Concern 851 · Working draft · editorial review in progress
Copyright in training
Copyright in AI training concerns whether developers have permission, a license or a legal exception to copy protected works while building a model.
Understanding the concern
Training often involves processing large collections of writing, images, music or code. A work being easy to find online does not automatically settle whether it may be copied for that purpose. The legal question concerns the particular use, the way material was obtained and the law that applies.
Different legal systems take different approaches. US fair use requires a case-specific analysis. EU rules include text and data mining exceptions with conditions, including rights reservation for some uses. A library analogy helps: being able to read a book and being allowed to make copies for a new service are separate questions. This issue is also distinct from whether an AI answer later copies protected expression.
An example
A developer collects a photographer's online portfolio for training; access to the website alone does not answer every copyright question.
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.
- EU
- The European Union, a group of European countries with shared institutions and some shared laws.
Evidence and its limits
The US Copyright Office report is analysis, not a blanket court ruling. EU exceptions have different conditions; neither framework makes every training use lawful or unlawful.
U.S. Copyright Office · May 2025 pre-publication report · Official report collection
Copyright and Artificial Intelligence, Part 3: Generative AI Training ↗ (opens in a new tab)Examines US copyright and training, including licensing. A report is not a final ruling on every training practice or jurisdiction.
European Union · Legislation
Directive (EU) 2019/790, Articles 3 and 4 ↗ (opens in a new tab)Text and data mining exceptions have different conditions, including lawful access and, for Article 4, rights reservation. National implementation and facts matter.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
- EU AI Act — high-level summary (opens in a new tab)
regulation
- Council of Europe AI Convention (opens in a new tab)
regulation
- International humanitarian law (IHL)
framework
The body of law that governs the conduct of armed conflict and the protection of people who are not fighting, which applies to AI used in or for conflict.
A question to discuss
What permission or legal exception covers this training use, and how can creators exercise any applicable choices?
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.
Justice
Justice means fair treatment, fair procedures, and attention to who receives benefits and burdens. It asks whether people with less power can be heard and seek repair.
Look at outcomes across different groups and individual cases. Provide a usable way to challenge decisions and correct harm. Equal wording in a policy does not guarantee equal treatment in practice.
Ask together: Who benefits, who bears the cost, and who can challenge the decision?
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?
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?
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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