Presentation
Stop, Collaborate, and… Lessons Learned from Collaborative Human-Artificial Intelligence System Development
DescriptionThe U.S. military is procuring systems for complex joint fighting, requiring novel teaming approaches that maximize the capabilities of humans, artificial intelligence (AI), and autonomy. This case study describes a research and development effort to support aerial reconnaissance and target acquisition with manned and unmanned platforms. The effort integrated human factors (HF) with cutting edge AI techniques, using decision centered design to identify requirements of the reconnaissance work and multiple AI development approaches. The team developed a layered integrated system in a testbed environment for iterative development and user testing. Lessons learned include the following. First, the team applied Roth and colleagues’ macrocognitive synthesized framework of decision making. The framework’s emphasis on integrated human and AI sensemaking capabilities, as well as interfaces to facilitate common ground, facilitated cross-discipline collaboration. Second, the team collaborated on the goals and data of underlying AI features in parallel with (a) how the human would interact with the AI and (b) the display elements that facilitate the interaction. Third, scenario-based design was valuable to facilitate co-design activities. The HF team identified cognitive requirements and complexities to create storyboards. Embedding cognitive requirements within scenarios made complex military work accessible to the full engineering team.
Contributors
Event Type
Case Study
Industry/Practitioner Content
Lecture
TimeWednesday, October 15th11:50am - 12:10pm CDT
LocationGrand B
Cognitive Engineering & Decision Making
Human AI Robot Teaming (AI)