College of Computing and Informatics
Bridge the gap between humans and AI by comparing, and contrasting the ability of eXplainable AI methods to explain neural nets.
Problem: Complex AI systems are hard to be interpreted by human users Consequences of the problem: accountability, transparency, and ethics Fact: no ground truth models are being used to guide the scientific advance of XAI