AIPOM Eval Scorecard Builder
What Is It
Define how AI behavior will be measured and converted into product decisions: metrics, rubrics, judges, calibration, thresholds, sampling, uncertainty, subgroup views, critical failures, ownership, and action rules.
Why Use It
Metric collections fail when they lack representative cases, calibrated judgment, consequences, or decision thresholds. A scorecard makes clear what a passing score permits—and what a critical failure blocks regardless of averages.
When to Use It
Use after behavior and representative cases are defined, before validation, launch, scale, or recurring production review. Recalibrate when behavior, population, context, judges, or consequences change.
What It Produces
- Metric and rubric definitions
- Judge, calibration, sampling, and uncertainty plan
- Thresholds, critical-failure rules, and subgroup views
- Continue, revise, constrain, rollback, or stop decision rules
Who Should Participate
Include product and evaluation owners, domain experts, data and engineering, affected-user perspectives, and governance partners where consequences are material.
Evidence to Bring
Bring behavior contracts, evaluation strategy, governed cases, baselines, incidents, consequences, subgroup needs, reviewer guidance, judge comparisons, and candidate thresholds.
How to Do It
- Define the product decision and behavior dimensions being evaluated.
- Select metrics and rubrics that distinguish quality, safety, workflow, human, and outcome evidence.
- Define unit, denominator, direction, aggregation, subgroup, and uncertainty for every measure.
- Choose human, automated, model-based, or hybrid judges according to consequence and explainability needs.
- Calibrate judges against qualified reference decisions and measure disagreement.
- Define sampling across representative, edge, adversarial, subgroup, and production cases.
- Set evidence-based thresholds and non-compensable critical failures.
- Map results to continue, revise, constrain, rollback, or stop actions.
- Assign measurement, review, decision, exception, and recalibration ownership.
Key Concepts
- A metric without a decision rule is observation, not governance.
- Aggregate performance can hide subgroup or critical failures.
- Model judges require calibration and monitoring.
- Thresholds express consequence and tolerance, not universal truth.
Organizational Applications
Use for pre-release evaluation, regression tests, vendor comparison, human-review quality, workflow adoption, production monitoring, and rollback decisions.
Common Pitfalls
- Choosing metrics because tools expose them
- Using averages to hide critical failures
- Treating model judges as objective
- Setting thresholds after seeing desired results
- Omitting sampling and uncertainty
- Assigning measurement without decision authority
Combine With
Use aipom-evaluation-strategy-advisor for coverage, aipom-production-evidence-review for recurring decisions, and aipom-risk-control-incident-playbook for threshold-triggered response.
Assets and Templates
- Eval scorecard template
- Synthetic worked example
- Weak example
Sources
- NIST, Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile, NIST AI 600-1, July 26, 2024, updated April 8, 2026. Supports documented measurement, representative evaluation, monitoring, and risk-based decision practices. Accessed July 17, 2026.
1---2name: aipom-eval-scorecard-builder3description: Define calibrated AI evaluation metrics, rubrics, judges, thresholds, sampling, uncertainty, ownership, and decision rules tied to behavior and consequences.4---56# AIPOM Eval Scorecard Builder78## What Is It910Define how AI behavior will be measured and converted into product decisions: metrics, rubrics, judges, calibration, thresholds, sampling, uncertainty, subgroup views, critical failures, ownership, and action rules.1112## Why Use It1314Metric collections fail when they lack representative cases, calibrated judgment, consequences, or decision thresholds. A scorecard makes clear what a passing score permits—and what a critical failure blocks regardless of averages.1516## When to Use It1718Use after behavior and representative cases are defined, before validation, launch, scale, or recurring production review. Recalibrate when behavior, population, context, judges, or consequences change.1920## What It Produces2122- Metric and rubric definitions23- Judge, calibration, sampling, and uncertainty plan24- Thresholds, critical-failure rules, and subgroup views25- Continue, revise, constrain, rollback, or stop decision rules2627## Who Should Participate2829Include product and evaluation owners, domain experts, data and engineering, affected-user perspectives, and governance partners where consequences are material.3031## Evidence to Bring3233Bring behavior contracts, evaluation strategy, governed cases, baselines, incidents, consequences, subgroup needs, reviewer guidance, judge comparisons, and candidate thresholds.3435## How to Do It36371. Define the product decision and behavior dimensions being evaluated.382. Select metrics and rubrics that distinguish quality, safety, workflow, human, and outcome evidence.393. Define unit, denominator, direction, aggregation, subgroup, and uncertainty for every measure.404. Choose human, automated, model-based, or hybrid judges according to consequence and explainability needs.415. Calibrate judges against qualified reference decisions and measure disagreement.426. Define sampling across representative, edge, adversarial, subgroup, and production cases.437. Set evidence-based thresholds and non-compensable critical failures.448. Map results to continue, revise, constrain, rollback, or stop actions.459. Assign measurement, review, decision, exception, and recalibration ownership.4647## Key Concepts4849- A metric without a decision rule is observation, not governance.50- Aggregate performance can hide subgroup or critical failures.51- Model judges require calibration and monitoring.52- Thresholds express consequence and tolerance, not universal truth.5354## Organizational Applications5556Use for pre-release evaluation, regression tests, vendor comparison, human-review quality, workflow adoption, production monitoring, and rollback decisions.5758## Common Pitfalls5960- Choosing metrics because tools expose them61- Using averages to hide critical failures62- Treating model judges as objective63- Setting thresholds after seeing desired results64- Omitting sampling and uncertainty65- Assigning measurement without decision authority6667## Combine With6869Use `aipom-evaluation-strategy-advisor` for coverage, `aipom-production-evidence-review` for recurring decisions, and `aipom-risk-control-incident-playbook` for threshold-triggered response.7071## Assets and Templates7273- [Eval scorecard template](template.md)74- [Synthetic worked example](examples/worked-example.md)75- [Weak example](examples/weak-example.md)7677## Sources7879- NIST, [Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile, NIST AI 600-1](https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence), July 26, 2024, updated April 8, 2026. Supports documented measurement, representative evaluation, monitoring, and risk-based decision practices. Accessed July 17, 2026.
Dean Peters