Abstract

Artificial Intelligence (AI) is reshaping the Life Insurance industry, streamlining processes like underwriting, claims handling, and personalized policy recommendations. These advancements have brought greater efficiency and accuracy, but they’ve also raised important ethical questions. As AI takes on a bigger role in making decisions that impact people’s lives, concerns around bias, transparency, and accountability have become more pressing.
This paper explores these ethical challenges and proposes a practical framework to test AI systems in Life Insurance. The focus is on identifying biases in data and algorithms, ensuring decisions are transparent and understandable, and establishing clear lines of accountability. By incorporating techniques like Explainable AI (XAI), fairness metrics, and continuous monitoring, the framework offers insurers a structured way to build trust while staying compliant with regulations. Through real-world case studies and actionable best practices, this research aims to show how insurers can strike the right balance between automation and human oversight, ensuring that AI-driven decisions are fair, transparent, and worthy of trust.