Transparency (AI)
The availability of relevant information about an AI system's design, development, data, operation, and limitations to appropriate stakeholders. Transparency answers the broader question 'what is this system and what happened?' and encompasses documentation, disclosure, and communication practices.
Why It Matters
Transparency is the foundation on which accountability, auditability, and trust are built. Without knowing what an AI system is, how it was built, and what data it uses, no meaningful oversight is possible.
Example
A company demonstrates AI transparency by publishing model cards for its deployed systems, maintaining a public AI registry listing all high-risk AI use cases, providing clear notice to users when they're interacting with AI, and disclosing training data sources and known limitations.
Think of it like...
AI transparency is like the glass floor in a building — it doesn't change the structure underneath, but it lets everyone see the foundation and decide whether they trust it to hold their weight.
Related Terms
Explainability
The ability to understand and articulate how an AI model reaches its decisions or predictions. Explainable AI (XAI) makes the decision-making process transparent and comprehensible to humans.
Interpretability
The degree to which a human can understand the internal mechanisms and reasoning process of a machine learning model. More interpretable models allow deeper inspection of how they work.
Model Card
A standardized document that accompanies a machine learning model, describing its intended use, performance metrics, limitations, training data, ethical considerations, and potential biases.