Understanding AI-Enabled Outputs: A Conceptual and Technical Primer for All-Source Intelligence Analysts

Intelligence analysts are increasingly able, and required, to consume and interpret outputs generated by artificial intelligence (AI) enabled tools — yet most receive little training in what these outputs actually represent, or how these might be robustly evaluated. This primer addresses that gap. It argues that analysts do not need to know how to develop or operate AI tools in order to use these tools’ outputs critically and judiciously. But they do need sufficient conceptual understanding, and foundational technical knowledge, to evaluate these outputs competently. Three principles provide the framework for this understanding: First, the distinction between AI-facilitated outputs – where automation improves the pace, scale and fidelity of data collection, processing and analytical procedures that analysts could otherwise perform; and AI-generated outputs – where many of the novel outputs generated by the semi-autonomous techniques involved could not have been produced by analysts working independently; Second, the critical difference between interpolative and extrapolative estimation and mechanistic prediction; and Third, the critical dependencies and substantive limitations that govern the reproducibility and practical utility of all AI-facilitated and AI-generated outputs. Together these principles constitute the technical and conceptual foundations of the AI literacy training that all-source intelligence analysts should receive – the case for which is presented in a companion piece to this article.