Concern 423 · Working draft · editorial review in progress
Insurance surveillance
Insurance surveillance is ongoing collection of personal behaviour or health-related data that may influence coverage, pricing or claims.
Understanding the concern
A driving app or connected device can provide information relevant to a particular insurance product. The concern is how much is collected, whether the measurement is fair and whether people understand its consequences. A shared car, unusual work schedule or faulty sensor can complicate the interpretation of a score.
Participation may be presented as a route to savings, but people also need to understand penalties, alternatives and later uses. A measure that predicts cost is not automatically an appropriate basis for every decision. Rules differ across insurance types and places, so broad claims about what insurers may do can mislead.
An example
A driver agrees to a discount program without understanding how particular recorded behaviours could affect the eventual price.
Illustrative unless identified as a documented case.
Evidence and its limits
The sources address AI governance and a driving-data case. They do not establish one legal rule for health, life and vehicle insurance.
National Association of Insurance Commissioners · Model guidance
NAIC Model Bulletin on Use of AI by Insurers ↗ (opens in a new tab)2023 model bulletin on governance and unfair discrimination; adoption and legal effect vary by state.
Federal Trade Commission · Enforcement case
General Motors and OnStar: FTC Case ↗ (opens in a new tab)Allegations about location and driving-data consent; final order announced January 2026.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
- OECD AI Principles (opens in a new tab)
framework
- NIST AI RMF (opens in a new tab)
framework
A question to discuss
What can this data change, how can errors be challenged, and what option exists for someone who declines monitoring?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Dignity
Dignity means recognizing each person's worth regardless of usefulness, status, or a system's score. People deserve respect and a voice in matters that affect them.
Explain important decisions, avoid humiliating treatment, and keep room for a person's story. Do not treat a prediction as a complete account of who someone is.
Ask together: Does this use treat each person as someone whose experience matters?
Trustworthiness
Trustworthiness means giving people good reasons to rely on you. It grows when promises, actions, and evidence line up. With AI, a friendly voice or a famous brand is not enough: people need to know what was checked and what could still go wrong.
In practice, keep promises small enough to test, record mistakes, and tell people when the tool's limits affect them. If a service promises human review, make sure a person really has time and authority to review it.
Ask together: What would give the people affected a sound reason to trust this use?
Stewardship
Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.
Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.
Ask together: What are we responsible for caring for over time?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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