Using a training dataset that covers all relevant demographic groups exposes the model to varied examples and reduces the chance that it learns patterns that favor or exclude particular populations.
Reducing the sampling temperature only makes outputs more deterministic; it does not address underlying bias in the learned parameters.
Removing outliers that correspond to minority groups shrinks representation and tends to worsen bias.
Simply increasing the depth or parameter count of the network can even amplify existing bias if the data remain unbalanced.
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Microsoft Azure AI Fundamentals AI-900
Describe features of generative AI workloads on Azure
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