Advanced•8–12 hours

AI Grader Calibration & Reliability Lab

Build an evaluation workflow in which a model-based grader is calibrated against human-reviewed evidence, with a disagreement policy, risk slices, and a version-bound release gate.

grader calibrationevaluation designhuman reviewrisk slicingrelease gating

Scenario

Task

A team evaluates hundreds of AI responses with a model-based grader. The aggregate score looks stable, but critical edge cases and disagreement with human reviewers disappear inside the average. You need to prove when the grader is fit for release decisions and when its verdict must be blocked or sent to adjudication.

Step-by-step execution

1. Establish the human baseline

Outcome: Ground truth has provenance and does not confuse expert disagreement with grader error.

Tasks

→Select representative normal, edge, and high-impact cases
→Have at least two reviewers independently score the critical slice
→Record disagreement and adjudication rationale
→Version the dataset and rubric

Checks

✓A critical label does not come from one unreviewed judge
✓An ambiguous case has an explicit unresolved or adjudicated state

2. Freeze the grader fingerprint

Outcome: A repeated run can be reproduced and explained.

Tasks

→Record model and revision
→Store the grader prompt and rubric
→Record inference settings and parsing schema
→Add invalid, refusal, and timeout states

Checks

✓There is no silent fallback to another model revision
✓Invalid output is never converted into PASS

3. Measure reliability by risk slice

Outcome: The aggregate score no longer hides expensive failure classes.

Tasks

→Compute agreement and the confusion matrix
→Measure severe false-pass separately
→Test multilingual, no-answer, and adversarial slices
→Analyze unstable verdicts across repeated trials

Checks

✓High-severity false-pass has its own blocking threshold
✓There is an explicit list of cases for human adjudication

4. Turn calibration into a release gate

Outcome: The grader is used only within its demonstrated scope of reliability.

Tasks

→Define PASS, REVIEW, BLOCKED, and UNKNOWN policy
→Require recalibration after model, prompt, or rubric changes
→Feed production disagreement into the regression loop
→Store a machine-readable report in CI

Checks

✓A grader fingerprint change invalidates previous calibration evidence
✓An override has an owner, rationale, and audit trail

Acceptance criteria

A versioned human-reviewed calibration set exists
The grader fingerprint is fully reproducible
High-severity false-pass is measured separately
Disagreement has an adjudication path
The release gate never treats UNKNOWN or invalid output as PASS