Transparency

Transparency in AI systems means making the model's capabilities, limitations, training data sources, and decision processes legible to users, auditors, and affected communities. It includes publishing model cards, disclosing training data categories and known biases, communicating confidence levels in outputs, and clearly labeling AI-generated content. Transparency does not require exposing proprietary weights; it means providing sufficient information for users to make informed decisions about how much to trust and rely on the system.