Concern 416 · Working draft · editorial review in progress
Behavioral advertising profiles
Behavioural advertising profiles are collections of observed and inferred interests used to choose which adverts a person sees.
Understanding the concern
A profile may combine visits, purchases and interactions across services. AI can add predictions about what someone might want or respond to. Those predictions are not necessarily things the person stated, and they may be wrong. Sensitive information can emerge from a combination of otherwise ordinary activities.
Think of a shop assistant keeping a notebook that follows you into every other shop. Personalisation may be useful, but people may not expect the notebook's reach or guesses. Transparency should cover both collected facts and inferred categories, along with practical control over their use. An anonymous-looking identifier does not automatically prevent targeted treatment.
An example
A person is placed in a sensitive advertising category based on browsing that had an unrelated purpose.
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.
- Inference
- Drawing a conclusion from clues. In AI engineering it can also mean running a trained model to produce an output.
Evidence and its limits
The FTC reports document profiling mechanisms and historical practices. They do not prove every current advertiser uses every listed category.
Federal Trade Commission · Investigation report
Data Brokers: A Call for Transparency and Accountability ↗ (opens in a new tab)2014 commercial data categories and marketing uses; historical examples do not establish current use by every broker.
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
Can people understand and control the inferred categories used to target 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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