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Why there is no universal cutoff
The question is not whether AI or conventional software is better in general. It is whether AI is appropriate for a particular purpose and context. NIST’s voluntary AI Risk Management Framework (AI RMF 1.0), released on January 26, 2023, treats trustworthiness as a concern across design, development, deployment, use, and evaluation—not as a property of a model considered in isolation. The framework is described on NIST’s resource pages as being updated, so check the current version before relying on it for a regulated or otherwise high-stakes use.
There is no established numeric threshold at which a task should switch from code to AI. A choice has to be made against the requirements and consequences of the specific application; a score that is acceptable for sorting low-impact messages may be unacceptable for approving a consequential action.
When conventional code is the better fit
Prefer ordinary, deterministic code when the requirement can be stated as explicit conditions and checked with repeatable tests. Examples include verifying that required fields are present, enforcing an access permission, checking whether a value falls within an allowed range, or applying a known business rule. These are practical engineering recommendations based on the contrast NIST draws between conventional software controls and AI’s data-dependent behavior, not a universal theorem that code is always more reliable.
Code is also the right place to enforce constraints that must hold regardless of what a model suggests. An AI-generated answer should not be able to grant itself permission, bypass a required field, or trigger an action outside an allowed range.
When AI may help
AI may be worth evaluating when inputs are ambiguous, unstructured, or too varied to enumerate comfortably as explicit rules—for example, interpreting a natural-language request or extracting meaning from an image. That is a reason to test an AI approach, not an automatic reason to deploy one.
AI behavior depends on data and statistical patterns. Training data may not represent the conditions in which a system is used; behavior can be difficult to predict; and data or concept drift can make performance deteriorate or change over time. If the system cannot meet a defined quality bar in representative cases, be monitored in its deployed context, or safely escalate uncertain cases, keep the responsibility in deterministic code or with a person.
A practical way to decide
- Define the job. Record the inputs, desired output, what counts as an error, the required consistency, and the consequences of a wrong result.
- Try explicit rules first. If the behavior can be written as clear conditions and tested against examples, implement those requirements in conventional code.
- Test AI only where interpretation is the hard part. Use representative examples of the real inputs, including unusual and incomplete cases. Set the quality bar before judging results; do not infer suitability from a few impressive demonstrations.
- Put guardrails around actions. Route model outputs through code that checks permissions, required fields, allowed ranges, and business constraints. Add confirmation or human review when the impact of an error merits it.
- Plan for failure and change. Decide who reviews, overrides, escalates, and corrects mistakes. Reassess when the model, data, users, operating environment, or intended use changes.
This is a practical decision method derived from risk-management principles, not an algorithm prescribed by NIST.
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Compare the whole system, not just the model
Set priorities and thresholds for the use case. NIST cautions that trustworthiness characteristics can trade off and do not apply equally in every setting. Its guidance states: “Human judgment should be employed when deciding on the specific metrics related to AI trustworthiness characteristics and the precise threshold values for those metrics.”
| Decision factor | Questions to ask |
|---|---|
| Correctness and reliability | Does the implementation meet requirements under expected operating conditions? What error rate do representative cases show? |
| Robustness | How does it handle unusual, incomplete, adversarial, or out-of-distribution inputs? |
| Impact and safety | Who or what could be affected by an error? How severe and reversible would the consequence be? |
| Testability | Can behavior be covered with clear, repeatable test cases? Which parts are difficult to evaluate? |
| Explainability and auditability | Can a reviewer understand, document, and reconstruct why the system acted? |
| Privacy and security | What sensitive input or output is collected, exposed, retained, or acted upon? |
| Maintenance | Could rules, data, models, or operating conditions change, and how would drift be noticed? |
| Human oversight | Who owns review, escalation, override, and correction when the system is uncertain or wrong? |
Keep people in the loop where the stakes require it
Match oversight to potential harm. NIST says risk management may need human intervention when an AI system cannot detect or correct its errors; serious safety risks call for especially urgent and thorough management. In practice, decide in advance which outputs can proceed automatically, which require confirmation, and which must be reviewed or escalated. The system should have a usable path to stop or correct an action rather than treating model output as an unquestionable decision.
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Revisit the boundary as conditions change
The original choice is not permanent. Data can become stale or stop matching the deployment context, and changes to the model, users, environment, or intended use can alter risk. Define what evidence will trigger review or corrective maintenance, and monitor whether the deployed system continues to perform as intended. NIST’s framework and playbook pages indicate that revision work is underway; consult the current NIST material and applicable sector-specific rules when making decisions for a regulated setting.
For general guidance, see the NIST AI Risk Management Framework, the NIST AI RMF Playbook, and NIST’s AI RMF resources.
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