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Transparency and explainability are two important concepts in AI governance, as they enable users to understand how an AI model is built, how data is used and processed, and how data affects internal weights and biases. Transparency provides a user with information about how a solution makes decisions, while explainability offers a user a rational justification for why a solution made a particular decision. Both concepts are necessary components of AI governance and increase the credibility and trustworthiness of AI solutions.