Risks and Control Measures for Building Trustworthy Autonomous Weapon Systems

This research examines the risks and control measures associated with building trustworthy Autonomous Weapon Systems (AWS), a rapidly evolving technology with various implications for military operations and international security. While AWS present advantages in precision and efficiency, they also imply operational, technical, and ethical challenges. Through a comprehensive analysis of relevant studies, this article identifies key risks inherent in AWS development, including algorithmic biases, unintended engagements, and cyber security vulnerabilities. For these, control measures are proposed to mitigate and avoid them, such as advanced fail-safe mechanisms, multi-layered human oversight protocols, and robust cyber security solutions. Particular attention is given to the role of meaningful human control as a fundamental mechanism for enhancing AWS trustworthiness without compromising operational effectiveness. The findings highlight the need for a dynamic, proactive, multidisciplinary risk-based approach to AWS development as trustworthy systems, emphasising the importance of international collaboration in establishing standardised risk assessment methodologies, trustworthiness benchmarks, and certification processes. Moreover, by systematically analysing both risks and control measures, this research provides a design framework for addressing the complex challenges of building trustworthy AWS in the context of evolving warfare technologies.

Authors

Institutions

Publication Details

Journal
International Conference on Cyber Warfare and Security
Published
2025-03-24
DOI
https://doi.org/10.34190/iccws.20.1.3191
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Risks and Control Measures for Building Trustworthy Autonomous Weapon Systems

Clara Maathuis, Kasper Cools
International Conference on Cyber Warfare and Security
Adversarial Robustness in Machine Learning
article

Risks and Control Measures for Building Trustworthy Autonomous Weapon Systems

Clara Maathuis, Kasper Cools
article en

Abstract

This research examines the risks and control measures associated with building trustworthy Autonomous Weapon Systems (AWS), a rapidly evolving technology with various implications for military operations and international security. While AWS present advantages in precision and efficiency, they also imply operational, technical, and ethical challenges. Through a comprehensive analysis of relevant studies, this article identifies key risks inherent in AWS development, including algorithmic biases, unintended engagements, and cyber security vulnerabilities. For these, control measures are proposed to mitigate and avoid them, such as advanced fail-safe mechanisms, multi-layered human oversight protocols, and robust cyber security solutions. Particular attention is given to the role of meaningful human control as a fundamental mechanism for enhancing AWS trustworthiness without compromising operational effectiveness. The findings highlight the need for a dynamic, proactive, multidisciplinary risk-based approach to AWS development as trustworthy systems, emphasising the importance of international collaboration in establishing standardised risk assessment methodologies, trustworthiness benchmarks, and certification processes. Moreover, by systematically analysing both risks and control measures, this research provides a design framework for addressing the complex challenges of building trustworthy AWS in the context of evolving warfare technologies.

International Conference on Cyber Warfare and SecurityVol. 20(1)
Hong Kong Metropolitan University (HK)
Openalex Percentile: Top 96%
Adversarial Robustness in Machine Learning
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.