To promote fairness, the team should assess the model's performance across different demographic groups to identify and mitigate potential biases. Simply increasing the dataset size may not address underlying biases present in the data. Excluding sensitive attributes without considering their indirect influence can still lead to biased outcomes. Focusing solely on accuracy may neglect fairness considerations, potentially disadvantaging certain groups.
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Microsoft Azure AI Fundamentals AI-900
Describe Artificial Intelligence Workloads and Considerations
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