Explainability in regulated AI: meeting the audit trail requirements

In regulated industries, an AI model that cannot explain itself is a liability waiting to happen. This whitepaper maps the technical and governance requirements for explainability across insurance underwriting, clinical decision support, and financial modelling. It connects emerging regulation to concrete engineering practices — model documentation, audit trails, and human-in-the-loop controls — so that your AI can withstand scrutiny from regulators and auditors alike.

What's inside

• What regulators expect from explainable AI systems
• Designing audit trails for automated decisions
• Documentation practices that satisfy compliance reviews
• Balancing model performance with interpretability