Robustness Evaluation

Robustness evaluation assesses the reliability and stability of machine learning models under various perturbations and unexpected inputs, aiming to ensure their safe and effective deployment in real-world applications. Current research focuses on developing comprehensive benchmarks and metrics to evaluate robustness across diverse domains, including natural language processing, computer vision, and reinforcement learning, often employing adversarial attacks and data augmentation techniques to stress-test models. This field is crucial for building trustworthy AI systems, as robust models are less susceptible to errors and failures caused by noisy data, adversarial attacks, or unexpected environmental conditions, ultimately improving the safety and reliability of AI-driven technologies.