Concern 422 · Working draft · editorial review in progress
Loyalty-program AI profiling
Loyalty-program profiling combines purchase history with other information to predict a customer's interests, circumstances or future behaviour.
Understanding the concern
A loyalty account connects transactions over time. That can support discounts, but it can also produce guesses about household changes or sensitive needs. The person may understand that purchases are recorded without expecting those inferences. A predicted life event is still a guess, even when the marketing system treats it as a useful category.
A shopping basket can have many explanations: gifts, purchases for someone else or a temporary need. Turning it into a personal label can expose private information or simply be wrong. Clear explanations should distinguish records the customer supplied from categories the company created, and make correction or refusal practical.
An example
A shopper receives highly personal offers based on purchases made for another household.
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 documents commercial profiling categories. The example avoids treating the widely repeated retailer-pregnancy anecdote as verified evidence of a universal capability.
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.
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
Which sensitive inferences are created from purchases, and can customers see or challenge 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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