What it means
Transparency in AI means being open about when, where and how AI is used, what it is designed to do, what data it relies on, and what its limitations are. It applies to the people using the system and to those affected by it.
Real-world example
A museum's website labels all AI-generated object descriptions with a visible note: "This text was drafted with AI from our collection records and reviewed by a curator on [date]." A linked page explains which tool is used, what it is used for and how visitors can report errors.
Why it matters for AI adoption
Transparency builds trust and allows people to make informed choices, and it is increasingly a legal requirement. The EU AI Act includes transparency obligations, for example to inform people when they interact with an AI system and to mark certain AI-generated content. Transparency only works if it is clear and placed where people need it.
Often confused with
Transparency covers the system as a whole; explainability covers individual outputs. Both are needed.
Questions to ask
- Do people know when they are interacting with AI or reading AI-generated content?
- Where can people find out how the system works and how to raise concerns?