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Updated: Aug 16, 2025

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training
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Resolving responsibility gaps for lethal autonomous weapon systems.

Patrick Taylor Smith1

  • 1Stockdale Center for Ethical Leadership, United States Naval Academy, Annapolis, MD, United States.

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|December 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for understanding collective responsibility in artificial intelligence (AI) failures. It addresses the "problem of many hands" and "responsibility gaps," particularly for autonomous weapon systems.

Keywords:
ethics of artificial intelligenceethics of technologylethal autonomous weapon systemsmilitary ethicsresponsibilityresponsibility gaps

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Area of Science:

  • Artificial Intelligence Ethics
  • AI Governance
  • Moral Philosophy

Background:

  • The increasing complexity of AI systems creates challenges in assigning accountability for failures.
  • Existing legal and ethical frameworks struggle with distributed AI development and deployment.
  • Lethal autonomous weapon systems (LAWS) highlight the urgency of addressing AI responsibility gaps.

Purpose of the Study:

  • To propose a novel conceptualization of collective responsibility applicable to AI outcomes.
  • To provide a framework for resolving the 'problem of many hands' in AI failure scenarios.
  • To enhance ethical and legal clarity surrounding AI accountability, especially for LAWS.

Main Methods:

  • Conceptual analysis of responsibility attribution in multi-agent systems.
  • Examination of philosophical concepts related to collective action and moral agency.
  • Application of the proposed framework to case studies involving AI failures.

Main Results:

  • A redefined understanding of collective responsibility that accounts for distributed AI agency.
  • Identification of specific mechanisms to bridge responsibility gaps in AI development and deployment.
  • Demonstration of how the framework mitigates the 'problem of many hands' for AI systems.

Conclusions:

  • The proposed framework offers a viable solution for assigning collective responsibility for AI outcomes.
  • Addressing responsibility gaps is crucial for the ethical development and deployment of AI, including LAWS.
  • This work contributes to establishing clearer AI governance and accountability structures.