What are some effective strategies to mitigate data poisoning in AI models?

What are some effective strategies to mitigate data poisoning in AI models?

There are several strategies to mitigate data poisoning, including data validation, anomaly detection, and robust training methods.

Can you explain how data validation works in this context?

Data validation involves checking the integrity and quality of the data before it's used for training, ensuring it meets certain criteria.

What role does anomaly detection play in preventing data poisoning?

Anomaly detection helps identify unusual patterns in the data that may indicate poisoning attempts, allowing for corrective actions.

Are there any robust training methods you recommend?

Yes, techniques like adversarial training and ensemble methods can enhance model resilience against poisoned data.