Black Box Twins, White-Collar Liability: Building Explainable AI for Aviation

The adoption of predictive maintenance digital twins in aviation shifts liability from manufacturers to operators, creating a black box risk mitigated only through an Explainable AI (XAI) architecture. Digital twins in high-stakes maintenance have transitioned from efficiency tools to legal evidence. This dossier outlines a forensic methodology for building auditable AI systems applicable across high-risk regulated industries. By documenting model boundaries, quantifying prediction confidence, and ensuring human override capability, organisations can transform algorithmic output from liability into a defensible decision trail satisfying international safety and AI regulatory standards.