Concern 448 · Working draft · editorial review in progress
Purpose-limitation drift
Purpose-limitation drift occurs when personal information is gradually reused for goals different from the reason it was collected.
Understanding the concern
A service may gather data to answer a support request, then later use it for product training, profiling or another model. Each step can seem convenient while the overall use moves far from what the person expected. A broad phrase such as improving services may not explain the practical change.
It is like lending notes for one class and later finding them used in an unrelated publication. Some new uses may be justified, but they need a clear assessment rather than an assumption that possession permits everything. The ICO's AI analysis discusses compatibility with the original collection purpose. Consent is one possible legal basis, not the only one in every situation.
An example
Private support conversations are reused to train a different product without a clear explanation of the new 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.
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
- Training
- The process of using examples to shape how an AI model behaves. Learning patterns from material does not establish that the material is true or fair.
- Drift
- A change over time in data or real-world conditions that can make a system less reliable.
Evidence and its limits
ICO analysis is UK-specific. The concern is unexplained or incompatible reuse, not a claim that every new purpose always requires fresh consent.
UK Information Commissioner's Office · Regulatory analysis
Purpose Limitation in the Generative AI Lifecycle ↗ (opens in a new tab)Compatibility of later training uses with original collection purposes in the UK framework.
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
Would people reasonably understand this reuse, and what basis and safeguards justify it?
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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