Artificial Intelligence Strategies for National Security and Safety Standards
This addresses trust issues for national security agencies, but it is incremental as it applies existing standards rather than introducing new methods.
The paper tackles the problem of trust in AI systems for national security by proposing to apply standards, specifically Intelligence Community Directive 203, to machine outputs to ensure they meet the same rigorous standards as human analysis.
Recent advances in artificial intelligence (AI) have lead to an explosion of multimedia applications (e.g., computer vision (CV) and natural language processing (NLP)) for different domains such as commercial, industrial, and intelligence. In particular, the use of AI applications in a national security environment is often problematic because the opaque nature of the systems leads to an inability for a human to understand how the results came about. A reliance on 'black boxes' to generate predictions and inform decisions is potentially disastrous. This paper explores how the application of standards during each stage of the development of an AI system deployed and used in a national security environment would help enable trust. Specifically, we focus on the standards outlined in Intelligence Community Directive 203 (Analytic Standards) to subject machine outputs to the same rigorous standards as analysis performed by humans.