How can I detect data poisoning in my AI models?

What is data poisoning and why is it a concern for AI models?

Data poisoning is when malicious data is injected into training datasets, compromising model integrity and performance.

How can I identify if my dataset has been poisoned?

You can look for anomalies in data distribution, unexpected model behavior, or performance drops on validation sets.

Are there specific techniques to prevent data poisoning?

Yes, techniques include data validation, anomaly detection, and using robust training methods to mitigate the impact.

What tools can help me monitor my data for poisoning?

Tools like TensorFlow Data Validation and custom anomaly detection scripts can help monitor and analyze your datasets.