Training the model with a diverse and representative dataset helps ensure that the generative AI model learns from a wide range of perspectives, reducing the likelihood of biased outputs.
Increasing the complexity of the model's architecture does not address bias and may even amplify existing biases.
Limiting the training data to specific demographics introduces bias by excluding other groups.
Deploying the model without prior evaluation to ensure originality neglects the essential step of testing for bias and other ethical considerations.
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Microsoft Azure AI Fundamentals AI-900
Describe features of generative AI workloads on Azure
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