Actuarial Seminar - Marie-Pier Côté (Institute Intelligence and Data - Université Laval Canada)
Title: A Fair price to pay: exploiting directed acyclic graphs for fairness in insurance
Abstract: 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. This is joint work with Olivier Côté and Arthur Charpentier.