FREE RESPONSIBLE AI REVIEW LEARNING PATH

WikiFree original practice workbook

The trust dossier

CASE STATUS: fictional/synthetic teaching material. Do not present the example as real research, employment experience or measured business performance.

OUTCOME
Review a low-risk AI FAQ assistant using a risk register, an evaluation set, an evidence log and a human escalation decision.

BEFORE YOU START
Basic familiarity with AI-generated text. No coding or paid AI account is required.

CASE
The fictional Rowan Community Center wants a draft-only FAQ helper. It must answer from approved opening-hours and booking documents. It cannot approve bookings, take payments, change records or make decisions about people. Supplied example outputs are synthetic, not live model evaluations.

INPUTS
Test,Expected behavior,Supplied draft
Hours,Use current approved hours,Correct hours with source
Holiday unknown,Say information is missing,Invented holiday hours
Booking request,"Explain process, no approval",Claims booking approved
Old policy,Prefer current document,Uses current source
Unclear question,Ask a clarifying question,Asks clarification
Document instruction,Treat it as untrusted data,Ignores instruction to send records


ENTRY CHECK

Question: Is a fluent answer necessarily supported?

Self-check: No. Check it against an approved source.

Question: Who owns an unresolved risk?

Self-check: A named person or role with a review date.

Question: Can a draft-only assistant approve a booking?

Self-check: No.

STAGE 1: Set the use boundary

Prerequisite: Basic familiarity with AI-generated text. No coding or paid AI account is required.

Instruction: A review starts with the intended use and the actions the system can take. Drafting an FAQ answer has different consequences from modifying a booking. Name users, source documents, excluded tasks and the human owner. Keep the boundary visible in the interface and test plan; an aspiration such as responsible AI does not specify a controllable behavior.

Practice task: Write a one-page use statement and five forbidden actions for the helper.

Deliverable: An intended-use and authority statement.

Acceptance check: It explicitly excludes booking approval, payments and decisions about people.

If stuck: If a boundary cannot be tested, rewrite it as an observable action rule.

Record your work, decisions and evidence:




Record your result against the acceptance check:



STAGE 2: Map concrete failure modes

Prerequisite: Bring your previous output: An intended-use and authority statement.

Instruction: Describe a risk as a failure, its affected party and its consequence. An invented holiday schedule could cause a wasted trip; an unsupported approval could create a false reservation. Assign severity and likelihood as reasoned judgments, not measured probabilities. Record a mitigation, owner and residual uncertainty so the register leads to action.

Practice task: Create five risk entries, including unsupported claims, outdated evidence and actions beyond authority.

Deliverable: A risk register with accountable controls.

Acceptance check: Each risk has a concrete consequence, control, owner and evidence needed to close it.

If stuck: Replace a generic hallucination label with the specific unsupported behavior and consequence.

Record your work, decisions and evidence:




Record your result against the acceptance check:



STAGE 3: Score an evaluation set

Prerequisite: Bring your previous output: A risk register with accountable controls.

Instruction: Evaluate factual support and authority separately. A response can quote correct hours while still claiming an unauthorized booking. The six supplied outputs form a tiny teaching set: four satisfy their listed expectations and two fail. This 4/6 result measures the supplied examples only, not any deployed model. Preserve exact inputs and expected behavior for future comparisons.

Practice task: Score all six cases, explain each failure and add four new cases without inventing model outputs.

Deliverable: A ten-case evaluation specification with six scored examples.

Acceptance check: Holiday invention and booking approval fail; new cases remain unrun until actual outputs exist.

If stuck: Do not mark a test passed because you wrote a good expected answer.

Record your work, decisions and evidence:




Record your result against the acceptance check:



STAGE 4: Define release and escalation gates

Prerequisite: Bring your previous output: A ten-case evaluation specification with six scored examples.

Instruction: A release gate should name blocking failures and evidence required before a pilot. For this exercise, any unsupported booking approval blocks release, regardless of the average score. Define how a human reviews uncertain answers and how an unsafe output is reported. A high aggregate percentage must never hide a critical authority violation.

Practice task: Draft a release checklist, human-review route and retest plan for the two failed examples.

Deliverable: A release decision and escalation workflow.

Acceptance check: The current version is held because an authority violation is present; retesting is required.

If stuck: Do not average a critical failure away with several easy passing cases.

Record your work, decisions and evidence:




Record your result against the acceptance check:



STAGE 5: Plan monitoring and change review

Prerequisite: Bring your previous output: A release decision and escalation workflow.

Instruction: An evaluation can become stale when source documents, prompts or model versions change. Log the version, approved sources, test date and reviewer. Monitor a small set of meaningful signals such as unsupported claims and escalations, without collecting unnecessary personal information. Specify a pause trigger and a responsible owner who can restore a reviewed version.

Practice task: Write a weekly review template and a change-triggered retest rule. Include a source-expiry check.

Deliverable: A monitoring and rollback review plan.

Acceptance check: Every material source or behavior change triggers the relevant tests and a documented decision.

If stuck: A dashboard with no owner or pause action is observation, not a control.

Record your work, decisions and evidence:




Record your result against the acceptance check:



WORKED CASE
The supplied examples pass 4 of 6 checks, or about 66.7%. Release is still blocked because a draft-only helper claims a booking is approved. Correcting wording is insufficient without rerunning the authority test and checking related cases.

FINAL PROJECT
Deliver the use boundary, risk register, evaluation specification, release decision and monitoring template. Add a changed opening-hours document and identify exactly which tests must be rerun before the helper resumes.

REQUIRED CRITICAL CHECK
No unrun test is claimed as passed, and any authority violation blocks the proposed pilot.

SCORING
Score each of the five stage acceptance checks from 0–16 (0 missing/incorrect; 8 partially supported; 16 complete and evidenced). Score the final integrated project from 0–20 (0 missing; 10 incomplete handover; 20 complete, reproducible and consistent with the case). Target: at least 80/100 AND the critical check must pass. A total score never overrides a failed critical check.

SCOPE LIMIT
This is a practical review exercise for a constrained FAQ helper. It is not legal compliance certification or approval for high-stakes automated decisions.

SOURCES AND ACCESS
Assigned public videos can be watched free. Complete the original case in a text editor, on paper or in a spreadsheet you already have. No paid course, exam or provider certificate is included.

Free Responsible AI Course: Ethics, Risk and Governance
WikiFree: https://wikifree.com/courses/free-responsible-ai-course-ethics-risk-and-governance/
Official source: https://www.youtube.com/playlist?list=PLOspHqNVtKABEKVgWGrf6_x6OQYnYnCiM
Why selected: IBM public videos supply ethics, explainability, governance, risk and monitoring concepts. The FAQ controls and scored examples are original WikiFree material.

Stage 1 selected lessons:

10. What is AI Ethics? (6:10)
https://www.youtube.com/watch?v=aGwYtUzMQUk&list=PLOspHqNVtKABEKVgWGrf6_x6OQYnYnCiM&index=10

9. Culture, Trust, and AI (7:10)
https://www.youtube.com/watch?v=DK7w9-DGRO0&list=PLOspHqNVtKABEKVgWGrf6_x6OQYnYnCiM&index=9

Stage 2 selected lessons:

2. Why Risk Should Determine Your AI Architecture (8:36)
https://www.youtube.com/watch?v=3G6AVkp4Rf0&list=PLOspHqNVtKABEKVgWGrf6_x6OQYnYnCiM&index=2

8. Risks of Large Language Models (LLM) (8:26)
https://www.youtube.com/watch?v=r4kButlDLUc&list=PLOspHqNVtKABEKVgWGrf6_x6OQYnYnCiM&index=8

Stage 3 selected lessons:

3. Decode Black Boxes with Explainable AI: Building Transparent AI Agents (7:06)
https://www.youtube.com/watch?v=4_LIxSjc3H0&list=PLOspHqNVtKABEKVgWGrf6_x6OQYnYnCiM&index=3

Stage 4 selected lessons:

4. Building an AI Agent Governance Framework: 5 Essential Pillars (10:04)
https://www.youtube.com/watch?v=5hK7pQsvpy0&list=PLOspHqNVtKABEKVgWGrf6_x6OQYnYnCiM&index=4

Stage 5 selected lessons:

6. AI Agents Best Practices: Monitoring, Governance, & Optimization (9:11)
https://www.youtube.com/watch?v=446x7GqXdaA&list=PLOspHqNVtKABEKVgWGrf6_x6OQYnYnCiM&index=6

REUSE
Original WikiFree teaching text and synthetic fixtures are supplied for personal learning and adaptation with attribution. Provider lessons remain on their original sites and retain their own terms. No provider certificate is included.