Crucial Concepts in AI: Transparency and Explainability

Transparency and explainability are critical concepts in general but especially applicable to AI given that most practitioners-even within IT-are unfamiliar with how these systems work. Both concepts are often discussed in the context of ethical AI, responsible AI, and AI governance. Though they are closely related, they have distinct meanings and serve different purposes in understanding and governing AI systems.

Transparency focuses on providing general information to a broad audience, including stakeholders and the public, about the AI system. Explainability is more specific and seeks to clarify individual decisions or outcomes to users, developers, and stakeholders who need to understand its behavior.

Transparency is focused on promoting trust in the system, while explainability is concerned with establishing trust in specific outputs. To accomplish this, transparency and explainability focus on different elements.