Building AI Systems That Survive an Audit: Evidence Trails, Traceability and Compliance by Design
A model returns an answer with a confidence score of 0.94. The team ships it. Six months later someone asks why the system produced that specific answer, and nobody can reconstruct it. For years accuracy was the only number that mattered in machine learning. Get the error rate down, ship the model, move on. In regulated domains that is no longer enough. The harder question is whether you can defend a single decision after it has been made. Most systems were never built to answer that, and by the time someone asks, the information needed is already gone.
