Concern 417 · Working draft · editorial review in progress
Cross-device tracking via AI
Cross-device tracking links activity from different phones, computers or other devices to the same person or household.
Understanding the concern
Some links come from signing into the same account. Others are predictions based on patterns such as shared connections or behaviour. A predicted link can be wrong, especially when people share devices or networks. Information from one context may then influence what appears in another.
It is like several notebooks being joined because an analyst thinks they belong to the same reader. That can reveal more than any notebook alone and can expose one person's interests to others. A control that works on one device may not cover the whole linked profile. Users need clear information about the scope of the link.
An example
Private browsing on a personal phone influences adverts shown on a shared household screen.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
Evidence and its limits
The FTC report distinguishes forms of linkage and privacy risks. It is a technical explanation, not a current inventory of all tracking vendors.
Federal Trade Commission · Technical and policy report
Cross-Device Tracking: An FTC Staff Report ↗ (opens in a new tab)Deterministic and probabilistic device linkage, consumer awareness and sensitive-data risks.
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
Are devices linked by a known account or a guess, and can users separate or correct those links?
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.
About the register and scoring →More ways to explore
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