Human Oversight of AI at Work: A Practical Review Checklist

Human oversight means more than having someone click “approve.” A responsible person needs enough context to assess the work, authority to change or reject it, and a clear role in the final decision.

For businesses using AI to draft, summarize, research or analyze information, a practical review process starts with the task and the consequences of an error. It should be stronger when the work affects people or supports an important recommendation.

What makes oversight meaningful?

A review process should answer four questions:

• Who reviews? Name a person or role with the relevant knowledge.
• What do they check? Specify the important facts, sources, assumptions and decisions.
• Can they intervene? Let the reviewer correct, reject or disregard AI output.
• Who remains responsible? Keep ownership of the final work or decision with a person.

NIST’s AI Risk Management Framework discusses the need to define and differentiate human roles and responsibilities in human–AI interaction. It recognizes that oversight arrangements depend on the system and context, rather than prescribing the same process for every use. 1

Human review is not a guarantee that work is correct, unbiased or legally compliant. Its value depends on what the reviewer actually does.

Match the review to the work

The following are suggested workflows, not additional Ethicality certification requirements.

Type of work	Practical review focus
An internal draft or brainstorming note	Check that suggestions fit the task before adopting them
Material customer-facing content	Verify important claims, context and the final message before sending or publishing
An important recommendation	Check underlying evidence and assumptions, with appropriate human approval before acting
A consequential decision about a person	Keep a person in charge of the final decision, with authority to reject the AI contribution

A routine draft and a recommendation affecting someone’s employment do not carry the same consequences. Do not let a low-risk workflow quietly become the default for higher-impact work.

A checklist before material AI-assisted work goes out

1. Check the original task

Does the output answer the actual question? A polished response can still miss a critical instruction or use the wrong context. Compare it with the brief, not just with your expectations of fluent writing.

2. Verify important factual claims

Open the underlying sources. Check that they exist and support the statement being made. Confirm names, dates and quotations where they matter. Do not treat a citation supplied by an AI tool as proof without checking it.

3. Check numbers and assumptions

Recalculate material totals or comparisons using the underlying information. Look for assumptions the output never states. If the reviewer cannot validate a calculation or recommendation, ask for help rather than approving it on appearance alone.

4. Look for omissions and unfair framing

Ask whose perspective or relevant information may be missing. Where the work concerns people, check whether unsupported inferences or stereotypes influence the result. A human reviewer can also make mistakes, so important concerns need an escalation path.

5. Recheck sensitive information

Make sure the final content does not expose confidential, privileged or sensitive information to the wrong audience. Separately, confirm that the information entered into the AI tool was permitted for that use.

Canadian privacy regulators’ generative AI principles address privacy-protective development and use. They are a useful reference when personal information enters the workflow; review at the end does not undo an inappropriate disclosure at the beginning. 2

6. Confirm disclosure and approval

Consider whether the customer needs an explanation of AI’s role, whether appropriate human approval has occurred, and who owns the final work. See our AI disclosure statement examples.

Give reviewers a genuine way to say no

If a person cannot verify the output, the process should allow them to pause, change the approach, request specialist review or reject the result. Do not make approval a formality by withholding time, information or authority.

A short record of a significant approval can be useful where the business needs accountability or an explanation later. That is a suggested practice, not a requirement to create a formal incident log or AI register for EAUS certification.

How EAUS 1.0 addresses human responsibility

Requirements R10–R14 of the Ethicality AI Use Standard (EAUS 1.0) cover review of material AI-assisted work, appropriate approval of important recommendations, human control of consequential decisions, reviewers’ authority and a person’s responsibility for the final work.

NIST’s broader AI Risk Management Framework is voluntary guidance. Ethicality’s assessment is a separate process against its own published standard, not NIST certification, AI-system testing or legal-compliance certification. 3

Put your review expectations into an AI use policy, share them with the team and make sure the workflow matches the words. The free policy template and readiness check can help you start; when your applicable practices are in place, you can apply for Ethicality certification.