Use AI to assist with bounded, repeatable work when mistakes are easy to spot and correct. Keep meaningful human review and decision authority where an error could affect a person’s safety, rights, employment, health, access to services, or significant finances. Make the choice task by task: repetition alone does not make work safe to automate.
Which tasks are reasonable candidates for AI assistance?
AI can be useful for producing a draft or a starting point when an employee can verify the result before it is used. NIST’s Generative AI Profile, released July 26, 2024, identifies uses such as text generation and editing, summarization, search, chat, and code generation and review. Those are examples of use contexts, not approval to publish, send, or execute output without checking it.
- Often suitable for assistance with routine checks: first drafts, summaries, formatting, brainstorming, transcription cleanup, and code suggestions.
- Check before relying on the result: factual accuracy, omissions, fabricated details, confidentiality, and whether the output fits its intended audience and purpose.
- Keep the employee responsible for the work: AI can supply a draft or suggestion, but an employee should decide whether it is accurate and appropriate to use.
The practical dividing line is not whether a task is repetitive. It is whether the likely mistakes can be detected and corrected before they cause harm.
When should employees require stronger human review?
Increase review when an AI output will be sent outside the organization, used in production, presented as factual, or relied on to guide a consequential choice. Examples include external communications, factual claims, code merged into production, customer-facing recommendations, and analysis used to inform important decisions. This is a risk-based application of NIST guidance, not a NIST task-by-task classification.
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For decisions affecting employment, health, safety, legal rights, access to benefits or services, or significant financial outcomes, a qualified human should retain meaningful decision authority. NIST calls for oversight to be assessed and documented in critical, high-stakes, or high-risk settings, and for its effectiveness to be evaluated before deployment. Its AI RMF Playbook says: “Processes for human oversight are defined, assessed, and documented in accordance with organizational policies from GOVERN function.” See MAP 3.5.
Pause, redesign safeguards, or keep a task human-led when sensitive information is involved, errors are difficult to detect, affected people lack a meaningful way to challenge an outcome, or system performance is poor for the people and conditions involved. These are practical warning signs derived from NIST’s emphasis on privacy, validity, reliability, fairness, accountability, and transparency—not a universal checklist or legal threshold.
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How to choose between manual work, reviewed AI, and more automation
Assess the actual task and operating context rather than assigning an automation level to an entire job. NIST describes arrangements ranging from systems that need no human oversight to those that specifically require it; the appropriate level depends on context. Compare the options using these questions:
- Impact: What harm could a wrong, incomplete, or biased result cause?
- Reversibility: Can the mistake be corrected before it affects someone—or promptly afterward?
- Detectability: Can a reviewer reliably identify errors, omissions, fabricated content, or bias?
- Performance evidence: Has the system been tested on representative cases under real operating conditions, and will performance be monitored?
- Privacy and security: What information goes into the system, where is it processed, and how are access and retention controlled?
- Accountability and recourse: Who owns the decision, who can override the output, and can an affected person seek an explanation or correction?
- Reviewer readiness: Does the person checking the output have the expertise, time, information, and authority to challenge it?
These considerations interact; they are not a numeric scorecard. A highly reversible, easy-to-check draft may need only routine review. An error that is difficult to spot or could seriously affect someone warrants more safeguards and stronger oversight, even if the task itself is repetitive.
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How to make human review effective
A person in the loop is not automatically a reliable safeguard. NIST notes that human-AI outcomes vary, and that AI can amplify human biases in some perceptual judgment settings. Reviewers need clear responsibilities and the practical ability to question or stop use of an output.
- Map the use: Define the task, intended users, affected people, and operating context. NIST’s AI RMF Playbook addresses this in its MAP function.
- Assign responsibility: Identify the operator, reviewer, accountable decision-maker, monitor, and escalation owner. Document who can override a result or stop the workflow.
- Set review depth by risk: Define what must be checked and what happens when it fails. For high-stakes use, test both the system and the proposed oversight in realistic conditions before deployment.
- Prepare reviewers: Train them on intended use, known limitations, how to interpret outputs, and relevant bias risks. Provide enough time and information to conduct a real review rather than a rubber stamp.
- Monitor and document: Keep appropriate records of outputs and decisions; watch for failures or changed performance; and revisit controls when the system, data, or workflow changes. NIST’s Generative AI Profile says use of generative AI “may also warrant additional human review, tracking and documentation, and greater management oversight.”
- Provide a route to raise concerns: Let employees report failures and challenge unsafe or inappropriate use. NIST identifies incident response and whistleblower protections among governance considerations.
NIST’s trustworthiness guidance emphasizes contextual validation, ongoing testing or monitoring, and possible human intervention when a system cannot detect or correct errors. Oversight should therefore be part of a working process—not just a person’s name on an approval field.
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What this guidance does—and does not—settle
The recommendations here synthesize U.S. National Institute of Standards and Technology (NIST) guidance; they are not a universal legal rule or a declaration that any task is automatically safe. NIST describes the AI Risk Management Framework as voluntary, and its AI RMF 1.0 is under revision. Employers still need to check the laws and mandatory sector requirements that apply in their jurisdiction and industry. NIST’s AI Risk Management Framework page provides its current framework information.
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