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An AI prediction is not an action plan. To turn an output into a defensible decision, connect it to a specific choice: define the goal and decision owner, list feasible actions, estimate what could happen under each action, and make the relevant tradeoffs explicit. Then choose a decision rule that fits the stakes and uncertainty, and decide how people and the AI system will be evaluated in the actual setting.
Why a prediction does not decide for you
A prediction estimates or represents uncertainty about something; a decision selects among available actions. A risk score, forecast, or classification becomes useful only when you know how the possible outcomes relate to the actions you can take and the consequences you care about. A highly accurate prediction may still be irrelevant if it would not change the choice or its expected consequences.
For example, a system might estimate that an event is likely. That estimate alone does not establish whether to intervene, wait, gather more information, or do nothing. Those choices depend on what each action could cause, the costs and harms involved, and the decision maker’s goals.
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How to turn an AI output into a decision
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State the decision, owner, and deadline
Write the question as a choice rather than a request to predict. Specify who has authority to decide, when the decision is needed, and what objective matters. If the AI output cannot affect the choice or its consequences, it may not be useful for this decision.
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List the feasible actions
Include the options that are genuinely available, not just the action implied by the model’s output. Depending on the setting, choices may include acting, not acting, waiting, or gathering additional information. Note constraints such as time, resources, policy, or authority that make some actions infeasible.
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Describe outcomes under each action
Identify the outcomes that matter and how they could differ depending on the action taken. Be precise about what the model predicts: for example, a score may estimate an outcome’s likelihood, but it does not automatically estimate what would happen if you intervened. In decision analysis, outcome uncertainty is considered conditional on the action. The Stan User’s Guide outlines this structure—decisions, possible outcomes, outcome distributions, and utility—in its Bayesian decision analysis chapter; the National Research Council also describes the elements in Completing the Forecast.
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Make consequences and tradeoffs explicit
For each outcome, identify relevant benefits, costs, and harms. If consequences cannot responsibly be reduced to one numerical scale, state the tradeoffs and constraints instead of concealing them inside a composite score. The goal is to clarify what matters to the decision maker, not to make every value look precise.
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Compare actions with a stated criterion
Consider the possible outcomes and their probabilities or other relevant uncertainty for each action. Expected utility is one formal comparison method: choose the action with the highest expected utility when the probability and utility assumptions are defensible and the method suits the problem. It is not a universal rule for every personal or organizational decision.
Check whether the preferred action changes when important assumptions change. If small changes in probabilities, costs, or values reverse the ranking, the choice is sensitive to those assumptions and should be treated accordingly. The National Academies’ chapter on value-of-information analysis explains how to ask whether additional information could improve a choice, given the decision maker’s goals, available actions, and outcome probabilities.
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Assign AI and human roles
Decide whether the system will act autonomously, defer a decision to a human expert, or offer an additional opinion to a human decision maker. Define who reviews the output, who can override it, who is accountable for the final choice, and what happens when the output is missing, unclear, or disputed. NIST describes these different configurations and emphasizes defining human roles and responsibilities in its AI Risk Management Framework.
Human review is not automatically a safeguard. NIST warns that data-driven representations can strip away context and that human-AI interaction can amplify bias in some conditions. Review procedures should therefore reflect the system, decision, and deployment context rather than assume that adding a person guarantees a better outcome.
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Choose a human-AI setup that fits the decision
There is no universal ranking of autonomous, advisory, or human-led configurations. Compare the arrangements in terms of autonomy, human authority and responsibility, how the output is presented, whether the system can be evaluated in the setting, and the risks of losing context or amplifying bias. A consequential or hard-to-reverse decision may require different authority and review arrangements from a low-impact, reversible choice; the appropriate setup depends on the use context.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate the decision process over time
Evaluate the system in the context where it is used, not just as an isolated prediction task. The relevant question is whether the combined process—AI output, human judgment, available actions, and consequences—supports the intended objective and manages foreseeable risks. Track whether the output is used as intended, whether outcomes or error patterns change, and whether the process still fits the decision and its constraints.
NIST’s voluntary AI Risk Management Framework organizes risk work under four functions: Govern, Map, Measure, and Manage. NIST presents governance as informing the other functions and risk management as continuing across the AI system lifecycle; the labels are not a mandatory linear sequence. Its Playbook offers suggested actions for those functions. The framework was released in 2023, and NIST’s framework page says version 1.0 is being revised as of October 2026, so consult the current materials when applying it: NIST AI Risk Management Framework and NIST AI RMF Playbook.
A compact decision record
Before using an AI output to support a real choice, record the essentials in a form appropriate to the stakes:
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- Decision: What choice is being made, by whom, and by when?
- Objective and constraints: What outcome matters, and what limits the available choices?
- Actions: Which options are feasible, including waiting or seeking more information where relevant?
- Outcomes and uncertainty: What can happen under each action, and what does the AI output actually estimate?
- Consequences: Which benefits, costs, harms, or non-numeric tradeoffs matter?
- Decision rule: How will the options be compared, and which assumptions could change the ranking?
- Roles and evaluation: Who reviews or overrides the output, who owns the decision, and how will the process be assessed in use?
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