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Steve Chien, Commissioner at the NSCAI, said.Additionally, according to Dr. And for that, we need robust and reliable AI, testing evaluation verification and validation, we need leadership among the different institutions that will be deploying the AI, we need rules of the road in accountability in governance, and we also need to develop patterns for human and AI interaction and teaming is a very critical point,” Dr. As part of the report, the Commission produced a detailed framework highlighting five issue areas and recommendations to guide AI’s responsible development and fielding across the national security community.“We recognize that establishing justified confidence in AI system is the critical issue in seeing AI systems deployed widespread. In that case, leaders will not adopt them, operators will not use them, Congress will not fund them, and the American people will not support them.

Clarify policies on human roles and functions, develop designs that optimize human-machine interaction, and provide ongoing and organization-wide AI training. Pursue a sustained, multidisciplinary initiative through national security research labs to enhance human-AI teaming. Consult interdisciplinary groups of experts to conduct risk assessments, improve documentation practices, and build overall system architectures to limit the consequences of system failure.
Establish policies that allow individuals to raise concerns about irresponsible AI development and institute comprehensive oversight and enforcement practices. Adapt and extend existing accountability policies to cover the full lifecycle of AI systems and their components. Create a standing body of multidisciplinary experts in the National AI Initiative Office. Appoint a full-time, senior-level Responsible AI lead in each department or agency critical to national security and each branch of the armed services. National Institute of Standards and Technology (NIST) should provide and regularly refresh a set of standards, performance metrics, and tools for qualified confidence in AI models, data, and training environments, and predicted outcomes.
