Ignorance is No Bliss: Public Perception of Safety in a Drone-assisted Police Operation

The use of drones in public safety operations offers police departments potential benefits such as safety and efficiency. However, rarely considered are the perspectives of the public who are affected by the usage of drone in police operations. Ensuring and enhancing the sense of public safety is a primary goal of the police department. Therefore, public safety agencies and drone designers should consider the perception of bystanders at police operations and the broader society to ensure the fulfilment their primary purpose and maximizing drones' potential. This paper examines the factors that affect the public perception of safety during a field trial involving 80 test participants. Analysis of the group interviews reveal that unknowns are the main trigger of the sense of insecurity, including unknowns of drones' identity, purpose and usefulness. We found the public feels safer when the drones play an active role in delivering knowledge rather than passive role of data collection. Our findings highlight key design considerations for future drone-human interactions, providing insights into specific interaction features aimed at enhancing public perceptions of safety. We discuss the social and design implications to inform the public in solving the sense of insecurity mainly induced by a lack of knowledge.

Authors

Institutions

Publication Details

Journal
Proceedings of the ACM on Human-Computer Interaction
Published
2025-10-16
DOI
https://doi.org/10.1145/3757611
Primary Topic
Disaster Management and Resilience
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Ignorance is No Bliss: Public Perception of Safety in a Drone-assisted Police Operation

Daniela Wyss, Mateusz Dolata, Eleanor Wu, Gerhard Schwabe
Proceedings of the ACM on Human-Computer Interaction
Disaster Management and Resilience
article

Ignorance is No Bliss: Public Perception of Safety in a Drone-assisted Police Operation

Daniela Wyss, Mateusz Dolata, Eleanor Wu, Gerhard Schwabe
article en

Abstract

The use of drones in public safety operations offers police departments potential benefits such as safety and efficiency. However, rarely considered are the perspectives of the public who are affected by the usage of drone in police operations. Ensuring and enhancing the sense of public safety is a primary goal of the police department. Therefore, public safety agencies and drone designers should consider the perception of bystanders at police operations and the broader society to ensure the fulfilment their primary purpose and maximizing drones' potential. This paper examines the factors that affect the public perception of safety during a field trial involving 80 test participants. Analysis of the group interviews reveal that unknowns are the main trigger of the sense of insecurity, including unknowns of drones' identity, purpose and usefulness. We found the public feels safer when the drones play an active role in delivering knowledge rather than passive role of data collection. Our findings highlight key design considerations for future drone-human interactions, providing insights into specific interaction features aimed at enhancing public perceptions of safety. We discuss the social and design implications to inform the public in solving the sense of insecurity mainly induced by a lack of knowledge.

Proceedings of the ACM on Human-Computer InteractionVol. 9(7)
Zeppelin Universität gemeinnützige GmbH (DE), University of Zurich (CH)
Openalex Percentile: Top 61%
Disaster Management and Resilience
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.