A Risk-Based Regulatory Approach To Autonomous Weapon Systems

Abstract International regulation of autonomous weapon systems (AWS) is increasingly conceived as an exercise in risk management. This requires a shared approach for assessing the risks of AWS. This paper presents a structured approach to risk assessment and regulation for AWS, adapting a qualitative framework inspired by the Intergovernmental Panel on Climate Change (IPCC). It examines the interactions among key risk factors—determinants, drivers, and types—to evaluate the risk magnitude of AWS and establish risk tolerance thresholds through a risk matrix informed by background knowledge of event likelihood and severity. Further, it proposes a methodology to assess community risk appetite, emphasising that such assessments and resulting tolerance levels should be determined through deliberation in a multistakeholder forum. The paper highlights the complexities of applying risk-based regulations to AWS internationally, particularly the challenge of defining a global community for risk assessment and regulatory legitimisation.

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Publication Details

Journal
Digital Society
Published
2025-04-01
DOI
https://doi.org/10.1007/s44206-025-00181-y
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

A Risk-Based Regulatory Approach To Autonomous Weapon Systems

Mariarosaria Taddeo, Luciano Floridi, Alexander Blanchard, Claudio Novelli
Digital Society
Adversarial Robustness in Machine Learning
article

A Risk-Based Regulatory Approach To Autonomous Weapon Systems

Mariarosaria Taddeo, Luciano Floridi, Alexander Blanchard, Claudio Novelli
article en

Abstract

Abstract International regulation of autonomous weapon systems (AWS) is increasingly conceived as an exercise in risk management. This requires a shared approach for assessing the risks of AWS. This paper presents a structured approach to risk assessment and regulation for AWS, adapting a qualitative framework inspired by the Intergovernmental Panel on Climate Change (IPCC). It examines the interactions among key risk factors—determinants, drivers, and types—to evaluate the risk magnitude of AWS and establish risk tolerance thresholds through a risk matrix informed by background knowledge of event likelihood and severity. Further, it proposes a methodology to assess community risk appetite, emphasising that such assessments and resulting tolerance levels should be determined through deliberation in a multistakeholder forum. The paper highlights the complexities of applying risk-based regulations to AWS internationally, particularly the challenge of defining a global community for risk assessment and regulatory legitimisation.

Digital SocietyVol. 4(1)
Stockholm International Peace Research Institute (SE), Yale University (US), University of Oxford (GB), Data Power Decisions (United States) (US), University of Bologna (IT)
Openalex Percentile: Top 97%
Adversarial Robustness in Machine Learning
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