A knowledge model for explainable military AI
Forrest Hare, Founder of Summit Knowledge Solutions, is a retired US Air Force targeting and information operations officer who now works with the Defense Intelligence Agency (DIA). His experience includes integrating intelligence from different types of communications, signals, imagery, open source, telemetry, and other sources into a cohesive and actionable whole.
Hare became aware of semantics technology while at SAIC and is currently focused on building a space + time ontology called the DIA Knowledge Model so that Defense Department intelligence could use it to contextualize these multi-source inputs.
The question becomes, how do you bring objects that don’t move and objects that do move into the same information frame with a unified context? The information is currently organized by collectors and producers.
The object-based intelligence that does exist involves things that don’t move at all. Facilities, for example, or humans using phones that are present on a communications network are more or less static. But what about the things in between such as trucks that are only intermittently present?
Only sparse information is available about these. How do you know the truck that was there yesterday in an image is the same truck that is there today? Not to mention the potential hostile forces who own the truck that have a strong incentive to hide it.
Objects in object-based intelligence not only include these kinds of assets, but also events and locations that you want to collect information about. In an entity-relationship sense, objects are entities.
Hare’s DIA Knowledge Model uses the ISO standard Basic Formal Ontology (BFO) to unify domains so that the information from different sources is logically connected and therefore makes sense as part of a larger whole. BFO’s maintainers (Director Barry Smith and team at the National Center for Ontological Research (NCOR) at the University of Buffalo) keep the ontology strictly limited to 30 or so classes.
The spatial-temporal regions of the Knowledge Model are what’s essential to do the kinds of dynamic, unfolding object tracking that’s been missing from object-based intelligence. Hare gave the example of a “site” (an immaterial entity) from a BFO perspective. A strict geolocational definition of “site” makes it possible for both humans and machines to make sense of the data about sites. Otherwise, Hare says, “The computer has no idea how to understand what’s in our databases, and that’s why it’s a dumpster fire.”
This kind of mutual human and machine understanding is a major rationale behind explainable AI. A commander briefed by an intelligence team must know why the team came to the conclusions it did. The stakes are obviously high. “From a national security perspective, it’s extremely important for AI to be explainable,” Hare reminded the audience. Black boxes such as ChatGPT as currently designed can’t effectively answer the commander’s question on how the intel team arrived at the conclusions it did.
Finally, the level of explain-ability knowledge models like the DIA’s becomes even more critical as information flows into the Joint Intelligence Operations Center (JIOC). Furthermore, the various branches of the US Armed Forces must supply and continually update a Common Intelligence Picture that’s actionable by the US President, who’s the Commander in Chief for the military as a whole.
Without this conceptual and spatial-temporal alignment across all service branches, joint operations can’t proceed as efficiently and effectively as they should. Certainly, the risk of failure looms much larger as a result.
Contributed by Alan Morrison