Explainable AI/ XAI

Definition

Explainable AI is methods that show why an AI system produced a prediction, recommendation, or action. In defense applications, it helps operators challenge, calibrate, and document AI-supported decisions. The hard part is cosmetic explanations that do not reflect actual model behavior, especially when systems are deployed across contested links, coalition boundaries, and mixed human-machine teams. KhanBMS treats it as a required companion to KhanBMS recommendations that affect mission decisions, tying the concept back to modular command, edge execution, and auditable authority.

Reference attributes

Layer

trust and transparency discipline

Operational value

Helps operators challenge, calibrate, and document AI-supported decisions

Primary risk

Cosmetic explanations that do not reflect actual model behavior

KhanBMS role

A required companion to KhanBMS recommendations that affect mission decisions

Related terms

• Interpretable Machine Learning (IML)
  Modeling approaches whose internal logic can be understood directly or with minimal post-hoc explanation.
• Mechanistic Interpretability
  Analysis of internal neural-network circuits, features, and representations to understand model behavior.
• Decision Support AI (DSAI)
  AI systems that synthesize data, alternatives, risk, and explanations for commanders or operators.
• Confidence Calibration
  Ensuring model confidence scores correspond to real-world likelihood of being correct.