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Resilience Engineering for Visual Screening in Security Settings (REVS)

In the realm of safety science, a paradigm shift towards resilience engineering offers new insights into addressing the challenges of complex systems, particularly in high-stakes environments like security screening. This project, Resilience Engineering for Visual Screening in Security Settings (REVS), aims to advance the understanding of resilience engineering principles within the context of security screening tasks. By investigating human-AI interaction and decision performance data from AI-enabled systems, REVS seeks to identify factors influencing system resilience and develop statistical models to quantify these effects.


REVS focuses on two AI-enabled decision support systems: an automated face-matching biometrics screening system and an “explainable AI” (XAI) baggage-screening system. By analyzing human-AI interaction data, the project aims to uncover previously unaccounted-for task environment factors that impact system resilience. This includes developing statistical models to assess the meaningful involvement of human operators and causal inference of task environment factors affecting system resilience in AI-enabled screening tasks. Additionally, REVS will generate new datasets in baggage screening visual detection tasks for broader community access.


The outcomes of REVS will have both intellectual merit and broader impacts. By advancing resilience engineering principles in security screening, the project will contribute to the development of more adaptable and robust systems. This will enhance security screening operations, improve decision-making processes, and ultimately increase safety in high-criticality environments. Furthermore, the project’s datasets and findings will be valuable resources for researchers and practitioners in the fields of safety science and AI.

Research Leadership Team 

Principal Investigator: Mickey Mancenido, Arizona State University
Co-PI: Erin Chiou, Arizona State University
Co-PI: Ross Maciejewski, Arizona State University

Data analytics


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The CAOE is committed to developing innovative tools and techniques to safeguard our homeland from potential threats and vulnerabilities.