AI Transparency
The practice of making AI systems' operations, decisions, and limitations understandable to stakeholders.
Overview
AI transparency refers to the practice of making AI systems' design, capabilities, limitations, and decision-making processes visible and understandable to relevant stakeholders — developers, users, regulators, and affected communities. It encompasses technical transparency (how the model works), data transparency (what data was used), and outcome transparency (how decisions affect people).
Key Details
Transparency mechanisms include model cards, datasheets for datasets, system documentation, explainability tools, and public disclosure of AI use. Many AI regulations (EU AI Act, NIST AI RMF) mandate transparency requirements. Transparency is foundational to accountability — stakeholders cannot evaluate fairness, safety, or appropriateness of AI systems they don't understand.