Swarm AI decision-making is a black box that prevents after-action review of why the swarm acted as it did

When a drone swarm autonomously selects targets, routes, and engagement sequences using neural network-based planning, the decision process is not human-interpretable. After an engagement, commanders cannot reconstruct why the swarm prioritized target A over target B, or why it chose a route that resulted in collateral damage. This makes after-action review, accountability, and doctrine refinement impossible. This persists because the neural networks used for swarm planning are inherently opaque (millions of parameters with no symbolic reasoning trace), and explainable AI research has not produced methods that work in real-time for multi-agent autonomous systems.