Description

As organizations deploy AI to automate and influence decisions, the ability to demonstrate how those decisions were made has become a core compliance and legal requirement. Regulators, auditors, and legal teams increasingly expect clear, traceable evidence linking data, models, and outcomes.

Evidence Trails for AI Decisions is a methodical guide for compliance, legal, and risk professionals responsible for documenting AI-driven activities in a defensible and reviewable manner. The book focuses on practical mechanisms for creating and maintaining evidence trails that withstand audit, investigation, and regulatory scrutiny.

This volume translates abstract governance and transparency requirements into concrete documentation practices, logs, and control artifacts. It emphasizes consistency, completeness, and usability of evidence rather than theoretical explainability.

Key areas covered include:

Decision logging and event capture for AI systems Data lineage, input provenance, and transformation records Model actions, overrides, and human-in-the-loop evidence Change logs for models, configurations, and thresholds Retention, access control, and evidentiary integrity Templates and tooling to operationalize audit-ready trails

Designed for organizations operating in regulated or high-risk environments, this book provides repeatable structures to support investigations, audits, and legal defensibility while enabling responsible use of AI at scale.