Level 3 · Guide 15
Evaluate AI outputs and disclose AI use responsibly
Evaluate AI output for accuracy, evidence, omissions, rights, and audience fit with a task-specific rubric. Record what AI did and what a person reviewed, then disclose that role wherever policy, law, contract, or impact requires it.
Product features may change. Check the last verified date and official sources.
Core concepts to know first
Focus on the decisions these terms support in real work rather than memorizing them.
Core concepts
1. Evaluation is specific to the intended use
Evaluation asks whether a result is fit for this use, while responsibility asks who accepts the consequence. Disclosure should be truthful, proportionate, and linked to the actual AI contribution and human review. In this guide, the first observable move is to create a rubric for accuracy, evidence, omissions, rights, and audience fit.
2. Evidence and omissions need separate checks
Record AI contribution, source provenance, and human changes. Preserve the approved input and the evidence behind the result so a reviewer can distinguish what the source says from what Claude inferred. This directly controls the risk that a generic rubric can miss domain-specific harm.
3. Rights and audience fit belong in the rubric
Apply the relevant policy, contract, or legal disclosure requirement. Record the decision and the remaining uncertainty instead of hiding it in polished prose. The review must explicitly test whether disclosure can overstate review that did not occur.
4. Responsibility remains human and organizational
Treat the possibility that copied material can create rights or attribution issues as a required test case. The intended result is an evaluation and disclosure record for output quality and responsibility, not an unreviewed answer that merely looks complete.
5. Disclosure should reflect the actual contribution
For Evaluate AI outputs and disclose AI use responsibly, the final concept joins the earlier checks into an operating boundary: use approved inputs, expose evidence and uncertainty, and stop before a consequential action. Ownership and a reproducible review determine whether an evaluation and disclosure record for output quality and responsibility may move beyond training.
Synthetic work scenario
How this applies at work
This scenario was written for learning and is not a real customer case.
A fictional communications team reviews an AI-assisted public FAQ built from approved sources. It checks every factual claim and right to use the material, records the editor’s changes, and applies the organization’s disclosure rule.
The scenario is newly written for this guide and is neither a customer case nor a performance claim. Its specific deliverable is an evaluation and disclosure record for output quality and responsibility. A reviewer can reproduce the work from the synthetic inputs without access to customer, employee, health, contract, or confidential company data.
Try it yourself
Choose one small task and follow the steps. Confirm organizational policy and data boundaries before using sensitive materials.
Step 1. Create a rubric for accuracy, evidence, omissions, rights, and audience fit
Create a rubric for accuracy, evidence, omissions, rights, and audience fit. Use only approved synthetic material and record both the evidence and any remaining uncertainty.
Verify: Confirm that another reviewer can reproduce the input, result, evidence, and stop point.
Step 2. Record AI contribution, source provenance, and human changes
Record AI contribution, source provenance, and human changes. Use only approved synthetic material and record both the evidence and any remaining uncertainty.
Verify: Confirm that another reviewer can reproduce the input, result, evidence, and stop point.
Step 3. Apply the relevant policy, contract, or legal disclosure requirement
Apply the relevant policy, contract, or legal disclosure requirement. Use only approved synthetic material and record both the evidence and any remaining uncertainty.
Verify: Confirm that another reviewer can reproduce the input, result, evidence, and stop point.
Completion checklist
Check only what you verified yourself. Every item must be checked before saving completion.
Some items are still unchecked. Review the result again.
What could go wrong?
Plausible language does not guarantee accuracy. Compare the result with originals, calculations, permissions, and current information.
- A generic rubric can miss domain-specific harm
- Disclosure can overstate review that did not occur
- Copied material can create rights or attribution issues
Boundaries that require human review
Academy practice stops at draft, preview, or approval pending. Actions with real impact require separate owner approval outside Academy.
What AI can do
- Step 1. Create a rubric for accuracy, evidence, omissions, rights, and audience fit
- Step 2. Record AI contribution, source provenance, and human changes
- Step 3. Apply the relevant policy, contract, or legal disclosure requirement
What a person must approve
- Applicable law, contract, organizational security policy, and explicit approval boundaries take priority. Stop and ask the responsible owner when they conflict.
- Academy exercises never send, publish, purchase, delete, execute contracts, or change permissions. A responsible person performs any real action through a separate process.
This guide is educational and does not replace legal, security, or privacy judgment for your organization.
Questions about this guide
- When is this guide complete?
- It is complete when the stated outcome is ready and another reviewer can retrace the inputs, evidence, boundaries, and decision. The target outcome is “An evaluation and disclosure record for output quality and responsibility.”
- May I practice with real company data?
- No. Use synthetic material in the Academy. Real data requires a separate review of policy, legal basis, contracts, minimization, retention, deletion, and the approved environment.
- What if the product screen differs from this guide?
- Product behavior can change. Check the verification date and official sources, then retest the current account and plan with a small, low-risk example.
Official sources and further reading
Recheck the current product and policy status in these primary sources.
Authorship and review
Save progress
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