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Choose the AI system that has the strongest evidence against the risks of your specific application—not the model with the highest general benchmark score or the broadest safety claim. Define the intended use and consequences first, set acceptance criteria before comparing candidates, test the complete system in representative conditions, and keep monitoring it after deployment. If no option meets your criteria, narrow the use case, add safeguards, or do not deploy.
Start with the decision and its consequences
The thing you are selecting is not just a model. It is the deployed system: the model and version, its data, prompts or other configuration, surrounding workflow, users, human oversight, and monitoring. A model that performs well in isolation may behave differently when connected to tools, shown different information, or used by people making consequential decisions.
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Write a short description of the intended purpose and the conditions in which the system will operate. Include:
- Who will use it, and who may be affected by its outputs.
- Which decisions or actions it can influence, and whether a person reviews outputs before they are acted on.
- What could happen if an output is wrong, late, unavailable, or misunderstood—and whether the harm can be reversed.
- Expected operating conditions, deployment geography, and foreseeable misuse.
- The fallback if the system fails, produces an uncertain answer, or is unavailable.
This context determines what “good enough” means. A low average error rate may still be unacceptable if a rare error could cause severe harm, or if performance is weaker for a population that the system serves.
NIST’s voluntary AI Risk Management Framework organizes risk work into Govern, Map, Measure, and Manage, and treats trustworthiness as a lifecycle concern rather than a one-time model check. Its AI RMF FAQs describe trustworthiness characteristics including validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy enhancement, and management of harmful bias. Which characteristics matter most—and how to measure them—depends on the application.
Set evidence requirements before comparing vendors
Separate hard constraints from preferences. A hard constraint is a condition a candidate must meet, such as a data-handling requirement or a maximum response time. Preferences can be weighed against one another after candidates pass those gates. Decide in advance what evidence will count and what result would be unacceptable; otherwise, a persuasive demo or an attractive benchmark can shift the goalposts.
| Evaluation area | Evidence to seek | Question for the decision |
|---|---|---|
| Task performance | Results on representative data and realistic operating conditions, with errors examined by type; consider confidence or calibration when relevant. | Does it perform the actual task reliably enough, including on consequential cases? |
| Reliability and robustness | Behavior across normal variation, edge cases, failures, and plausible changes in input or data distribution. | Does performance degrade in ways the workflow can detect and handle? |
| Safety and misuse | Tests of foreseeable misuse, harmful outputs, and failure modes in the intended context. | Can a dangerous or out-of-scope response reach an affected person or trigger an action? |
| Security and resilience | Evidence addressing relevant attacks, manipulation, and system dependencies. | Can the system be disrupted or induced to behave unsafely under plausible threats? |
| Privacy and data governance | Applicable data-handling controls and information about how data is used in the deployment. | Can the system be used within the application’s data requirements? |
| Transparency and review | Records, explanations, auditability, and practical ways for people to review or contest outputs. | Can an appropriate person understand and challenge an output when needed? |
| Uneven performance | Results for relevant populations or subgroups, where appropriate and lawful to evaluate. | Are there material differences that the use case cannot accept or mitigate? |
| Operations and oversight | Latency, availability, deployment control, costs, human-review needs, and change-control information. | Can the complete workflow meet its operational constraints and retain effective oversight? |
These are practical comparison dimensions, not a universal scoring formula. The sources do not prescribe a standard weighting scheme or pass threshold. Define application-specific thresholds, including any hard stops for high-severity failures. Do not treat a vendor’s general safety statement as evidence that the candidate meets those thresholds.
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Use a controlled evaluation that reflects the intended use, not just a public benchmark or a polished demonstration. NIST’s AI Resource Center provides resources for testing, evaluation, verification, and validation (TEVV). Domain experts should help interpret the results: a metric alone may not reveal whether an error is dangerous, detectable, or recoverable in the real workflow.
Build a representative test set
Include typical cases, difficult cases, relevant subgroups, edge conditions, foreseeable misuse, and situations in which the system or a dependency fails. Use a holdout or another appropriately controlled evaluation set so that the test is not simply a replay of examples used to configure the system. Record limitations in what the test can establish.
Evaluate the workflow around the model
Test how information reaches the model, what its output looks like to the user, what happens when the output is uncertain or wrong, and whether human review and fallback steps work as intended. Measure error severity as well as frequency; an aggregate score can hide a rare but unacceptable failure. Check that reviewers have enough context, time, and authority to intervene rather than merely approve outputs.
Keep the evaluation reproducible
For each run, record the model and version, configuration, date, input data, prompt or policy settings, and evaluation method. State which scenarios were and were not tested. A result supports claims only about the versions, conditions, and scenarios actually evaluated; it does not establish that the system is safe in untested settings.
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Where possible, run candidates through the same task-specific protocol and compare them on the risks and constraints established for the application. A useful comparison record includes:
- Demonstrated task performance and the types, severity, and frequency of observed errors.
- Robustness, security, privacy, and relevant data controls.
- Transparency, auditability, and support for meaningful human review.
- Latency, availability, deployment control, and other operational requirements.
- Monitoring and change-control arrangements that matter after selection.
Escalate an unacceptable high-severity failure even when average performance is strong. If one candidate is weaker on a preference but passes every hard constraint, document why the trade-off is acceptable. Do not convert scores from unrelated benchmarks into a claim of suitability: benchmark results do not automatically transfer to your data, configuration, users, or workflow.
Make a documented decision—or decline to deploy
Select the candidate that meets the hard constraints and has the strongest evidence against the application’s defined risks. The decision record should identify the selected configuration, evaluation evidence, rejected alternatives, known limitations, residual risks, responsible owners, and mitigations. Also specify what would trigger reevaluation, such as a material change in use, workflow, data, model version, or operating conditions.
If no candidate meets the acceptance criteria, the defensible options are to narrow the intended use, add and test safeguards, or not deploy. A human-in-the-loop label by itself is not a mitigation; the review step must be effective in the actual workflow.
Monitor risk after selection
Assign ownership for incident reporting, performance or drift monitoring, model and configuration changes, and periodic revalidation. Reassess when real-world conditions differ from the test conditions or when changes could alter system behavior. NIST’s AI RMF Playbook supports applying risk-management practices across the lifecycle; the framework page says AI RMF 1.0 is under revision, so check the current version before using it as an implementation reference.
Account for the applicable legal framework
Legal classification depends on the system, its intended purpose, and the jurisdiction. In the EU, the AI Act is binding, but not every AI system is classified as high-risk. The European Commission Service Desk’s classification guidance says to consider whether a system qualifies as an AI system, its intended purpose, relevant regulated-product or Annex III routes, filters, and transitional rules. The cited guidance described itself as draft and said feedback would run through 23 July 2026; its formal adoption status is not established here, so check the current page before relying on it.
For systems that fall within the AI Act’s high-risk provisions, Article 9 addresses a documented, maintained, continuous and iterative risk-management process over the lifecycle, covering intended use and reasonably foreseeable misuse. Article 15 addresses accuracy, robustness, and cybersecurity. For a compliance decision, consult the consolidated legal text and jurisdiction-specific legal counsel; this selection method is not a substitute for legal advice.
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