AI transparency explained: how explainability, interpretability, and AI governance combine to build trustworthy, EU AI Act-compliant AI systems and tools.
by Databricks Staff
AI transparency is the practice of making an artificial intelligence system's data, model behavior, and decision-making processes visible and understandable to the people affected by it. Transparent AI systems document how training data was collected, how a model reaches an output, and who is accountable when that output causes harm. AI transparency is distinct from explainability, which addresses how a model arrives at a specific prediction, and from interpretability, which describes how directly a human can trace a model's internal logic. This guide gives developers, auditors, policymakers, and product teams a working reference for building trustworthy AI and governing transparent AI systems.
AI transparency means giving stakeholders meaningful information about how an AI system was built, what data trained it, and how it produces its outputs. Transparent AI systems document their AI models, AI algorithms, and AI processes so that developers, regulators, and end users can evaluate whether the system behaves as claimed.
