An AI prediction is not a decision. To use it responsibly, connect the output to a specific choice: define who decides and what they are trying to achieve, identify feasible actions, estimate what may happen under each action, and compare the consequences using an explicit rule. Then decide what role the AI and any human reviewer will have, and evaluate the system in the setting where it is used.
Start by writing down the decision
Frame the problem as a choice, not as a request for a prediction. State who has authority to decide, when a decision is needed, what options are realistically available, and what objective matters. For example, “Which applications should receive additional review this week?” is a decision question; “Which applications are likely to be problematic?” is a prediction question.
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A prediction is useful only insofar as it can inform the choice or its consequences. A highly accurate estimate may still be irrelevant if it does not change which action makes sense. The decision-analysis framework described in the National Research Council’s Completing the Forecast connects objectives and available actions to possible consequences and uncertainty.
List the actions and outcomes
Include all feasible choices, not only the action suggested by the model. Depending on the setting, options may include acting now, taking a different action, waiting, gathering more information, escalating to a person, or taking no action. Then describe the outcomes that matter and how they could differ under each action.
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In decision analysis, uncertainty about outcomes is considered conditional on the action taken. An AI score may estimate the likelihood of an event, but it does not automatically tell you what will happen if you intervene. A risk estimate for a person or case is not, by itself, an estimate of the effect of changing that person’s treatment or handling. Make clear what the model predicts and how that prediction relates to each option. The Stan User’s Guide chapter on decision analysis describes the basic elements as decisions, possible outcomes, outcome distributions conditional on decisions, and utilities assigned to consequences.
Make objectives and tradeoffs explicit
Identify the benefits, costs, and potential harms associated with each possible outcome. Include constraints that matter in practice, such as time, available staff, legal requirements, or the consequences of delaying a decision. If values cannot responsibly be reduced to one number, state the competing priorities and constraints directly rather than hiding them behind an overall score.
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Expected utility is one way to compare actions when the probabilities and the values assigned to outcomes can be specified defensibly. For each action, consider the possible consequences, their likelihood, and their value to the decision maker; then compare the resulting expected utility. The Stan User’s Guide outlines this approach, and the National Research Council’s forecast decision-analysis discussion likewise connects objectives, actions, consequences, and conditional probabilities. Expected utility is a method, not a universal answer: its usefulness depends on whether the assumptions and value judgments fit the decision.
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Compare actions under uncertainty
Evaluate options against the same decision-relevant considerations. These often include expected benefit, the severity of possible harm, uncertainty in the estimates, reversibility, timing, constraints, and the decision maker’s values. Do not compare a model’s score for one option with an unexamined intuition about another; compare the likely consequences of the actions themselves.
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When the ranking depends on an uncertain probability or a contested value judgment, test whether a reasonable change in that assumption would change the preferred action. If it would, the decision is sensitive to that assumption, and the uncertainty should be visible to the decision maker. This is a practical way to avoid treating a single estimate as more conclusive than it is.
Ask whether more information could change the choice
Sometimes the next useful action is to gather information rather than act immediately. Value-of-information analysis asks whether additional information could improve a decision, given the decision maker’s goals, available options, and possible outcome probabilities. The National Academies’ 2011 chapter on intelligence analysis describes this approach. The key question is not whether more data would be interesting, but whether it could plausibly change the choice enough to matter.
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Set the AI’s role and human responsibilities
Decide explicitly how the AI output will be used. NIST describes systems that make decisions autonomously, defer decisions to a human expert, or provide an additional opinion to a human decision maker. These are different arrangements, and NIST does not prescribe one as best for every use. Specify who can accept, reject, or override the output, who is accountable for the final decision, and what happens when the output is unavailable or disputed.
A human reviewer is not automatically a safeguard. NIST notes that data-driven representations can omit context and that human-AI interaction can amplify bias under some conditions. A review process therefore needs to fit the actual task: reviewers need relevant context, a clear scope of authority, and a way to question the output rather than simply defer to it. These cautions appear in Appendix C of the NIST AI Risk Management Framework.
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Evaluate the system in its use context
Assess more than whether a model predicts well on a technical measure. Evaluate whether its output supports the intended decision, how errors affect different actions and people, how often the system is used as intended, and whether human-AI interaction creates risks such as lost context or undue reliance. The relevant checks depend on the application and the consequences of error.
NIST’s voluntary AI Risk Management Framework (AI RMF) 1.0, released on January 26, 2023, organizes risk-management guidance around four functions: Govern, Map, Measure, and Manage. NIST presents them as connected functions, not a mandatory linear checklist; governance informs the others, and risk management continues across the AI system lifecycle. Its AI RMF Playbook offers suggested actions for the functions. NIST’s framework page said it was being revised as of October 2026, so consult that page for its current status.
A compact decision record
For a consequential AI-assisted choice, record the essentials in a form another decision maker can understand:
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- Decision: what must be chosen, by whom, and by when.
- Objective and constraints: what outcomes matter and what limits apply.
- Options: feasible actions, including delay, further information, escalation, or no action where appropriate.
- Evidence and uncertainty: what the AI output estimates, what it does not establish, and the uncertainty attached to expected outcomes under each action.
- Consequences and rule: the relevant benefits and harms, plus the criterion used to compare options.
- Roles: whether the system advises, defers, or acts, and who is responsible for the final decision.
- Evaluation: how the system’s performance and its effects on the decision process will be monitored in the actual use context.
This is a practical synthesis of decision-analysis concepts and NIST’s lifecycle and human-AI guidance, not a prescribed NIST checklist.
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