Make It Reconstructable: Traceable and Reliable Use
Explore how to apply DoD Responsible AI principles by building traceable and reliable AI use practices. Learn to document sources, human reviews, and output details to support reconstruction. Understand how to define intended use and acceptance criteria, and when to escalate missing evidence to maintain trustworthy AI use in mission-critical workflows.
We'll cover the following...
The last lesson treated a pattern in outcomes as evidence, because a neutral-looking ranking can still create unequal impact. The same evidence habit matters when the output is not a ranking but a polished paragraph. A fluent summary can carry a claim that sounds sourced, even when no source exists.
An analyst drafts a briefing slide using an AI-generated summary that includes a line like, “Per internal report, the adversary’s access window began 14 days earlier than previously assessed.” The sentence reads like it came from a document someone could pull. No one can find that report, and the tool output does not show where the line came from. The risk is not only that the detail is wrong. The risk is that, later, no one can reconstruct what was relied on, what was checked, and who made the judgment to treat it as true enough.