Why I build explainability in first, not last

Most teams treat explainability as a reporting step bolted on at the end. I treat it as a design constraint. If I cannot explain a prediction to the person it affects, the model is not finished.

The workflow

Start with a baseline you understand. Add complexity only when a plain model leaves accuracy on the table, and measure the interpretability cost each time. Wire SHAP into the evaluation loop so every candidate model ships with a feature-attribution report, not just a metric.

More on the tooling in the next post.