Include a diverse set of data points representing different demographic groups - This is the correct answer. To promote fairness in an AI solution for loan approvals, it is crucial to include a diverse set of data points that represent various demographic groups. This helps the model learn from a wide range of experiences and ensures that the system does not disproportionately favor or disadvantage any particular group.
Exclude sensitive attributes like race and gender from the training data - While excluding sensitive attributes like race and gender can prevent direct bias, it may not be enough to ensure fairness.
Use historical data without modification to reflect real-world trends - Using historical data without modification might perpetuate existing biases in the data.
Prioritize algorithm efficiency over data diversity - While efficiency is important, prioritizing it over data diversity can lead to biased or incomplete models.
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Microsoft Azure AI Fundamentals AI-900
Describe Artificial Intelligence Workloads and Considerations
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