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.
Authors
- Matthew Burtell
- Helen Toner (ORCID: https://orcid.org/0000-0002-2465-2682)
- Tim G. J. Rudner
- Emelia Probasco
Institutions
- Georgetown University (US)
- New York University (US)
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