B. Validating that the data is representative.
Ensuring the data is representative is the most critical factor in mitigating bias, as unrepresentative data can lead to skewed models that reinforce or amplify existing biases.
While privacy tools, consent, and third-party data sufficiency are important, they primarily address ethical, legal, or quality concerns rather than directly tackling bias in the training and testing process.
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