Audit Trails & Provenance

When an AI system makes or influences a decision, being able to trace backwards - what data went in, what model version was used, what the output was, who acted on it - is essential for accountability, debugging, and compliance. This is the audit trail, and for AI systems it's considerably more complex than for traditional software. The inputs may include real-time data that's no longer available. The model itself may have been updated since the decision was made. The interaction between the AI output and the human decision may not have been recorded. Data provenance - tracking where data came from, how it was processed, and what transformations it underwent - adds another layer. If a model was trained on data that later turns out to be biased, mislabelled, or improperly obtained, you need to know which decisions were affected. Building proper audit trails requires forethought and infrastructure: logging systems, version control for models and data, and decision records that capture the human element alongside the automated one. It's not glamorous work, but when something goes wrong - or when a regulator comes asking questions - it's invaluable.