes. Ce n’est pas une pirouette artistique à la Sophie Calle, c’est la situation standard dès qu’on manipule des traces (géolocalisation, heures, dépenses, appels, conduite). Les données ne mentent pas forcément. Mais elles sous-déterminent le récit puisqu’elles autorisent plusieurs mondes possibles. En assurance (et plus largement dans l’économie de la prédiction), on a l’habitude de prendre des corrélations pour en faire des décisions. Pas seulement pour “comprendre”, mais pour tarifer, classer, accepter, refuser. Le point n’est pas de dire que c’est illégitime par principe. Le point est de rappeler ce qu’on fait quand on se contente de corréler, et ce qu’on oublie quand on n’a pas de contexte.

Continue reading Corrélation et causalité, circonstance et contexte

Insurance, biases, discrimination and fairness

Almost two years ago, “Insurance, Biases, Discrimination and Fairness” was published.  A quick note: the book was initially based on a (long) report published by the Institut Louis Bachelier, in both French and English (with links on my blog)
☐ French : https://freakonometrics.hypotheses.org/63834
☐ English : https://freakonometrics.hypotheses.org/63866
Since these links (on the ILB website) are now dead (and I can’t find the reports on the website anymore – 404 error), I re-uploaded both versions — people often ask me for them.
☑︎ Report (EN): https://freakonometrics.hypotheses../..Gb.pdf
☑︎ Report (FR): https://freakonometrics.hypotheses../..Fr.pdf
(Much more material can be found on the blog…)

Il y a bientôt deux ans paraissait l’ouvrage “Insurance, Biases, Discrimination and Fairness”. Je précise qu’il s’appuyait initialement sur un (long) rapport publié par l’Institut Louis Bachelier, en français et en anglais (j’en avais alors parlé sur mon blog) :
☐ Français : https://freakonometrics.hypotheses.org/63834
☐ Anglais : https://freakonometrics.hypotheses.org/63866
Les liens sur le site de l’ILB étant désormais inaccessibles (et ne retrouvant plus le rapport sur le site), j’ai remis en ligne les deux versions — on me les demande souvent.
☑︎ Rapport (EN) : https://freakonometrics.hypotheses../..Gb.pdf
☑︎ Rapport (FR) : https://freakonometrics.hypotheses../..Fr.pdf
(Et au passage, beaucoup d’autres ressources sont disponibles sur mon blog…)

Balance and Calibration of Probabilistic Scores: From GLM to Machine Learning

Tomorrow, I will give a talk on “Balance and Calibration of Probabilistic Scores:“” From GLM to Machine Learning” at Singapore campus – ESSEC Asia-Pacific. The abstract is

This study evaluates binary classifier performance with a focus on calibration, which is often overlooked by traditional metrics like accuracy. In high-stakes domains such as finance and healthcare, well-calibrated probabilities are crucial. We highlight the limitations of standard calibration metrics, particularly under score distortions and heterogeneous distributions. To address this, we introduce the Local Calibration Score and advocate optimizing models using Kullback-Leibler (KL) divergence to better align predicted scores with true probabilities. Our approach emphasizes balancing global and local calibration, ensuring overall distributional alignment while maintaining reliability across different score ranges. Using Random Forest and XGBoost across diverse datasets, we show that KL-based tuning improves calibration without sacrificing performance. Our results reveal that relying solely on traditional metrics can mislead model assessment, especially in sensitive decision-making scenarios. This is some joint work with Agathe Fernandes Machado and Ewen Gallic.

Modeling and Understanding Indirect Discrimination in Algorithmic Fairness

In a couple of days, I will give a talk on “Modeling and Understanding Indirect Discrimination in Algorithmic Fairness” at Singapore campus – ESSEC Asia-Pacific. The abstract is

Observed disparities between groups in algorithmic decisions (whether in hiring, credit approval, or risk prediction) do not necessarily imply direct discrimination. They may also stem from legitimate differences in the distribution of explanatory attributes. Understanding and quantifying which components of these gaps are “explained” versus those that reflect direct or indirect discrimination lies at the core of modern causal approaches to algorithmic fairness. This talk will begin with an accessible introduction to group-gap decomposition, building on the classical Kitagawa–Oaxaca–Blinder econometric framework. This approach separates differences attributable to observable characteristics from residual components that may signal discriminatory effects. The second part will introduce recent developments leveraging optimal transport to construct individual-level counterfactuals, enabling estimation of direct and indirect causal effects for each observation. In particular, we will show how sequential transport mappings aligned with a causal graph can disentangle pathways and quantify the contribution of each mediator. This methodology overcomes limitations of traditional linear models, introduced by Kitagawa, Oaxaca and Blinder, provides interpretable counterfactuals, and is well suited to complex empirical settings. The presentation will combine intuitive motivation, illustrative examples, and recent research insights, with the goal of making these tools accessible and useful to researchers in management science, applied economics, and data science.

Talk at NTU (Nanyang) in Singapore

Tomorrow, I will be at Nanyang Technological University to give a talk at an internal seminar, “Fairness and discrimination in insurance

What’s unique about insurance is that even statistical discrimination, which by definition is devoid of malicious intent, poses significant challenges. Because, on the one hand, policymakers would like insurers to treat their policyholders equally, without discrimination based on race, gender, age or other characteristics, even if it could make (statistical) sense to (indirectly) discriminate. On the other hand, at the core of actuaries’ activities lies discrimination, between risky and non-risky policyholders. And this risk is often statistically correlated with sensitive characteristics that regulation would like to prohibit insurers from taking into account. The analysis of possible discrimination in decision rules, whether human or algorithmic, is an old subject. Most of the concepts date back at least to the 50s, but recent developments in artificial intelligence have brought these issues back into the spotlight. Massive data facilitate statistical or proxy discrimination, and black-box algorithms do not facilitate understanding. Not to mention the various regulations that make it difficult to collect sensitive information, and ultimately test whether decisions can be discriminated against, especially indirectly.

The talk is based on the textbook Insurance, Biases, Discrimination and Fairness, as well as recent papers, arXiv:2511.11294 (AAAI’26), arXiv:2408.03425 (AAAI’25), arXiv:2309.06627 (AAAI’24) and arXiv:2306.12912  (ECML’24).

Buzy week in Singapore

It has been a buzy week at the 40th Annual AAAI Conference on Artificial Intelligence, here in Singapore where Bertille Tierny and François Hu will give talks (in the “main track”, in the “student track”, in a workshop) to present our recent work, “Decomposing Direct and Indirect Biases in Linear Models under Demographic Parity Constraint“. More to come very soon…

En faire des tonnes pour quelques grammes

Même s’il me démange depuis des mois (des années?), j’écris ce billet un peu à reculons. Il me gêne, il me tire par la manche, et je le repousse depuis. D’abord parce qu’il est inconfortable. Ensuite parce qu’il touche à une problème où le monde universitaire est étrangement incroyablement polarisé. L’avion (dans un contexte de greenwashing académique). D’un côté, j’ai des collègues qui ont arrêté de prendre l’avion. Vraiment. Plus de déplacements depuis des années. Et ils continuent à faire une recherche de qualité, à encadrer admirablement des étudiants, à faire vivre des collectifs. Ils ont pris la mesure de ce que signifie le changement climatique, de la vitesse vertigineuse à laquelle on fonce dans le mur. Je les admire, en un sens. Ils ont tranché, sans s’excuser, sans “… mais”. De l’autre côté, j’ai des collègues qui ne cessent de voyager. Des allers retours transatlantiques pour un séminaire d’une heure. Un workshop d’une journée la semaine suivante. La certitude tranquille que c’est le prix de l’excellence. Pour eux, se poser la question de l’avion, c’est presque un contresens. Si l’on commence à s’attaquer aux chercheurs, autant dire qu’on a perdu de vue les vrais leviers, les vraies émissions, les vrais coupables. Il y a mille autres manières, disent ils, de faire baisser les émissions, avant de venir embêter la recherche. Et puis il y a moi, entre les deux. J’aimerais pouvoir écrire que j’ai une règle simple. Ce n’est pas le cas. J’ai beau être un ours assez asocial, j’aime voyager. J’aime ces semaines de workshop dans un lieu isolé où l’on discute du matin au soir, où quelqu’un peut débloquer un problème en dénichant un article que j’avais manqué. J’aime ces moments où la recherche avance grâce à des gens qui se sont trouvés dans la même pièce, ou à la même terrasse, au bon moment. Mais j’ai mauvaise conscience. Et comme souvent, elle revient au moment où il faut boucler la valise et télécharger la carte d’embarquement. Demain, je dois prendre l’avion. Alors j’en profite pour finir ce billet commencé il y a des mois, et jamais fini… D’abord pour rappeler quelques faits, et décrire nos pratiques à ceux qui ne vivent pas dans le milieu académique, où partir à une conférence dans des endroits exotiques est devenu un geste presque anodin. Mais surtout pour ouvrir la discussion, pour récolter d’autres témoignages, pour entendre parler de labos et d’équipes qui ont des chartes, des principes, découvrir des collectifs qui ont trouvé des compromis intelligents, de collègues qui inventent des solutions originales. Je n’ai pas envie d’écrire un billet qui se termine par “ben, je fais ce que je peux“. J’aimerais plutôt comprendre comment, concrètement, on peut faire mieux. Continue reading En faire des tonnes pour quelques grammes

"sendo l'intento mio scrivere cosa utile a chi la intende…"