Predictive Technologies, Ethics, and the Future of Insurance

Image of a pair of hands holding a bubble with a family inside

FUNDING

Uehiro Oxford Institute RP Award

PROJECT DATES

Project duration: 3 years (1st September 2025 – 31st August 2028)

PI

Dr Jonathan Pugh


PROJECT DESCRIPTION

Insurance is a crucial social good – it protects individuals from the financial consequences of significant risk events, whose effects may otherwise be individually unaffordable. However, the rise of powerful predictive technologies – such as artificial intelligence, digital health tools, and genetic testing - is changing how insurance companies assess risk, enabling them to develop far more personalized risk assessments. This might help consumers obtain insurance products that are better tailored to their own individual circumstances. However, the use of these technologies to deliver increasingly personalised risk assessments also raises profound questions about foundational ethical principles underlying the provision of insurance, including fairness, privacy, respect for autonomy, and solidarity. 
This interdisciplinary research program will explore the ethical implications of the personalized insurance pricing enabled by these sorts of powerful predictive technologies, focusing on life, critical illness, and income protection insurance. Drawing on legal, philosophical, and economic expertise, the project will address two interrelated strands: (1) privacy, consent, and data and (2) fairness, discrimination, and solidarity. Public engagement will be central to the project’s methodology, with stakeholder workshops shaping the research agenda and dissemination strategy.

As the insurance industry continues to develop alongside these powerful predictive technologies, this project will aim to ensure that ethical values retain a crucial role at the heart of the industry, and to support policy-makers and insurers in navigating these new challenges responsibly.

PROJECT OUTPUTS

The project outputs will be listed here in due course.