Studying the Effects of Robot Intervention on School Shooters in Virtual Reality
We advance the understanding of robotic intervention in high-risk scenarios by examining their potential to distract and impede a school shooter. To evaluate this concept, we conducted a virtual reality study with 150 university participants role-playing as a school shooter. Within the simulation, an autonomous robot predicted the shooter's movements and positioned itself strategically to interfere and distract. The strategy the robot used to approach the shooter was manipulated -- either moving directly in front of the shooter (aggressive) or maintaining distance (passive) -- and the distraction method, ranging from no additional cues (low), to siren and lights (medium), to siren, lights, and smoke to impair visibility (high). An aggressive, high-distraction robot reduced the number of victims by 46.6% relative to a no-robot control. This outcome underscores both the potential of robotic intervention to enhance safety and the pressing ethical questions surrounding their use in school environments.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Themes of Revenge: Automatic Identification of Vengeful Content in Textual Data
Revenge is a powerful motivating force reported to underlie the behavior of various solo perpetrators, from school shooters to right wing terrorists. In this paper, we develop an automated methodology for identifying ven…
Developing a Discrete-Event Simulator of School Shooter Behavior from VR Data
Virtual reality (VR) has emerged as a powerful tool for evaluating school security measures in high-risk scenarios such as school shootings, offering experimental control and high behavioral fidelity. However, assessing …
A Novel Robot-Assisted Learning Pedagogy for Children with ASD
Interaction paradigms used in robot-assisted autism intervention have historically employed robots as teachers, clinical assistants, or more-abled peers to promote a variety of social skills. These modalities often lever…
Machine Learning Analysis of Heterogeneity in the Effect of Student Mindset Interventions
We study heterogeneity in the effect of a mindset intervention on student-level performance through an observational dataset from the National Study of Learning Mindsets (NSLM). Our analysis uses machine learning (ML) to…
BIG-bench Machine LearningCausal Interventions for Fairness
Most approaches in algorithmic fairness constrain machine learning methods so the resulting predictions satisfy one of several intuitive notions of fairness. While this may help private companies comply with non-discrimi…
Fairness