State-of-the-art in human-centric studies of AI-enhanced situational awareness within the security domain

Abstract

Advanced situational awareness and decision-making systems in the security domain heavily build upon versatile combinations of different artificial intelligence models, potentially including their earlier versions. Often, the broad variety of implemented algorithms results in complex system architectures which may challenge human comprehension of expert users. System performance is frequently evaluated by different technical metrics against various data sets, such as model accuracy, precision, and recall. However, without any consideration of human-autonomy teaming or human-system interaction, the possibilities of executing comprehensive system assessments are likely to remain limited. This systematic review examines the current state-of-the-art in human-centric studies on situational awareness systems applying machine learning or artificial intelligence as key technologies. Our findings are based on up to 40 studies that were identified in our literature searches. This paper outlines the transition in research on the domain and current trends. It also discusses the research gap on human-centric approach.

Keywords: Situational Awareness, Artificial Intelligence, Security, Human-System Interaction

DOI: 10.54941/ahfe1005808