How can we prevent data poisoning in AI systems?

How can we prevent data poisoning in AI systems?

Preventing data poisoning involves several strategies, such as data validation, anomaly detection, and robust training methods.

What are some common techniques used for data validation?

Common techniques include input sanitization, consistency checks, and using trusted data sources to ensure quality.

Can you explain anomaly detection in more detail?

Anomaly detection identifies unusual patterns in data that may indicate poisoning, using statistical methods or machine learning.

What robust training methods can help mitigate data poisoning?

Robust training methods include adversarial training, ensemble methods, and using noise-robust algorithms to improve resilience.