In about ten days (Saturday afternoon), I will be presenting a poster on fairness, discrimination and insurance at the Montréal AI Symposium, based on our joint paper The Fairness of Machine Learning in Insurance: New Rags for an Old Man?, written with Laurence Barry. Since the paper was quite literary, I used material from the document Insurance: Discrimination, Biases & Fairness to get more a visual poster. Additional information will come while discussing…
The report Insurance, biaises, discrimination and fairness is now officially online on the website of the Institut Louis Bachelier.
This post was written with Laurence Barry and Ewen Gallic, in French, in November 2019 (see hal-02350006)
Insurance policies are classic examples of random contracts. This forces insurers to regularly quantify this uncertainty, to calculate probabilities in order to propose “fair” premiums for the commitments they are going to make. Isn’t it time to question this practice, at a time when artificial intelligence is exploding, offering predictive algorithms of a precision never seen before? At a time when big data / big brother could mean the disappearance of uncertainty itself?
Continue reading What is the future of predictive probabilities in insurance?
On Friday May 13th, we are organizing with Marie-Pier Côté a workshop on fairness and discrimination in insurance.
This Friday, it is our semester break in Montréal, so I will be giving a talk at the University of Illinois Urbana-Champaign, on fairness and discrimination in actuarial pricing. As mentioned in the abstract, the talk will be based on two recent papers, The Fairness of Machine Learning in Insurance: New Rags for an Old Man? and A fair pricing model via adversarial learning
Slides are now online.
At the core of insurance business lies classification between risky and non-risky insureds, actuarial fairness meaning that risky insureds should contribute more and pay a higher premium than non-risky or less-risky ones. Actuaries, therefore, use econometric or machine learning techniques to classify, but the distinction between a fair actuarial classification and “discrimination” is subtle. For this reason, there is a growing interest about fairness and discrimination in the actuarial community Lindholm, Richman, Tsanakas, and Wuthrich (2022). Presumably, non-sensitive characteristics can serve as substitutes or proxies for protected attributes. For example, the color and model of a car, combined with the driver’s occupation, may lead to an undesirable gender bias in the prediction of car insurance prices. Surprisingly, we will show that debiasing the predictor alone may be insufficient to maintain adequate accuracy (1). Indeed, the traditional pricing model is currently built in a two-stage structure that considers many potentially biased components such as car or geographic risks. We will show that this traditional structure has significant limitations in achieving fairness. For this reason, we have developed a novel pricing model approach. Recently some approaches have Blier-Wong, Cossette, Lamontagne, and Marceau (2021); Wuthrich and Merz (2021) shown the value of autoencoders in pricing. In this paper, we will show that (2) this can be generalized to multiple pricing factors (geographic, car type), (3) it perfectly adapted for a fairness context (since it allows to debias the set of pricing components): We extend this main idea to a general framework in which a single whole pricing model is trained by generating the geographic and car pricing components needed to predict the pure premium while mitigating the unwanted bias according to the desired metric.
Ce vendredi, je donnerai un exposé au groupe de travail d’IVADO, invité par Dany Plourde. Les slides sont maintenant en ligne. En guise d’introduction, cette petite anecdote
L’exposé sera assez standard, avec une rapide introduction sur l’assurance, avant de revenir sur trois notions clés: la discrimination, les biais et les mesures d’équité
Comme c’est la première fois que je présente au Québec, je vais revenir sur quelques statistiques de marché
et une comparaison d’états aux États-Unis, et de provinces au Canada, pour caractériser des possibles discriminations
Sur les biais, je reviendrais deux de mes préférées, le paradoxe de Simpson et l’inférence écologique
et la loi de Goodhard, et le biais de rétroaction
On finira avec quelques définitions de l’équité
Ce jeudi, j’interviendrai à une conférence organisée par Covéa sur l’éthique de l’IA et la science de données, avec Astrid Bertrand et Chaouki Boutharouite. Les slides sont maintenant en ligne.
Mi-novembre, je participerai à la conférence FAIR+AI à Cachan, en France.
Le programme est maintenant disponible
Our research paper, Insurance against Natural Catastrophes: Balancing Actuarial Fairness and Social Solidarity, with Molly James and Laurence Barry, is now published in the Geneva Papers on Risk and Insurance.
Natural disasters offer a special case for the study of private and public insurance mix. Indeed, the experience accumulated over the past decades has made it possible to transform poorly known hazards, long considered uninsurable, into risks that can be assessed with some precision. They exemplify however the limits of the risk-based premiums method, as it might imply unaffordability for some. The French scheme reflects such ideas and offers a wide coverage for moderate premiums to all, but is shaken by climate change: we show that some wealthier areas, that were not perceived as “at risk” in the past, have become exposed to submersion risk in the future. This singularly makes some well-off properties the potential main beneficiaries of a scheme that was historically thought to protect the worst-off. Acknowledging that some segmentation might become desirable, we examine several models for flood risk and the disparity in premiums they entail.