A practical framework for developing, evaluating, and auditing fair algorithmic pricing systems by regulators and practitioners. 

This report offers a critical and practical framework to help evaluate and implement fairness in life insurance, recognizing that fairness is contextual and not one-size-fits-all.

Measuring fairness in life insurance is inherently complex. The paper reveals that fairness criteria varies for both individuals and groups. The paper demonstrates that each approach carries ethical, operational, and financial trade-offs for insurers.

Invited by the New York State Assembly for Public Hearing as a global expert on AI in Insurance 

I contributed to the development of the Guidance as a member of the Actuaries Institute's Anti-discrimination Working Group and received the Actuaries Institute's Volunteer of the Year Award for this contribution. Two research papers, Frees and Huang (2021) and Xin and Huang (2023), on anti-discrimination insurance pricing were included as academic resources. This Guidance Resource provides important information and suggestions to professionals and businesses as to how to properly apply, and comply with, anti-discrimination laws when making AI-informed decisions in the course of actuarial practice in the insurance and other related industries. 

Dashboard (Longevity Calculator)

Australian Longevity Explorer

Open Dataset

 Australian Socio-Economic Mortality Dataverse

Technical Paper

Towards Fairer Retirement Outcomes: Socio-economic Mortality Differentials in Australia

Media Article

How are socio-economic factors linked to life expectancy gaps?

Policy Paper

Coming out in early 2026