Robust AI

Robust AI research aims to create artificial intelligence systems that are reliable, resilient, and trustworthy, even when faced with unexpected inputs, adversarial attacks, or changing environments. Current efforts focus on developing defenses against adversarial examples and prompt injection attacks, improving model interpretability and explainability, and designing robust architectures such as hybrid neuro-symbolic systems and hierarchical reinforcement learning frameworks. This work is crucial for ensuring the safe and effective deployment of AI in high-stakes applications like healthcare, finance, and autonomous systems, ultimately fostering greater trust and wider adoption of AI technologies.