Concern 412 · Working draft · editorial review in progress
Health and wellness monitoring
Employer health-monitoring risk arises when workplace devices or programs collect bodily or wellness information that affects workers' privacy or treatment.
Understanding the concern
A wearable may support a safety goal, such as noticing a hazard, or be offered as part of a wellness program. The data can reveal more than the worker intended to share. An employee may feel pressure to participate if refusal could look uncooperative or affect opportunities.
The distinction between support and evaluation matters. Information collected to help someone should not quietly become a judgement about their reliability or future cost. A genuinely voluntary program needs a real option to decline. Access should also be limited: a supervisor may need to know a work adjustment without seeing detailed health records.
An example
A worker joins a wellness program but worries that activity data will influence promotion decisions.
Illustrative unless identified as a documented case.
Evidence and its limits
ICO guidance addresses the employment power imbalance and health monitoring; it is UK-specific and currently marked for review.
UK Information Commissioner’s Office · Guidance
What If We Carry Out Health Monitoring? ↗ (opens in a new tab)Health and wellness monitoring, power imbalance and purpose limits; UK guidance flagged as under review.
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
Can workers decline without disadvantage, and what prevents health information from being reused to judge them?
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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