Consumer protection and product liability law applied to AI
Two older legal traditions meet AI here: consumer protection law, which polices what you claim, and product liability law, which allocates the cost when a product causes harm.
Why this matters for the AIGP exam
These questions test whether you know that ordinary commercial law never stopped applying — the regulators' own phrase is that there is no AI exemption from the laws on the books.
The essentials
- Deceptive and unfair practices. In the US the FTC enforces against false or unsubstantiated AI claims — "AI washing" — under its existing Section 5 authority. Claims about what a model can do need the same substantiation as any other advertising claim.
- Unfairness reaches design. Beyond claims, practices such as manipulative interface design and the quiet repurposing of consumer data for training can be attacked as unfair practices.
- The EU's revised Product Liability Directive is the significant recent shift: software, including AI systems, is now squarely a "product", and a defective product carries strict liability — no need to prove negligence. The revision also eases the injured party's burden of proof for complex products and can compel disclosure of technical evidence.
- The US side is a patchwork of negligence, warranty and state product-liability doctrine, with open questions about when an AI system is a product and who in the chain — developer, integrator, seller — answers for a defect.
What the exam asks
Which tradition a scenario engages (a claim problem or a harm problem), what strict liability changes for a provider, and why marketing copy is a governance artefact.
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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