Non-Robust AI
Non-Robust AI refers to artificial intelligence systems that are highly sensitive to small changes or perturbations in their input data, often leading to incorrect or unpredictable outputs. This concept highlights vulnerabilities in AI models, such as susceptibility to adversarial attacks, data distribution shifts, or noise, which can compromise reliability and safety in real-world applications. It contrasts with robust AI, which is designed to maintain performance under varying conditions.
Developers should learn about non-robust AI to understand and mitigate risks in AI deployment, especially in critical domains like healthcare, autonomous vehicles, or finance where failures can have severe consequences. This knowledge is essential for building more resilient systems, implementing adversarial training, and ensuring models generalize well beyond their training data, thereby enhancing trust and compliance with safety standards.