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Decide whether AI belongs in the task
Before asking AI to draft, summarize, analyze, code, or act, consider four questions: Is the task repeatable? What could happen if the result is wrong? How readily can someone detect and correct a mistake? Does faster completion matter without removing necessary oversight? Microsoft recommends using these factors to decide whether a task should be automated, supported by AI, or kept human-led. Microsoft’s guidance on choosing when to use Copilot or an agent puts the practical questions plainly: “Who will review or validate the output before it’s used or shared?” and “Can you easily verify the result before it’s used or shared?”
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| Approach | When it may fit | Trade-off to consider |
|---|---|---|
| Human-led | The task is hard to verify, errors could have serious consequences, or judgment and accountability are central. | People retain control of the work, but AI may contribute less to speed. |
| AI-supported | AI can prepare a draft, summary, analysis, or other starting point, while a person remains responsible for the work. | May save preparation time, but the reviewer still needs to check and complete the output. |
| Automated with review | The task follows a repeatable pattern, the consequences of errors are manageable, and results can be checked and corrected before use. | Can reduce routine effort, but depends on effective verification and clear approval points. |
If you cannot readily verify a result, do not treat automation as the default. Microsoft advises considering partial automation or keeping the work human-led, using AI for drafting or preparation instead.
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Review the output before accepting or sharing it. Compare important claims, figures, and instructions with reliable source material, the original record, or an appropriate external source. Check especially carefully where the particular system or task may be less reliable. Microsoft’s Responsible AI practices overview recommends highlighting potential inaccuracies and encouraging users to verify outputs; it notes that targeted checks, such as checking numbers against external sources, can be useful when measurements show lower accuracy for that content type.
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- For a summary, compare key facts and qualifications with the document being summarized.
- For analysis, check the underlying data and whether the conclusions follow from it.
- For code or instructions, confirm that they fit the intended environment and do not introduce unintended effects before relying on them.
- For figures, measurements, or calculations, recalculate or consult the original data where appropriate.
A citation, explanation, or confident tone is not proof. Treat any verification aid as something to inspect, too: Microsoft’s overreliance guidance frames verification as checking both correctness and completeness, and cautions that aids intended to support verification can themselves be unreliable.
Review meaning and completeness
Accuracy is not the only test. Ask whether the response answers the actual request, preserves important conditions, and avoids adding unsupported claims. A statement can be factually plausible yet still be unsuitable if it omits a constraint, changes the intended meaning, or leaves out a necessary step.
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- Does it address the right audience, purpose, and scope?
- Are important exceptions, dates, assumptions, or dependencies missing?
- Has the output introduced claims or instructions that the source material does not support?
- Would a reader or colleague interpret it differently from the original intent?
Keep approval and accountability with a person
The reviewer should make the final decision: accept, edit, reject, or escalate. They remain accountable for the work that is used or shared, even when AI produced much of its first draft. UNESCO’s Recommendation on the Ethics of Artificial Intelligence says: “Member States should ensure that AI systems do not displace ultimate human responsibility and accountability.” That principle is a useful foundation for workplace review, not a claim that one checklist or approval level fits every task.
Put stronger controls around AI actions
When AI can access data, execute tasks, or make decisions through connected tools, reviewing its prose alone is not enough. Consider safeguards suited to what the system can do and the consequences of an action. Microsoft’s Responsible AI in Azure Workloads guidance recommends considering auditable agent activity, role-based access control, and circuit breakers for agentic systems. These are architecture safeguards to evaluate for the workflow, not universal legal requirements.
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- Auditability: Keep an appropriate record of agent activity so actions can be examined.
- Access control: Limit permissions to the data and operations the workflow actually needs.
- Interruption: Consider a circuit breaker or other way to stop activity if it behaves unexpectedly.
- Approval points: Decide which actions require a person to inspect or approve them before they take effect.
Use guidance in its proper scope
Human oversight is also an active area of standards work. ISO lists ISO/IEC FDIS 42105 as a 2026 Edition 1 Final Draft International Standard in the approval phase; its abstract describes guidance for human control and monitoring across the AI system life cycle. It is a final draft, not a finalized published standard on that stated status.
UNESCO’s 2023 Guidance for generative AI in education and research addresses those fields specifically. It describes generative AI as producing new content in response to prompts rather than simply curating existing webpages; it should not be treated as a general workplace rulebook.
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