Machine learning improves predictive accuracy in insurance pricing but exacerbates trade-offs between competing fairness criteria across different discrimination measures, challenging regulators and insurers to reconcile profitability with equitable outcomes. While existing fairness-aware models offer partial solutions under GLM and XGBoost estimation methods, they remain constrained by single-objective optimization, failing to holistically navigate a conflicting landscape of accuracy, group fairness, individual fairness, and counterfactual fairness. To address this, we propose a novel multi-objective optimization framework that jointly optimizes all four criteria via the Non-dominated Sorting Genetic Algorithm II (NSGA-II), generating a diverse Pareto front of trade-off solutions. We use a specific selection mechanism to extract a premium on this front. Our results show that XGBoost outperforms GLM in accuracy but amplifies fairness disparities; the Orthogonal model excels in group fairness, while Synthetic Control leads in individual and counterfactual fairness. Our method consistently achieves a balanced compromise, outperforming single-model approaches.

翻译：机器学习提升了保险定价的预测准确性，但也加剧了不同歧视度量标准下相互竞争的公平性准则之间的权衡，这给监管机构和保险公司在协调盈利性与公平结果方面带来了挑战。尽管现有的公平性感知模型在GLM和XGBoost估计方法下提供了部分解决方案，但它们仍受限于单目标优化，未能整体性地驾驭准确性、群体公平性、个体公平性与反事实公平性之间相互冲突的格局。为解决此问题，我们提出了一种新颖的多目标优化框架，该框架通过非支配排序遗传算法II（NSGA-II）联合优化所有四个准则，从而生成一个多样化的权衡解帕累托前沿。我们采用一种特定的选择机制在该前沿上提取一个溢价。我们的结果表明，XGBoost在准确性上优于GLM，但放大了公平性差距；正交模型在群体公平性方面表现优异，而合成控制则在个体公平性与反事实公平性方面领先。我们的方法始终能达成平衡的折衷，其表现优于单一模型方法。