AI vs. Explainability: How to Make Autonomous Systems and Still Understand What’s Going On

Autonomous systems are getting faster and smarter, thanks to software like Swarmer AI. Shared awareness enables drones to navigate contested airspace, divide targets among themselves, adapt to electronic warfare in real time and complete missions without continuous human input. That capability is exactly what modern conflict demands.

But, the more decisions an autonomous system makes on its own, the more important it becomes to understand why it makes them. In high-stakes environments, "the model decided" is not an acceptable answer for commanders reviewing a failed mission, for legal accountability or for strategic system improvements.

Building systems that can act autonomously and still show their work remains a central challenge of explainable AI.

What Is Explainability in AI?

Explainability in AI refers to the ability to trace and understand how a system arrived at a particular decision - both the outcome and the reasoning that led to it.

AI itself is a broad category of methods used to make software perform tasks normally associated with human intelligence. Two important approaches are rule-based systems and machine learning. The distinction matters because they provide very different levels of predictability and explainability.

In a rule-based system, decision-making follows explicitly defined logic. Given a particular set of inputs, the system applies known rules to produce an output. Because that process is deterministic, it enables decisions to be reconstructed and swarm behaviour to be modified on the basis of auditable evidence.

Machine-learning systems work differently. Rather than having every decision rule explicitly specified by an engineer, these systems learn patterns from data. Neural networks are one type of machine-learning model. They can be highly effective for well-defined tasks such as object classification, trajectory prediction and sensor fusion.

For Swarmer, the important distinction is between where machine learning is useful and where deterministic, rule-based logic is required. "Machine learning can support specific perception and prediction tasks, while core coordination and decision logic can be built from defined mathematical rules whose outputs can be traced and audited," said Alex Fink, the US CEO of Swarmer. "For example, swarm allocation - assigning tasks according to the relative probability of success across platforms and targets - is fundamentally a linear-algebra problem rather than something that requires a general machine-learning decision model."

That architectural choice also allows operational constraints to be encoded directly into the system. Rules of engagement can be implemented as technical constraints that the software must obey, with compliance subsequently verified and audited.

In autonomous military systems, explainability therefore means more than being able to offer a plausible explanation after a decision. It means designing the decision process so that what the system was permitted to do, what information it acted on, which rules it applied, and why it produced a particular action can be reconstructed after the fact.

Where It Matters Most: The After Action Review

Explainability isn't something operators think about when a mission goes well. When a drone finds its target and completes its objective, the decision trail is largely invisible, and that's fine. Nobody needs to audit a successful outcome in real time.

"When something stands out, such as a different pattern in drone behavior, that is when you need the ability to investigate what was going on," said Vasyl Vlasov, Head of Engineering at Swarmer. "It's not about what it did. We need to know why something happened the way it did."

Every Swarmer mission generates a continuous record of telemetry data and sensor inputs. When a mission underperforms, that data becomes the basis for a structured post-mission investigation.

Vlasov likens it to showing your work on a math problem. The answer alone tells you little. To understand where something went wrong, you need to see every step that produced it.

In Swarmer's case, engineers can trace which drone saw which target, why one approach path was selected over another, and how the swarm redistributed after losing a member to jamming.

Explainability as Part of the Operational Loop

With Swarmer software, post-mission analysis feeds directly into the platform's improvement cycle: deploy, observe, adapt and improve. Operational data from missions informs model refinements that are pushed to the fleet via cloud update. This allows drones to benefit from lessons learned during prior swarm missions.

But not all refinements are automatic. When unexpected behaviors emerge, engineers rely on the decision trace to diagnose and correct them deliberately.

Without the ability to trace why a behavior occurred, engineers can't confidently adjust it. Changing a model parameter might improve one outcome while inadvertently degrading another. Transparency in the decision architecture is what allows engineers to make targeted, confident changes rather than educated guesses. Without that traceability, changing a model parameter might improve one outcome while inadvertently degrading another.

This is also why the feedback loop compounds over time. Swarmer's platform has supported more than 100,000 combat missions in Ukraine since April 2024, and each post-mission review adds to a body of operational knowledge that that Swarmer believes cannot be fully replicated through through laboratory simulations..

Explainability as an Ethical and Legal Requirement

Beyond engineering utility, explainability supports the responsible development and use of autonomous weapons systems. Swarmer designs its products to align with best practices concerning lawfulness, accountability, explainability, traceability and appropriate human oversight. Principles of this kind appear in ICRC's position on autonomous weapons systems and NATO's AI strategy.

Without explainability, a system's decisions cannot be meaningfully audited for rules-of-engagement (ROE) compliance, obstructing legal accountability.

Explainability is what keeps human authority meaningful even when autonomous systems are acting faster than humans can intervene. Operators must approve target engagements before the system acts, and the final call on lethal action stays with a person. But that judgment is only worth something if the operator can understand and trust the system's reasoning.

AI That Earns Human Trust

The most powerful autonomous systems are not the ones that operate with the least human involvement. They're the ones that have earned enough trust through demonstrated performance and transparent decision making.

Explainability is the foundation for that trust - giving operators confidence to delegate, engineers the ability to improve and oversight bodies the means to verify.

Building autonomous systems that can also show their work is not a constraint on capability, but the condition under which meaningful capability gets deployed.