Fairness metrics often lack actuarial relevance and are expressed in abstract units, obscuring real-world consequences. For actuaries to intervene, proxy effects and unfair biases must be quantified in insurance-relevant terms: dollars and people. This session will present new research from the CAS Race and Insurance Pricing series, focusing on the unique challenge of establishing fairness in actuarial pricing. We argue that actuarial fairness, solidarity, and causality form the three dimensions of fairness in insurance. These give rise to a five-point spectrum of pricing benchmarks, each reflecting distinct fairness goals and trade-offs. We quantify the monetary impact of unfairness at both the policyholder and segment levels through a large-scale Québec auto insurance case study.
Learning objectives are (1) Describe three dimensions of fairness in insurance pricing: actuarial fairness, solidarity, and causality (2) Translate these dimensions of fairness into a spectrum of five pricing benchmarks (3) Diagnose and quantify potential unfairness at both individual and segment levels using actuarially meaningful metrics.
From Tuesday to Friday, I will attend the 60th Actuarial Research Conference in Toronto. With Olivier and Marie-Pier Côté, we will give a series of talk on fairness and discrimination.
I will talk in an Invited Session on Artificial Intelligence in Insurance (as well as Marie-Pier Côté)
Olivier Côté will present in Session 1 – Bias in Assessing Financial Risk
With Olivier and Marie-Pier, we will present in one of the Casualty Actuarial Society Sponsored Sessions, Session 1 – A Scalable Toolbox for Exposing Indirect Discrimination in Insurance Rates
Many industries are 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 the first part of this seminar, we propose to mitigate possible discrimination (related to so call « group fairness », related to discrepancies in score distributions) 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. This part will be based on recent work with François Hu and Philipp Ratz (2310.20508, 2309.06627, 2306.12912 and 2306.10155). In the second part, we will focus on another aspect of discrimination usually called « counterfactual fairness », where the goal is to quantify a potential discrimination « if that person had not been Black » or « if that person had not been a woman ». The standard approach, called « ceteris paribus » (everything remains unchanged) is not sufficient to take into account indirect discrimination, and therefore, we consider a « mutates mutants » approach based on optimal transport. With multiple features, optimal transport becomes more challenging and we suggest a sequential approach based on probabilistic graphical models. This part will be based on recent work with Agathe Fernandes Machado and Ewen Gallic (2408.03425 and 2501.15549).
This week-end (🥳) I will attend the 39th Annual Meeting of the Canadian Econometrics Study Group (CESG), in Toronto, Ontario on October 25-27, 2024. I will give a talk on calibration, based on recent work, with Agathe Fernandes Machado, Ewen Gallic, Emmanuel Flachaire and François Hu. Slides are available online.
I will be in Toronto this week, for the SSC annual conference (Canadian Statistical Society), to present some recent work, with Emmanuel Flachaire. Because of some administrative duty last week, I am a bit late, so I won’t be able to upload the slides before the talk. Sorry about that…
This Friday, the Second Québec-Ontario Workshop on Insurance Mathematics (WIM) will take place in Toronto, at the Fields Institute. Mathieu will give a talk on “Multivariate integer-valued autoregressive models applied to earthquake occurrences“. The paper can still be downloaded on http://arxiv.org/ and the slides can be downloaded here.
"sendo l'intento mio scrivere cosa utile a chi la intende…"