Data Poisoning

Data poisoning in artificial intelligence refers to the intentional act of corrupting the data used to train an AI system to manipulate its behavior or degrade its performance.To elaborate, AI systems learn and make decisions based on the data they're trained on. In the context of "Data poisoning," malicious actors intentionally introduce misleading or inaccurate data into the training set with the intent of causing harm. The effects can vary: the AI might make wrong decisions, show biased behavior, or even fail to function properly. For example, an autonomous car's AI, if subjected to data poisoning, might be tricked into misinterpreting road signs, leading to potentially dangerous consequences. Hence, it's crucial to ensure the integrity and quality of the data used to train AI systems, as well as the robustness of the models against such attacks.

Summarizing, data poisoning refers to the damaging act of distorting training data to manipulate the behavior or impair the effectiveness of an AI system.