Achieving transparency helps understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This provides insights into how the model was created and allows it to be reproduced in a transparent way. Therefore, ensuring that the factors used in an AI system to approve loans are explainable aligns with the Microsoft guiding principle of transparency.