What is Data Poisoning?

Data poisoning refers to a specific type of adversarial attack that targets machine learning systems by introducing misleading or false data into the training dataset. The primary goal of this malicious activity is to degrade the performance of the model, skew its predictions, or mislead the overall decision-making process. By injecting carefully crafted incorrect information, attackers can compromise the integrity of the model and cause it to make erroneous predictions or automate flawed decision-making based on the corrupted data.

This type of attack is particularly concerning in environments where machine learning algorithms are used to make significant decisions, as poisoned data can result in the model learning from distortions rather than accurate representations of real-world data. Understanding data poisoning is crucial for those working with AI governance, as it highlights the importance of data quality and the need for robust security measures to protect the integrity of training datasets.

The other choices do not accurately capture the essence of data poisoning. Enhancing model performance and increasing data integrity are opposite to what data poisoning seeks to achieve, while monitoring data usage does not address the mechanisms through which attackers can undermine machine learning models.