Ensuring fairness in predictive models is a critical component of any successful AI initiative. Implementing fairness testing is complex and can be driven by regulatory expectations, reputational considerations, and operational integrity. Furthermore, fairness in insurance is not solely technical: It is an interdisciplinary problem that requires judgment from domain experts, compliance teams, and AI engineers. Failing to integrate these perspectives produces blind spots that technical fixes alone cannot resolve. In the May issue of the Actuarial Intelligence Bulletin, published by the Society of Actuaries Research Institute, we shared our thoughts in an article on ensuring fairness in predictive models (see page 19).
Key discussion points include the following:
- An interdisciplinary approach to fairness assessment and remediation
- Setting up the challenge
- Choosing the right fairness metric
- Bias mitigation and the fairness-accuracy trade-off
- Mortality risk scoring
- Model governance structures
- Fairness as part of the operating model
This article originally appeared in the Actuarial Intelligence Bulletin.