A Checklist for Explainable AI in the Insurance Domain

TL;DR

This paper reviews AI use in Dutch insurance, assesses explainability challenges, and proposes a checklist to improve transparency and collaboration in adopting explainable AI techniques.

Contribution

It introduces a tailored checklist for XAI in insurance, extending existing standards to enhance transparency and cooperation.

Findings

AI adoption is increasing in Dutch insurance.

Explainability remains a key challenge for AI in insurance.

The proposed checklist aims to standardize XAI practices.

Abstract

Artificial intelligence (AI) is a powerful tool to accomplish a great many tasks. This exciting branch of technology is being adopted increasingly across varying sectors, including the insurance domain. With that power arise several complications. One of which is a lack of transparency and explainability of an algorithm for experts and non-experts alike. This brings into question both the usefulness as well as the accuracy of the algorithm, coupled with an added difficulty to assess potential biases within the data or the model. In this paper, we investigate the current usage of AI algorithms in the Dutch insurance industry and the adoption of explainable artificial intelligence (XAI) techniques. Armed with this knowledge we design a checklist for insurance companies that should help assure quality standards regarding XAI and a solid foundation for cooperation between organisations. This checklist extends an existing checklist of SIVI, the standardisation institute for digital cooperation and innovation in Dutch insurance.