Rethinking Technological Readiness in the Era of AI Uncertainty
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866913890738110464 |
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| author | Browne, S. Tucker Bailey, Mark M. |
| author_facet | Browne, S. Tucker Bailey, Mark M. |
| contents | Artificial intelligence (AI) is poised to revolutionize military combat systems, but ensuring these AI-enabled capabilities are truly mission-ready presents new challenges. We argue that current technology readiness assessments fail to capture critical AI-specific factors, leading to potential risks in deployment. We propose a new AI Readiness Framework to evaluate the maturity and trustworthiness of AI components in military systems. The central thesis is that a tailored framework - analogous to traditional Technology Readiness Levels (TRL) but expanded for AI - can better gauge an AI system's reliability, safety, and suitability for combat use. Using current data evaluation tools and testing practices, we demonstrate the framework's feasibility for near-term implementation. This structured approach provides military decision-makers with clearer insight into whether an AI-enabled system has met the necessary standards of performance, transparency, and human integration to be deployed with confidence, thus advancing the field of defense technology management and risk assessment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11001 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rethinking Technological Readiness in the Era of AI Uncertainty Browne, S. Tucker Bailey, Mark M. Software Engineering Artificial Intelligence Computers and Society Machine Learning Artificial intelligence (AI) is poised to revolutionize military combat systems, but ensuring these AI-enabled capabilities are truly mission-ready presents new challenges. We argue that current technology readiness assessments fail to capture critical AI-specific factors, leading to potential risks in deployment. We propose a new AI Readiness Framework to evaluate the maturity and trustworthiness of AI components in military systems. The central thesis is that a tailored framework - analogous to traditional Technology Readiness Levels (TRL) but expanded for AI - can better gauge an AI system's reliability, safety, and suitability for combat use. Using current data evaluation tools and testing practices, we demonstrate the framework's feasibility for near-term implementation. This structured approach provides military decision-makers with clearer insight into whether an AI-enabled system has met the necessary standards of performance, transparency, and human integration to be deployed with confidence, thus advancing the field of defense technology management and risk assessment. |
| title | Rethinking Technological Readiness in the Era of AI Uncertainty |
| topic | Software Engineering Artificial Intelligence Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2506.11001 |