Artificial Intelligence – AI – is everywhere. It’s the power behind voice assistants, self-driving vehicles, and robots that can perform tasks as varied as cleaning offices and harvesting crops. Its applications in Life & Annuities (L&A) insurance are just as diverse, from underwriting policies and assessing risks to predicting mortality rates. It would seem like the sky’s the limit for AI usage in L&A insurance, but like most technologies, AI has its downsides, particularly in the inherent biases present in the data used to train AI models. If unchecked, these biases can lead to unfair and discriminatory outcomes, potentially disadvantaging certain individuals or protected classes. 

AI models alone do not have any inherent biases, but if an AI model is trained on data with these biases, it is likely to perpetuate and amplify them in its decision-making process.

Before AI bias can be rectified, it must be detected. AI models need to be trained with the right data to eliminate bias. One way is to ensure a balanced training dataset or at least reduce the imbalance in the available dataset. By including data from different demographic groups and regions, the AI model can make fairer predictions. Insurance companies should also conduct regular audits and tests to assess their AI algorithms for bias and equity. This involves monitoring outcomes and reviewing decisions to identify and address discriminatory patterns. In addition, insurers should prioritize the use of transparent and explainable AI models, which can provide insights into how decisions are made. This allows for better scrutiny of the algorithm’s outputs and helps build trust with customers. And while AI can significantly streamline processes, human oversight is still the ideal way to ensure fairness and address potential biases that AI might not recognize, by identifying and rectifying errors and/or discriminatory outcomes.

In the L&A insurance ecosystem, AI bias remains a major challenge. Evolving business models and their underlying data will constantly change, making it impossible to build a perfect training dataset. Only by embracing responsible AI development can carriers develop insurance products are accessible, affordable, and equitable for all.