Summary
Artificial intelligence (AI) systems increasingly assist with decisions in areas such as, medicine, science, and education, but they still make simple reasoning mistakes. Thus, they can be unreliable in high-stakes settings. This project develops a science of AI reliability by identifying why these systems fail and by designing principled methods to make them more dependable, both on their own and when working with humans. The project's novelties are a unified framework that connects model-level robustness to human-AI collaboration, grounded in controlled mathematical environments that isolate
