Module 01 · Foundations · Interactive lesson
Train a
Classifier
A classifier is just a line drawn through data. Here, 120 veterans wait to be sorted into a benefits fast-track or a manual-review queue. Move the line by hand, or let gradient descent train it — then watch what the most accurate line does to fairness.
Decision boundary
step 0
Live metrics
Accuracy
72%
86/120 correct
Fairness gap
10%
within limit
Tune the model
What training is
Gradient descent nudges the three numbers — two weights and a bias — a little each step to reduce error. Press Auto-train and watch the boundary slide into place on its own.
Accuracy isn't fairness
Because Reserve records skew lower on documentation, the most accurate line fast-tracks Active veterans far more often. Accuracy climbs while the fairness gap widens past the limit.
Why governance
A model can be 'right' on average and still systematically disadvantage a group. The Guardrailed Classroom measures that gap and blocks the deploy until it's closed.
Why this matters
Optimizing for accuracy alone is not a neutral act. The same training run that makes the model more accurate can make it less fair— which is exactly why a scholar in the Guardrailed Classroom doesn't just train a model, they train it under a guardrail that watches the gap.
Mission first, people always.