Intellectual property law and AI: authorship, training data, and the licensing puzzle
Intellectual property is the most actively litigated area of AI law, and it splits into two questions: can you train on other people's work, and can you own what the model produces.
Why this matters for the AIGP exam
The exam draws on real disputes and expects you to reason about both directions — inputs and outputs — plus the licensing terms that manage the risk in practice.
The essentials
- Outputs: human authorship. In the US, copyright requires a human author. Purely AI-generated works are not protectable, and a text prompt alone has not been enough to make the prompter an author; protection attaches to the human-contributed elements of a work.
- Inputs: the training-data fight. Whether training a model on copyrighted works is infringement or fair use is being litigated now, with major suits from news organisations, authors and visual artists. No settled answer — the exam tests the arguments, not a verdict.
- Jurisdictions differ. The EU allows text-and-data mining with an opt-out for rightsholders (which the AI Act requires GPAI providers to honour); Japan's exception is broader; the UK's is narrower. Where training happens matters.
- Licensing is the practical answer. Organisations manage the uncertainty through contract: what data a vendor may train on, who owns outputs, whether prompts and outputs stay confidential, and who indemnifies whom if output infringes.
What the exam asks
Whether a described output is protectable, what risk a training pipeline carries, and which contract term addresses a stated concern.
Going deeper
This page is the condensed version. The full topic — with the detail above expanded and practice questions attached — is in the app, inside Domain II. Domain I, a quarter of the course, is free to try first.
Back to the AIGP study guide.
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