What are the risks of fine-tuning AI models?

What are the risks of fine-tuning AI models?

Fine-tuning AI models can lead to overfitting, where the model performs well on training data but poorly on unseen data.

Can fine-tuning introduce bias into the model?

Yes, if the fine-tuning data is biased, it can cause the model to learn and perpetuate those biases.

How can we mitigate the risks associated with fine-tuning?

To mitigate risks, use diverse datasets, validate performance on various data, and regularly audit the model's outputs.

Are there specific examples of fine-tuning gone wrong?

Yes, there have been cases where fine-tuned models exhibited harmful stereotypes or made incorrect predictions due to biased training data.

What best practices should I follow when fine-tuning?

Best practices include using a representative dataset, monitoring performance metrics, and conducting thorough testing before deployment.