Curate a more representative training dataset is the most effective first step because generative models learn statistical patterns from their training data and improving data representation reduces the chance the model will reproduce gender stereotypes.

Ensuring balanced and diverse examples and filtering harmful patterns addresses the root cause of biased outputs and gives you a solid foundation for later actions. Curating the dataset reduces harmful associations and makes subsequent steps such as fine tuning and evaluation more reliable.

Increase the model's temperature setting only alters randomness and lexical variety and does not change the model's learned associations so the same stereotypes can still appear.

Conduct subgroup bias analysis is useful for detecting and monitoring disparities across demographics but it measures the problem rather than remediating the training data that causes biased generation.

Fine-tuning the model can be effective when performed with a curated and inclusive dataset and with careful evaluation but fine tuning without improving data quality risks reinforcing existing biases and should follow data remediation and testing.

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