A Checklist for Explainable AI in the Insurance Domain

Transparency gaps exist in current insurance AI systems: The paper identifies a significant lack of transparency and explainability in AI algorithms used across the Dutch insurance industry, making it difficult for both experts and non-experts to understand how these systems make decisions that directly affect customers.

Explainability is essential for detecting bias: Without transparent AI systems, insurance companies cannot adequately assess potential biases hidden within their data or models—a critical concern since biased algorithms can lead to unfair treatment in underwriting, claims, and pricing decisions.

A practical checklist can standardize XAI practices: The researchers developed a checklist extending existing Dutch insurance standards to help companies establish consistent quality standards for explainable AI, providing a concrete tool for responsible AI implementation across the industry.

Industry-wide cooperation requires shared standards: The checklist is designed to facilitate cooperation between insurance organizations, suggesting that establishing common XAI standards is necessary for the industry to collectively address transparency and accountability challenges.

Explainability affects both usefulness and accuracy: The paper emphasizes that lack of algorithm transparency raises questions about both the practical usefulness and reliability of AI systems in insurance, indicating that explainability is not just an ethical concern but also a business and technical one.