2024 Network Awards – Emerging, Applied Research
Winner:
Dr Fei Huang, Senior Lecturer, UNSW
and team member: Xi Xin, PhD student, UNSW
Judges’ comments
This research addresses an important global issue. It was designed and executed with extensive external stakeholder involvement, and after completion, it has been disseminated broadly and is shaping changes in end-user practice.
The initiative
Insurance discrimination issues have been an important topic for the insurance industry for decades and are evolving in part due to insurers’ extensive use of Big Data. A challenge and grey area in regulation has resulted from the growing use of big data analytics by insurance companies – direct discrimination is prohibited, but indirect discrimination using proxies or more complex and opaque algorithms is not clearly specified or assessed. This phenomenon has recently attracted the attention of insurance regulators all over the world. Meanwhile, various quantitative fairness metrics have been proposed and flourished in the machine learning literature with the rapid growth of artificial intelligence (AI) in the past decade. However, there is a missing link between the regulations, fairness criteria, and pricing models for insurance applications, which creates a barrier for insurers to mitigate indirect discrimination in practice.
In their research, they address this issue by establishing the linkage among potential and existing insurance regulations, quantitative fairness criteria, and anti-discrimination insurance pricing models. In particular, they: (1) review anti-discrimination laws and regulations of different jurisdictions with a special focus on indirect discrimination in the general insurance industry; (2) summarise the fairness criteria that are potentially applicable to insurance pricing, match them with different potential and existing anti-discrimination regulations, and implement them into a series of existing and newly proposed anti-discrimination insurance pricing models; and (3) compare the outcome of different insurance pricing models via the fairness-accuracy trade-off and analyse the implications of using different pricing models on customer behaviour and cross-subsidies.
Contributions and impacts
Research outputs from this project has won two prestigious best paper awards, including Carol Dolan Actuaries Summit Prize 2022, ASTIN Colloquium Best Paper Award 2022. They have been cited by industry publications and submissions to government inquiries. Their published research papers in this field are among the top downloaded articles in the North American Actuarial Journal. Their research outputs have received both domestic and international media coverages on various fairness and discrimination related issues, including AI, Big Data, COVID-19, climate change, insurance affordability, and genetic testing.
Fei contributed to the development of the “Guidance Resource: Artificial intelligence and discrimination in insurance pricing and underwriting” through collaboration with Australian Human Rights Commission (AHRC) and Actuaries Institute
Dr Fei Huang
Related academic publications
Xin, X., & Huang, F. (2023). Antidiscrimination Insurance Pricing: Regulations, Fairness Criteria, and Models. North American Actuarial Journal, 28(2), 285–319. https://doi.org/10.1080/10920277.2023.2190528
Frees, E. W. (Jed), & Huang, F. (2021). The Discriminating (Pricing) Actuary. North American Actuarial Journal, 27(1), 2–24. https://doi.org/10.1080/10920277.2021.1951296
Xi Xin
Related media coverage
- Home insurance is on the rise. Is there an affordable solution? Business Think
- AI discrimination potential explored, RESOLVE, December 2023
- If you get a genetic test, could a life insurance firm use it against you? Business Think
- How to manage bias in insurance data and algorithms. ANZIIF article
- Anti-discrimination Insurance Pricing: Regulations, Fairness Criteria, and Models, Actuaries Digital
- Pricing fairness: tackling big data and COVID-19 insurance discrimination, Media Coverage by Business Think and UNSW Newsroom
- How insurers can mitigate the discrimination risks posed by AI, Media Coverage by IMD and Business Think
- Virtual Summit Shorts: The Discriminating (Pricing) Actuary., Actuaries Digital
- SBS Interview (in Chinese) 2021 on Insurance Discrimination, Link
- Value Driven Data Science Podcast 2022, Episode 3: Fairness and Anti-Discrimination in Machine Learning
- Fei Huang’s Personal Webpages: https://www.unsw.edu.au/staff/fei-huang