# The Hallucination Risk Scorecard

Rate the risk before it becomes the record.

## Product Promise
An interactive scorecard that quantifies hallucination risk across traceability, specificity, volatility, confidence language, and verification coverage.

## Intended Buyer
Audit teams, compliance officers, research managers, and consultants who need a defensible acceptance/rejection record.

## Included Deliverables
- Excel/Sheets-style scorecard CSV
- 15-page methodology
- Threshold interpretation guide
- Research, compliance, and policy templates

## Practitioner Workflow
1. Scoring dimensions: define the decision, evidence, owner, and acceptance threshold before use.
2. Weights: define the decision, evidence, owner, and acceptance threshold before use.
3. Thresholds: define the decision, evidence, owner, and acceptance threshold before use.
4. Sector adaptations: define the decision, evidence, owner, and acceptance threshold before use.
5. Audit log: define the decision, evidence, owner, and acceptance threshold before use.

## Operating Standard
Use this product as a practical governance aid, not as legal advice. For legal, regulatory, medical, financial, or employment decisions, require qualified human review and preserve the evidence trail.

## Evidence Rules
- Prefer primary sources over AI-generated summaries.
- Keep the raw AI output, prompts, model name, date, and reviewer identity.
- Separate verified claims from inferred, plausible, and unsupported claims.
- Do not cite AI output as a substitute for a real source.
- For volatile law, policy, science, or market claims, re-check the source close to publication.

## Official Source Anchors
- Quebec Act respecting the protection of personal information in the private sector, CQLR c P-39.1: https://www.legisquebec.gouv.qc.ca/en/document/cs/p-39.1/20240701
- NIST AI Risk Management Framework 1.0: https://www.nist.gov/itl/ai-risk-management-framework
- ISO/IEC 42001:2023 AI management systems: https://www.iso.org/standard/42001