AWS Certified Cloud Practitioner

Explanation

When developing and deploying Large Language Models (LLMs), addressing fairness and bias is crucial for ethical AI deployment. The two most appropriate actions from the options are:

A. Include fairness metrics for model evaluation - This is essential to measure and monitor the model's performance across different demographic groups and ensure it doesn't produce biased outputs.

C. Modify the training data to mitigate bias - Since bias often originates from training data, addressing bias at the data level is a fundamental approach to creating fairer models.

Why other options are not the best choices:

• B. Adjust the temperature parameter of the model - While temperature affects randomness in outputs, it doesn't directly address fairness or bias issues.
• D. Avoid overfitting on the training data - This is a general machine learning best practice but not specifically about fairness in LLM deployment.
• E. Apply prompt engineering techniques - While prompt engineering can help guide model behavior, it's a workaround rather than addressing the root causes of bias in the model itself.

Best Practices for Fair LLM Development:

1. Data curation and preprocessing to identify and mitigate biases
2. Fairness-aware model training with appropriate loss functions
3. Comprehensive evaluation using fairness metrics across different groups
4. Continuous monitoring in production to detect emerging biases
5. Transparent documentation of model limitations and biases