用多目标优化平衡保险定价中的准确率与公平性，解决模型偏见难题。
Fairness-Aware Insurance Pricing: A Multi-Objective Optimization Approach
- 采用NSGA-II算法同时优化准确率、群体公平、个体公平和反事实公平
- XGBoost精度更高但加剧不公平，合成控制法在个体公平上表现最佳
- 生成多种权衡方案，适合需要兼顾利润与公平的保险公司
机器学习虽提升了保险定价的预测准确性，却加剧了不同歧视度量之间的公平性权衡，使监管机构和保险公司难以在盈利与公平之间取得平衡。现有公平感知模型在GLM和XGBoost框架下仅提供部分解决方案，受限于单目标优化，无法全面应对准确率、群体公平、个体公平与反事实公平之间的冲突。为此，我们提出一种新型多目标优化框架，通过非支配排序遗传算法II（NSGA-II）联合优化四项指标，生成多样化的帕累托前沿解集，并采用特定选择机制从中提取最优保费方案。实验表明，XGBoost在准确性上优于GLM，但放大了公平性差异；正交模型在群体公平性上表现突出，合成控制法在个体与反事实公平性上领先。所提方法始终实现均衡妥协，优于单一模型方法。
原文摘要 · Abstract (English)
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.
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