A model-based evaluator rates responses highly, but sampled human review finds systematic errors. What is the appropriate response?

1. Validate the evaluator and calibrate it against trusted task-specific evidence
2. Remove all human-reviewed failures from the dataset
3. Promote solely because the score is numeric
4. Assume an evaluator can never be wrong