Research Paper#AI in Insurance, Fairness in Machine Learning, Multi-Objective Optimization🔬 ResearchAnalyzed: Jan 3, 2026 08:44
Fairness-Aware Insurance Pricing with Multi-Objective Optimization
Analysis
This paper addresses the critical issue of fairness in AI-driven insurance pricing. It moves beyond single-objective optimization, which often leads to trade-offs between different fairness criteria, by proposing a multi-objective optimization framework. This allows for a more holistic approach to balancing accuracy, group fairness, individual fairness, and counterfactual fairness, potentially leading to more equitable and regulatory-compliant pricing models.
Key Takeaways
- •Proposes a multi-objective optimization framework for fairness-aware insurance pricing.
- •Uses NSGA-II to generate a Pareto front of trade-off solutions.
- •Addresses the limitations of single-objective optimization in balancing competing fairness criteria.
- •Evaluates different models (GLM, XGBoost, Orthogonal, Synthetic Control) across various fairness metrics.
- •Demonstrates the potential for more equitable and regulatory-compliant insurance pricing.
Reference
“The paper's core contribution is the multi-objective optimization framework using NSGA-II to generate a Pareto front of trade-off solutions, allowing for a balanced compromise between competing fairness criteria.”