Transparency

The ability of an AI system to provide understandable reasons or evidence for how it produced a prediction, recommendation, or decision, enabling humans to interpret and trust its behavior.

The ability to understand how an AI model or system arrives at its predictions, decisions, or outputs by examining its internal mechanisms, decision-making process, or contributing factors.

The principle that individuals and organizations are responsible for the decisions, actions, and outcomes of AI systems throughout their lifecycle.

The framework of policies, processes, and oversight that ensures AI systems are developed, deployed, and operated responsibly, safely, and in compliance with regulations.

A document that describes an AI model's intended uses, limitations, capabilities, evaluation results, and other relevant information.

A detailed document outlining an AI system's architecture, intended use cases, safety evaluations, capabilities, and operational limitations.