To implement a large language model (LLM) responsibly, the firm should focus on fairness and mitigating bias, which are critical for ethical AI deployment.
A. Include Fairness Metrics for Model Evaluation:
Fairness metrics help ensure that the model's predictions are unbiased and do not unfairly discriminate against any group.
These metrics can measure disparities in model outcomes across different demographic groups, ensuring responsible AI practices.
C. Modify the Training Data to Mitigate Bias:
Adjusting training data to be more representative and balanced can help reduce bias in the model's predictions.
Mitigating bias at the data level ensures that the model learns from a diverse and fair dataset, reducing potential harms in deployment.
Why Other Options are Incorrect:
B. Adjust the temperature parameter of the model: Controls randomness in outputs but does not directly address fairness or bias.
D. Avoid overfitting on the training data: Important for model generalization but not directly related to responsible AI practices regarding fairness and bias.
E. Apply prompt engineering techniques: Useful for improving model outputs but not specifically for mitigating bias or ensuring fairness.
