AI Transparency
What is AI Transparency?
The principle that AI system operations and decisions should be understandable and open to examination. Transparency includes documenting data sources, model architecture, training processes, and decision-making logic.
Terms that appear alongside ai transparency
Each of these is named in at least one of the same controls as ai transparency. The number is how many controls name both.
- transparency 9 shared controls
- explainability 6 shared controls
- ai governance 5 shared controls
- governance 5 shared controls
- governance framework 5 shared controls
Frameworks that govern ai transparency
What the standards actually require on ai transparency
Requirements naming ai transparency across 2 standards, quoted from the control text.
Each agency must publish a statement, available to the public, that sets out its approach to adopting and using AI, with the content and form the DTA's transparency statement standard prescribes.
australia-policy-for-the-responsible-use-of-ai-in-government-version-2-0::2.1 · Publish and maintain an AI transparency statement and notify the DTA →Per UNICEF AI: transparency + explanation for children + adult capacity building + create enabling environment.
UNICEFAI-4 · Transparency, Explanation, Adult Capacity →Questions people ask about ai transparency
What is AI Transparency?
Why is AI Transparency important for compliance?
Which compliance frameworks address AI Transparency?
Where can I learn more about AI Transparency?
See how AI Transparency applies across compliance frameworks
Our platform maps 1,016 frameworks with 316K cross-framework control mappings. Explore how this concept is addressed across standards.