Evaluating model performance across demographic groups and adjusting it to reduce observed disparities shows that the team is actively measuring and mitigating bias. Fairness requires equitable outcomes, not merely equal treatment. Ignoring demographics, deleting sensitive attributes, or focusing only on overall accuracy can leave hidden biases unaddressed.
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Microsoft Azure AI Fundamentals AI-900
Describe Artificial Intelligence Workloads and Considerations
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