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This article studies how multiple notions of fairness can be incorporated into a single Bayesian non-parametric regression framework for insurance pricing, witha focus on claim frequency modeling under a log-link. We consider a Generalized Gaussian Process Regression (GGPR) model for count data with risk exposure and introduce fairness interventions in its architecture. Specifically, we addressnotions of individual fairness by altering the kernel structure to control the similarity between policies (e.g., to mitigate omitted variable bias). We also address group-level fairness by enforcing demographic parity through linear constraints affecting the posterior. This modified GGPR architecture allows us to jointly enforce multiple fairness definitions, spanning both group and individual-level criteria, within a single probabilistic model. We empirically explore trade-offs with actuarial fairness, and how different fairness criteria interact when combined. The results highlight the importance of adopting a multi-criteria, context-aware approach to fairness in insurance pricing.
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Jamotton, C., & Hainaut, D. (2026). A Multi-Criteria Fair Gaussian Regressor for Insurance Premium (LIDAM Discussion Paper ISBA 2026/03). https://hdl.handle.net/2078.5/272729