And decision support frameworks are designed to assist users in making informed, efficient, and effective decisions. These systems leverage not only data analytics, but predictive modeling and machine learning to process large datasets, identify patterns, and provide actionable recommendations. 

‍Human-Collaborative AI (H-CAI) Decision Support is a relatively new area which focuses on the technologies and techniques of conveying meaning to the human in a manner they will unambiguously understand, and conveying information, direction, and decisions to the AI/ML system such that it will unambiguously understand the human.  This two-way flow has all the classic elements of Natural Language Processing (NLP) in terms of conversation references, context, and flow patterns, but adds the elements of spatial and temporal reference as well as advanced UI/UX and cognitive psychology. Human-Collaborative AI (H-CAI) Decision Support seeks to find the perfect synergy between human operators and AI-based systems and build a bridge of communication and understanding between them. The human needs to understand the knowledge, issues, and decision analytics the AI has performed while the AI needs to understand the questions, direction, and ‘commander’s intent’ of the human operator. This is a critical challenge, as without this level of synergy, misunderstandings arise, mistakes are made, and human trust of the system is lost.

For the last five years, CSI has been working to establish a foundation for H-CAI in the domain of tactical logistics decision support. While much progress has been made, there are still critical open issues. CSI’s goal is to build on its foundational work to enable users to interact with a dynamic and adaptive AI/ML, working collaboratively to understand and address hard problems and emerging issues, while maintaining high levels of understanding, trust, and confidence.