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This is a practical risk-based approach, not a universal legal rule. The guidance below draws on voluntary U.S. National Institute of Standards and Technology (NIST) frameworks; applicable legal and sector requirements depend on where and how an employer operates.
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Which tasks are reasonable candidates for AI assistance?
AI is often most useful when it helps produce or organize work that an employee can check against a clear purpose or source. Examples include creating a first draft, summarizing material, formatting text, brainstorming, cleaning up a transcription, and suggesting code. These are examples for triage, not a declaration that any output is safe to publish or use without checking.
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- Drafting and editing: Use AI to get a starting point, then check facts, tone, audience, and confidential information before sharing.
- Summarizing: Compare important details with the underlying material, especially names, figures, qualifications, and exceptions.
- Formatting and transcription cleanup: Check that the transformation has not changed meaning or omitted relevant content.
- Code suggestions: Treat generated code as a proposal. A qualified employee should inspect, test, and approve it before it is merged or run in a consequential environment.
The right arrangement can range from manual work to AI assistance with review to more autonomous operation. NIST notes that some AI systems may not require human oversight, while others may specifically require it; context determines the appropriate arrangement. Its AI Risk Management Framework 1.0, Appendix C gives video compression as an example of a use that may not require oversight. That example is not a general exemption for routine office work.
When should an employee review AI output more closely?
Increase review when an error could travel outside the team, be hard to reverse, or shape an important decision. This is a practical application of NIST’s context- and risk-based guidance, not a NIST task-by-task classification.
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| Work situation | Practical review approach |
|---|---|
| Internal brainstorming, formatting, or a low-impact draft | Check that the result fits the task and contains no obvious errors or inappropriate data before relying on it. |
| External communications or factual claims | Verify claims against reliable source material and have an appropriate person approve the communication before it goes out. |
| Code intended for production | Require qualified review and testing before merge or execution; do not treat plausible-looking code as verified. |
| Customer-facing recommendations or analysis that informs an important decision | Check the evidence, limitations, and fit for the specific case; preserve a responsible human’s authority over the decision. |
| Decisions affecting employment, benefits or services, safety, health, legal rights, or significant financial outcomes | Keep a qualified human accountable, with the ability and authority to question, override, or stop the process. |
The last category is a heightened-review recommendation inferred from NIST’s treatment of high-stakes and high-risk settings, not an exact list published by NIST. NIST’s AI RMF Playbook, MAP 3.5 says processes for human oversight should be defined, assessed, and documented, and calls for evaluating oversight effectiveness before deployment in critical, high-stakes, or high-risk settings.
How to decide whether a task needs a person in the loop
Compare manual work, AI assistance with review, and more autonomous use across the factors below. They are not a universal numeric scorecard: NIST cautions that trustworthy-AI characteristics interact and that trade-offs depend on context.
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- Impact: What could happen if the output is wrong, incomplete, or biased?
- Reversibility: Can someone correct the result before it affects a person, or undo its effects afterward?
- Detectability: Can the employee reliably identify errors, omissions, fabricated material, or bias?
- Performance evidence: Has the system been evaluated on representative examples in realistic operating conditions? Is performance monitored over time?
- Privacy and security: What information will be entered, where will it be processed, and how are access and retention controlled?
- Accountability and recourse: Who owns the final decision? Can a human override it, and can an affected person seek an explanation or correction?
- Reviewer readiness: Does the reviewer have the expertise, time, information, and authority to challenge the output rather than rubber-stamp it?
Pause, redesign controls, or keep the work human-led if sensitive data cannot be handled appropriately, mistakes are difficult to detect, affected people have no meaningful route to challenge an outcome, or performance is poor under the conditions and for the population involved. Repetition alone is not a reason to automate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to set up effective human review
- Define the use: Identify the task, intended users, people affected, and operating context before choosing an AI system or level of automation.
- Assign decision roles: Name the operator, reviewer, accountable decision-maker, monitor, and escalation owner. Specify who can override or stop the workflow.
- Test in proportion to the risk: Match review depth to plausible harm. For high-stakes use, evaluate both the system and the proposed oversight in realistic conditions before deployment.
- Prepare reviewers: Train them on intended use, known limitations, output interpretation, and bias risks. Make sure they have enough time and information to do the review.
- Keep appropriate records and monitor: Track outputs and decisions where appropriate; watch for failures or changed performance, and revisit controls when the system, data, or workflow changes.
- Provide a reporting route: Give employees a way to report failures and challenge unsafe or inappropriate uses.
A human reviewer is not automatically an effective safeguard. NIST notes that outcomes in human-AI interaction can vary and that AI can amplify human biases in some perceptual judgment settings. Clearly defined roles, reviewer proficiency, escalation paths, and meaningful authority to intervene matter as much as having a person present. NIST’s AI RMF Appendix C discusses human-AI configurations and these interaction risks.
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For broader reliability, safety, security, accountability, transparency, explainability, privacy, and fairness considerations, consult NIST’s AI Risk Management Framework resource. NIST says the framework is voluntary and that AI RMF 1.0 is being revised; employers should check the current framework and the laws and sector rules that apply to their own workplace.
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