Dr. Quinn is a nationally recognized expert in the social and behavioral sciences with more than 30 years of experience. Her work currently focuses on behavioral applications for intelligence exploitation, Human Machine Teaming, Human Systems Integration, Human Centered Design, trust and ethics of Artificial Intelligence, training, and upskilling. She has designed quasi-experimental, single subject, usability validation, and survey research and data analysis methodologies. She is an expert on protection of human subjects and global privacy policies.
She conducts research into the understanding of Manned/UnManned Teaming (MUM-T), shared situation awareness, and calibration of operational end users’ trust in the artificial intelligence capabilities and decision making for the US Air Force Research Laboratory. She led conduct of human use research that evaluated Army intelligence analyst performance using the DARPA Insight suite of automated software tools for intelligence, surveillance, and reconnaissance. She employed a comprehensive assessment framework that measured workload, situation awareness, trust, usability, and usefulness in conjunction with mission/ task-specific metrics. Initially, analysts refused to use the automated analysis features. Dr. Quinn led the redesign of the user interface and modified the training provided to the analysts to enhance the transparency and understandability of the automation. Analysts subsequently embraced the automation and significant gains were obtained in mission performance.Dr. Quinn led development and delivery of training to instructors for the USAF Distributed Common Ground System (DCGS) Weapon Systems Trainer (DWST). Later, she led the feasibility study and prototype development effort for AFRL that evolved DWST into a simulation for training SIGINT analysts.
She is currently leading an internal Leidos project to incorporate Human System Integration methods and practice into the engineering and system life cycle development processes applied across the enterprise; and developing a multi-level human-machine teaming (HMT) performance measurement framework.
Dr. Quinn, a Leidos Technical Fellow, has conducted over 200 major presentations and keynote addresses at national and international conferences, and has authored more than 80 publications in peer-reviewed journals, 3 books, and 27 monographs. She recently led an IEEE workshop on Methods for Using the 2020 NIST Principles for AI Explainability.