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Assign AI work one activity at a time, in its real operating context. AI is a better fit when a task is bounded, testable and reversible, and the consequences of a mistake are limited. Keep people accountable for consequential decisions—especially those affecting safety, rights, opportunities or other important interests—and give them genuine authority to intervene.
There is no universal score that tells you what to automate. The right level of autonomy depends on the task, the system’s demonstrated fitness for it, who may be affected and what happens when it fails.
How to decide which tasks AI should handle
Start with the outcome you want, then break the work into activities. A single workflow may include routine steps suitable for automation alongside judgments that need a person. For example, an AI system might organize information or flag an item for attention, while a qualified employee makes the final decision.
NIST’s AI Use Taxonomy: A Human-Centered Approach offers a vocabulary for describing how AI contributes to an outcome. Published in 2024, it identifies 16 AI use activities. It helps classify a use; it does not prescribe whether that use should be automated.
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- Define the outcome and activities. State what success means, which steps AI would perform, who relies on the output and who could be affected.
- Map the setting and stakes. Note the domain, users, affected people, data involved, likely failure modes and foreseeable misuse. Ask what happens if the output is wrong, late or used outside its intended context.
- Check fitness for this particular activity. Look for evidence of performance in the intended setting, known limitations, bias or opacity concerns, and whether users can interpret the output appropriately. A system’s general capability is not proof that it is reliable for this use.
- Choose an oversight arrangement. Decide whether AI assists, recommends, acts under supervision or operates autonomously within limits. Name who is responsible for decisions and intervention.
- Make intervention practical. Specify who may question, override, pause or escalate an action; what information they will receive; and what fallback is available.
- Monitor and revise. Track performance, incidents, feedback, overrides and the reasons for them. Reassess when the system, evidence or operating context changes.
These steps reflect the context-specific approach in the OECD Due Diligence Guidance for Responsible AI. The OECD describes potentially high-risk uses as dependent on context and jurisdiction, rather than a fixed category that applies identically everywhere.
What should determine the level of oversight?
Compare each candidate activity across the factors below. This is a practical checklist, not a validated scoring system: a severe possible harm should not be averaged away by lower concerns elsewhere.
- Impact of error: Who could be harmed, and how serious could the consequences be?
- Reversibility: Can someone correct the action before it causes lasting effects?
- Context and judgment: Does the decision depend on values, nuance or information the system may not represent?
- Evidence of performance: Has this system been evaluated on this activity, with these users and conditions?
- Contestability and control: Can affected people challenge an output, and does an empowered person have enough information and time to respond?
- Data and misuse: Does the task involve sensitive inputs or foreseeable use beyond the intended setting?
- Review burden: Can people review the work meaningfully at its volume and pace, or is the process likely to become rubber-stamping?
NIST cautions that turning complex human phenomena into measurable quantities can lose context, and that human-AI interaction can sometimes amplify bias. A measurable output is not automatically a complete or neutral account of the situation.
Choose an oversight arrangement
Oversight is a spectrum, not a binary choice between “AI” and “human.” Pick the least autonomous arrangement that still serves the task safely and effectively, and define responsibilities before deployment.
| Arrangement | What happens | Useful control to define |
|---|---|---|
| Fully manual | A person performs the task without AI. | Keep the existing process and its accountability clear. |
| Human-led, AI-assisted | A person performs the task and uses AI for bounded support. | Identify which parts AI supports and how the person checks them. |
| AI recommendation, human decision | AI analyzes or proposes an action; a responsible person decides. | Give the decision maker enough context to assess and reject the recommendation. |
| Human-supervised action | AI performs defined steps, with a person able to intervene or approve specified actions. | Set intervention triggers, authority and response time. |
| Autonomous with monitoring | AI acts within a constrained scope, with monitoring and escalation. | Provide a safe stop, escalation route and fallback. |
These are practical descriptions, not formal NIST tier names. NIST describes human-AI configurations ranging from fully autonomous to fully manual, including AI decisions, deferral to an expert and AI serving as an additional opinion. Its AI Risk Management Framework Appendix C says: “Human roles and responsibilities in decision making and overseeing AI systems need to be clearly defined and differentiated.”
When does AI need human oversight?
Increase human involvement when errors could have serious consequences, actions are difficult to reverse, the decision depends on context the system may miss, or people affected by the outcome need a meaningful way to challenge it. The right reviewer must be competent for the decision, receive relevant information, have time to assess it and hold authority to change the outcome.
A nominal approval step is not meaningful oversight if the reviewer is expected to accept outputs at speed, cannot see the basis for a recommendation or lacks permission to stop the process. Decide in advance which actions require approval, which conditions trigger escalation, and who can suspend use when performance or circumstances change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Should a person review every AI decision?
Not necessarily. Reviewing every output can be wasteful or create a bottleneck, and a high-volume review process can encourage rubber-stamping. Conversely, sampling alone may be inadequate when an individual error could cause serious, irreversible harm. Match review to the consequences and the task: use human decisions or approval for consequential choices, and consider monitoring, exception review and escalation for bounded, lower-impact actions.
Whatever the arrangement, record overrides and why they occurred, along with incidents and relevant feedback. These signals can expose failure patterns, unclear guidance or a mismatch between the system and the work. NIST notes that organizations may find it useful to study how often and why people overrule AI output.
How to keep the decision current
Task delegation is not a one-time sign-off. NIST’s AI Risk Management Framework Core organizes risk management around Govern, Map, Measure and Manage. Governance runs across the lifecycle; the framework calls for context mapping, documented roles and oversight procedures, training and monitoring. Revisit the arrangement when the system changes, performance shifts, new failure modes emerge or the task is used in a different context.
A 2019 study by Brian Lubars and Chenhao Tan surveyed preferences across 100 tasks and considered motivation, difficulty, risk and trust. The authors report little preference for full AI control and a strong preference for machine-in-the-loop designs. That result describes preferences in their study, not objective safety or a universal optimum.
Neither the NIST nor OECD guidance establishes a universal numeric threshold for deciding which tasks AI should handle or how much oversight each requires. Treat the decision as a documented, context-specific risk judgment, then monitor whether the chosen controls work in practice.
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