# The Knowledge Integrity Mini-Course

Think critically about what AI tells you.

## Product Promise
A five-module micro-course that trains knowledge workers to evaluate AI outputs with rigorous verification, provenance, bias, and documentation habits.

## Intended Buyer
Teams moving beyond basic AI literacy into evidence evaluation and accountable AI-assisted work.

## Included Deliverables
- 5 written modules
- Module workbooks
- Verification exercises
- Completion certificate template
- Resource library

## Practitioner Workflow
1. Verification: define the decision, evidence, owner, and acceptance threshold before use.
2. Provenance: define the decision, evidence, owner, and acceptance threshold before use.
3. Bias detection: define the decision, evidence, owner, and acceptance threshold before use.
4. Documentation: define the decision, evidence, owner, and acceptance threshold before use.
5. Capstone exercise: 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- Do not cite AI output as a substitute for a real source.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