Let AI handle bounded, testable work when it performs adequately and mistakes are limited and reversible. Keep people responsible for consequential choices when errors could affect safety, rights, opportunities, or other important interests—especially if those errors are difficult to undo. Make the decision for each task in its real context, then choose an explicit level of human involvement and revisit it as conditions change.
Start with the task and its consequences
There is no reliable rule that says a particular kind of work should always be automated. The unit to assess is a task in context: what outcome is sought, what activities contribute to it, who relies on the result, and who might be affected.
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NIST’s 2024 AI Use Taxonomy: A Human-Centered Approach identifies 16 activities describing how AI can contribute to an outcome. A single goal may involve several activities, so it is useful to examine the parts separately rather than label an entire job “automatable.” The taxonomy is a way to describe AI use, not a decision rule for whether to automate.
Next, map the setting: the people and data involved, likely failure modes, foreseeable misuse, and what would happen if an output were wrong. OECD guidance recommends first understanding an organization’s AI uses, then conducting deeper due diligence where appropriate. What counts as high risk depends on the use and jurisdiction; it is not a universal category that can be assigned without context.
Evaluate whether AI fits this activity
Assess the system against the specific activity and setting in which it would be used—not against a broad claim about what AI can do. Consider whether its performance has been evaluated on relevant cases, what its known limitations are, and whether users can interpret its output appropriately.
- Impact: Who could be harmed by an error, and how serious could the harm be?
- Reversibility: Can a person correct the action before it has lasting consequences?
- Context and judgment: Does the decision depend on values, nuance, or information the system may not represent?
- Evidence: Has performance been assessed for this particular task and setting?
- Contestability and control: Can affected people challenge an output, and does an empowered person have the time and authority to act?
- Data and misuse: Are sensitive inputs involved, or could the system be used out of context?
- Review burden: Is meaningful human review practical at the volume and speed required, or are reviewers likely to rubber-stamp outputs?
These factors are a practical comparison framework, not a validated score. Do not let several apparently low-risk factors cancel out a single severe consequence. NIST cautions that turning complex human phenomena into measurable quantities can lose context, and that human-AI interaction can sometimes amplify bias.
Choose an oversight arrangement
Human involvement is a spectrum, not a binary choice. Pick a configuration that matches the task’s consequences and the system’s demonstrated fit. The following are practical descriptions, not formal NIST tier names.
| Arrangement | How it works | Best suited to |
|---|---|---|
| Fully manual | No AI is used for the task. | Work where AI is not suitable or where the organization has not established a safe, useful role for it. |
| Human-led, AI-assisted | A person performs the task and uses AI for bounded support. | Tasks where assistance may help but a person should retain the work and judgment. |
| AI recommendation, human decision | AI analyzes or proposes an action; a responsible person makes the consequential choice. | Tasks where AI can inform a decision but should not make it. |
| Human-supervised action | AI performs defined steps, while a person can intervene or approve specified actions. | Tasks that can be delegated in part if intervention and approval points are clear. |
| Autonomous with monitoring | AI acts within a constrained scope, with monitoring, escalation, and a safe stop or fallback. | Bounded tasks where the system is suitable and the organization can detect and respond to problems. |
NIST describes configurations ranging from fully autonomous to fully manual, including AI decisions, deferral to an expert, and AI as an additional opinion. It also says: “Human roles and responsibilities in decision making and overseeing AI systems need to be clearly defined and differentiated.”
Make human oversight meaningful
A human reviewer is not a safeguard merely because a person is nominally in the loop. Before deployment, specify who is responsible for each decision and whether that person can actually question, override, pause, or escalate the AI’s action. Give reviewers the information and training needed to assess outputs, along with enough time and authority to intervene. Define a fallback for cases where the system is uncertain, unavailable, or produces an unsuitable result.
Track when and why people overrule AI, as well as incidents and feedback from users and affected people. Override patterns can reveal problems with the system, the task definition, or the review process; they are evidence to investigate, not proof on their own that either the human or AI was right.
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Task delegation is not a one-time approval. NIST’s AI Risk Management Framework organizes risk work into Govern, Map, Measure, and Manage, with governance running across the AI lifecycle. In practice, keep monitoring performance and incidents, collect feedback, and reassess the arrangement when the system, evidence, users, or operating context changes.
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OECD’s due-diligence guidance likewise frames AI risk assessment as context-specific. Neither the reviewed NIST nor OECD guidance establishes a universal numeric threshold for deciding which tasks to assign to AI or how much oversight to require. A 2019 study by Brian Lubars and Chenhao Tan surveyed preferences across 100 tasks and found little preference for full AI control and a strong preference for machine-in-the-loop designs. That result describes the study’s participants’ preferences; it is not a measure of objective safety or a universal rule for deployment.
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