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The answer is D. Ensure that a training dataset is representative of the population.
Here's why:
The Microsoft principle of transparency in responsible AI focuses on understanding how AI systems work and their potential impact. A representative training dataset is crucial for transparency because:
Bias Detection: A non-representative dataset can lead to biased AI models. If the data doesn't accurately reflect the real world, the model might make unfair or discriminatory decisions. Transparency requires us to be aware of and mitigate these biases.
Explainability: Understanding the data the model was trained on helps explain its behavior. If we know the data is representative, we have more confidence that the model's outputs are based on real-world patterns, not skewed by a biased dataset.