Charles River Analytics Inc., developer of intelligent systems solutions, created the Causal Models to Explain Learning (CAMEL) approach to help artificial intelligence effectively communicate with human teammates.

Under DARPA’s Explainable Artificial Intelligence (XAI) effort, we led a team that included Brown University, the University of Massachusetts at Amherst, and Roth Cognitive Engineering. The team developed probabilistic causal modeling techniques and an interpretive interface that enable users to naturally interact with machines. CAMEL simplifies explanations of how these complex, deep learning machines work.

Our approach will significantly impact the way that machine learning systems are deployed, operated, and used inside and outside the Department of Defense (DoD).

Learn more about how CAMEL makes it easier for humans and machines to communicate.