R0044/2026-03-29/Q002/S03
WebSearch — Military AI overreliance and targeting harm
Summary
| Field |
Value |
| Source/Database |
WebSearch |
| Query terms |
AI agreeable output military harm case study near-miss "target identification" OR "intelligence analysis" OR "decision support" overreliance trusted wrong |
| Filters |
None |
| Results returned |
10 |
| Results selected |
3 |
| Results rejected |
7 |
Selected Results
| Result |
Title |
URL |
Rationale |
| S03-R01 |
ICRC: Risks of AI in military targeting support |
https://blogs.icrc.org/law-and-policy/2024/09/04/the-risks-and-inefficacies-of-ai-systems-in-military-targeting-support/ |
Authoritative IHL analysis |
| S03-R02 |
Research on Military AI Risk Governance (Marvin Project) |
https://dl.acm.org/doi/full/10.1145/3748825.3748926 |
Quantified operator trust rates |
| S03-R03 |
Military AI Needs Technically-Informed Regulation |
https://arxiv.org/html/2505.18371v1 |
Over-trust and degraded vigilance analysis |
Rejected Results
| Result |
Title |
URL |
Rationale |
| S03-R04 |
Various military AI ethics articles |
Multiple URLs |
Policy analysis rather than empirical evidence or case studies |
Notes
Military AI evidence is the hardest to obtain in specific incident form. The Marvin Project data is the most concrete, but is cited secondarily. Classified military incidents involving AI over-reliance would not appear in open-source searches.