What is data poisoning in AI?
An example of an AI chatbot conversation about data poisoning in AI systems.
What is data poisoning in AI?
Data poisoning is a type of attack where malicious data is injected into a training dataset, compromising the model's performance.
How does data poisoning affect machine learning models?
It can lead to inaccurate predictions, reduced reliability, and can even cause the model to behave in unintended ways.
Can you give an example of data poisoning?
Sure! An example is when an attacker adds misleading labels to images in a dataset, causing the model to misclassify objects.
What are some ways to prevent data poisoning?
Preventive measures include data validation, anomaly detection, and using robust training techniques to mitigate the impact of poisoned data.
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