Which of the following considerations is the most important in mitigating the potential of bias...

The correct answer is B because ensuring that training and testing data is representative is the most critical factor in mitigating bias in AI systems. AI governance frameworks emphasize that biased or unrepresentative datasets can lead to discriminatory outcomes, particularly when certain demographic groups are underrepresented or overrepresented. Representative data helps ensure that the model performs fairly and accurately across different populations. While privacy-enhancing tools and consent address legal and ethical data use, they do not directly prevent bias in model outcomes. Similarly, assessing the sufficiency of third-party data focuses on quantity rather than fairness or distribution. Effective bias mitigation begins with evaluating whether the dataset reflects the diversity and characteristics of the real-world population the AI system will impact.