Before acting on an AI-generated prediction, separate its checkable claims about the present or past from its uncertain claim about the future. Verify each factual claim against suitable sources, define the forecast precisely, examine its assumptions and probability, and raise the evidence threshold when the decision could have serious consequences.
First separate facts from the prediction
A prediction often arrives wrapped in an explanation: names, dates, figures, quotations, descriptions of current conditions, and claims about cause and effect. Those supporting statements may be checkable now. The future event itself is not: until the outcome is observed, it remains uncertain.
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That distinction changes what “fact-checking” can establish. You can verify whether a cited report exists or whether a statistic is accurately described. You cannot prove in advance that the forecast will come true. Assess its evidence and assumptions now; evaluate its accuracy later against the observed outcome and comparable forecasts.
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Use this workflow before relying on the output
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Define exactly what the AI predicts
Write down the event, subject or population, geography, time window, and outcome that would count as success or failure. “Sales will rise soon” is too vague to evaluate. Specify which sales, where, how much or by what measure, and by when. Keep the prediction separate from the reasons offered for it; a persuasive explanation does not make an unclear forecast testable.
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Break the explanation into factual claims
List the names, dates, numbers, quotations, current conditions, and causal statements used to support the prediction. Treat each as unverified. The House of Commons Library recommends identifying claims and checking them independently, including dates, figures, and quotations (Working with AI and spotting AI-generated text).
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Inspect the sources, not just the citations
Open each AI-provided link. Confirm that the page exists, says what the AI claims it says, and supports the specific statement—not merely a related or weaker point. Prefer original documents, official statistics, regulators, peer-reviewed studies, or authoritative secondary sources as appropriate. A citation generated by AI is a lead to check, not proof that the claim is true. The House of Commons Library advises careful checking of generated material, ideally with an expert (House of Commons Library guidance).
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Check whether the evidence is current and relevant
Look at publication and update dates, then check whether the evidence concerns the same geography, population, task, and period as the forecast. A source can be genuine but stale, or accurate in one setting and a poor fit for another. UK government guidance notes that AI outputs are not guaranteed to be accurate and may reflect limitations in their data or use (Artificial Intelligence Playbook for the UK Government).
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Ask what would make the forecast testable
For a probabilistic forecast, ask for a probability or range, the event definition, the forecast horizon, the date of the evidence, key assumptions, and what new evidence would change the estimate. A confidence-sounding sentence without a defined event and time window cannot be meaningfully checked. Probability should be interpreted in light of what the system can do and how its performance has been evaluated; Google’s People + AI Guidebook discusses trust calibration and communicating uncertainty (Trust).
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Compare with a suitable baseline when one exists
Ask whether a relevant base rate or reference forecast exists—for example, how often a clearly defined event occurs in comparable cases. A forecast probability is more informative when compared with such a baseline than when presented alone. Keep comparisons like-for-like: same event definition, population, geography, evidence cutoff, and horizon.
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Search deliberately for what is missing
Look for caveats, dependencies, exceptions, contrary evidence, and affected groups left out of the explanation. Ask whether the answer blends sources, overstates certainty, or fills a gap with an assumption. Microsoft’s validation guidance prompts readers to consider omissions, inaccuracies, and context before moving forward (Validate Copilot output before you act on it).
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Set the evidence threshold to match the stakes
If acting could affect health, safety, money, legal rights, employment, or another important interest, do not rely on a fluent answer or an unchecked source list. Seek authoritative evidence and qualified human review; document who is accountable and what was checked. Canadian federal guidance cautions that generated content can be inaccurate, outdated, incomplete, or harmful in decision-making, and says not to use it when its quality cannot be confirmed (Responsible use of generative AI). UK government guidance likewise ties human involvement to the purpose and possible consequences of AI use (Artificial Intelligence Playbook for the UK Government).
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Record the forecast before the result is known
If you want to assess forecasts over time, preserve the exact event definition, probability, timestamp, horizon, and source before the outcome is known. Later, record what happened and compare the prediction with an appropriate reference. This prevents a forecast from being quietly redefined after the result is visible and makes evaluation across cases possible.
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How to assess a forecast after it resolves
For forecasts that assign probabilities to binary events, a Brier score can summarize probability error across a set of resolved predictions. It is not a verdict on one forecast: one hit can be luck, and one miss does not establish that a system is generally unreliable. Use comparable cases and an appropriate reference forecast.
An aggregate Brier score also does not isolate calibration by itself. It blends reliability, discrimination, and outcome uncertainty, so it should not be treated as a simple measure of whether probabilities are well calibrated. The ECMWF distinguishes forecast accuracy, skill relative to a reference, and usefulness for a decision; scikit-learn explains the components and limits of the Brier score (ECMWF forecast verification; scikit-learn: Brier score loss).
When comparing AI forecasts or tools
Compare forecasts only when they address the same event, population, geography, evidence cutoff, and horizon. Over a relevant set of outcomes, consider probability calibration, overall scoring, and skill against a suitable baseline. Also check whether sources, assumptions, limitations, and potential bias are transparent—and whether the forecast is useful for the decision you actually face. A higher score alone does not establish that a system is better for every purpose.
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- Accuracy: How close were the forecasts to the observed outcomes?
- Skill: Did they perform better than a relevant reference forecast?
- Utility: Would using the forecast help with the actual decision, given its consequences and costs?
A practical decision rule
Ask: “Can I trust this enough to move forward?” Microsoft uses that question in its guidance for validating Copilot output, but the answer depends on the claim, the evidence, and what is at stake. If you cannot define the prediction, verify its important supporting claims, or confirm the quality of evidence needed for the decision, pause rather than treating the AI’s confidence or fluency as confirmation.
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