Not Oracles of the Battlefield: Safety Considerations for AI-Based Military Decision Support Systems

AI-based military decision support systems that help commanders observe, orient, decide, and act on the battlefield are highly sought after by military leadership. With the advent of large language models, AI developers have begun advertising automated AI-based decision support systems designed to both analyze and act on data from the battlefield. While the desire to use decision support systems to make better decisions on the battlefield is unsurprising, the responsible deployment of such systems requires a clear understanding of the capabilities and limitations of modern machine learning models. This paper reviews recently proposed uses of AI-enables decision support systems (DSS), provides a simplified framework for considering AI-DSS capabilities and limitations, and recommends practical risk mitigations commanders might employ when operating with an AI-enabled DSS.

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Publication Details

Journal
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Published
2024-10-16
DOI
https://doi.org/10.1609/aies.v7i1.31712
Citations
3
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
1.04
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article

Not Oracles of the Battlefield: Safety Considerations for AI-Based Military Decision Support Systems

Matthew Burtell, Helen Toner, Tim G. J. Rudner, Emelia Probasco
3 citations
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Adversarial Robustness in Machine Learning
1.04
article

Not Oracles of the Battlefield: Safety Considerations for AI-Based Military Decision Support Systems

Matthew Burtell, Helen Toner, Tim G. J. Rudner, Emelia Probasco
article en
3 citations

Abstract

AI-based military decision support systems that help commanders observe, orient, decide, and act on the battlefield are highly sought after by military leadership. With the advent of large language models, AI developers have begun advertising automated AI-based decision support systems designed to both analyze and act on data from the battlefield. While the desire to use decision support systems to make better decisions on the battlefield is unsurprising, the responsible deployment of such systems requires a clear understanding of the capabilities and limitations of modern machine learning models. This paper reviews recently proposed uses of AI-enables decision support systems (DSS), provides a simplified framework for considering AI-DSS capabilities and limitations, and recommends practical risk mitigations commanders might employ when operating with an AI-enabled DSS.

Proceedings of the AAAI/ACM Conference on AI Ethics and SocietyVol. 7
Georgetown University (US), New York University (US)
Openalex Percentile: Top 18%
Adversarial Robustness in Machine Learning
1.04
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