Concern 230 · Working draft · editorial review in progress
Provenance for training data
Training-data provenance is a traceable record of where training material came from and what permissions or restrictions accompany it.
Understanding the concern
A dataset can pass through many downloads, filters, and combinations before reaching a model. Along the way, information about origin, license, consent, or permitted use can be lost. Without a record, claims about responsible data use become harder to verify.
A provenance record can show what someone claimed or which source was used, but it does not automatically prove that the claim was accurate or that every use is lawful. Permission and legal exceptions depend on the material, purpose, and jurisdiction. Documentation and rights review answer different questions.
An example
An ingredient's shipping record identifies the supplier, but a separate check is needed to establish whether it was authorized for this use.
Illustrative unless identified as a documented case.
Words in plain English
- 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.
- Provenance
- The history of information: where it came from and how it has been handled or changed.
Evidence and its limits
The sources address dataset documentation and public training summaries. They do not establish that every training item requires an identical license.
Longpre and colleagues · Dataset audit
The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing and Attribution in AI ↗ (opens in a new tab)Traces dataset lineage, licensing, and attribution problems in audited collections; not proof that every third-party dataset is unlawful.
European Commission · 2025 · Official regulatory guidance
Template for general-purpose AI model providers to summarise their training content ↗ (opens in a new tab)Describes public training summaries for covered providers, including information gaps and transition conditions.
World Wide Web Consortium · Technical standard
PROV Overview ↗ (opens in a new tab)Describes provenance records and relationships. Recording lineage does not guarantee the truth or lawful use of the material.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Can we trace important training sources and verify the rights claims made about 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.
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.
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