Concern 206 · Working draft · editorial review in progress
Terms-of-service ambiguity
Ambiguous terms of service make it difficult to understand what an AI provider may do with prompts, outputs, and other data.
Understanding the concern
People may need to know whether data is retained, reviewed, used for training, shared, or deleted. If these rights are buried in unclear language or scattered across settings and policies, a person may agree to something they did not reasonably understand.
Different plans and configurations can have different terms. A statement about one product should not be carried over to every service from the same provider. Clear summaries should match the actual agreement and explain choices at the point where the person shares information.
An example
A form offers a simple upload button while the important rules about reuse sit in several separate documents.
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.
- 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.
- Prompt
- The instructions or information given to an AI to guide its response.
- Terms of service
- The rules and agreement a provider sets for using a product or service.
Evidence and its limits
The sources establish data-governance and lawful-processing concerns. They do not determine the meaning or enforceability of any specific provider's contract.
European Data Protection Board · December 2024 · Regulatory opinion overview
Opinion on personal data and AI models ↗ (opens in a new tab)Discusses anonymity, legitimate interest, and unlawful processing case by case. Consent is not the only potential lawful basis.
NIST · AI 600-1 · 2024 · Official risk framework
Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile ↗ (opens in a new tab)Describes generative-AI risk categories and management actions; it does not measure the failure rate of every product.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Can a typical user explain what happens to their information after reading the notice?
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.
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?
Accountability
Accountability means being answerable for decisions and taking responsibility for repair. It requires more than saying that the software made the choice.
Name the person or team responsible, keep useful records, and give them authority to correct problems. Make the route to a complaint clear to the people affected.
Ask together: Who will answer for the outcome and help put things right?
Truthfulness
Truthfulness means making claims that match the evidence and being clear about what we do not know. It includes correcting mistakes rather than protecting a polished story.
Check sources, label guesses, and distinguish a trial result from a proven benefit. If AI helped produce a claim, someone still needs to take responsibility for checking it.
Ask together: What can we honestly say, and what still needs checking?
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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