Define the intended use and accountable owners
Describe the user task, the model’s role and what happens to its output. Drafting a reply for review differs from sending it automatically. Identify affected users, operating conditions and uses outside the approved scope. Consider whether a simpler rule or existing process meets the need before adding AI.
Name owners for product decisions, data access, evaluation, operational incidents and release approval. Record who can stop the system or change its scope. Responsibility cannot be delegated to a dashboard or assigned only to the model vendor. Include people who understand the workflow and the consequences of errors.
Turn risks into evidence requirements
For each plausible failure, document who could be affected, the control intended to reduce it, the test that evaluates the control and the release decision it informs. Prioritize according to the actual use and consequence rather than copying a generic checklist.
In a hypothetical support assistant, an obsolete policy could produce an incorrect refund instruction. The team might require source-version tracking, rejection of unsupported answers and reviewer approval. Test with superseded documents and missing evidence, then record whether the control works. This is an illustrative workflow, not a client case study or proof that every support assistant is suitable for release.
The NIST AI Risk Management Framework provides voluntary guidance for incorporating risk and trustworthiness into AI design, use and evaluation. Use it to organize project evidence; citing a framework does not certify a deployment or establish legal compliance.
Evaluate representative conditions and uneven errors
Choose metrics tied to the task and the people using it. Define the reference labels, sample selection and evaluation conditions. Keep development examples separate from final evaluation. Include missing inputs, difficult cases and realistic integration failures rather than reporting only successful demonstrations.
Report relevant segment results with sample sizes, uncertainty and known gaps. An overall score can hide a group receiving less reliable service. Select fairness questions and metrics with the responsible domain specialists; different metrics answer different questions. Removing demographic fields alone does not establish fair behavior because other inputs or historical labels may preserve disparities.
Keep model quality, downstream outcomes and operating controls distinct. A correct prediction does not prove an accessible workflow, and a plausible explanation does not prove a decision is justified. Record unresolved concerns and the evidence needed before expanding use.
Test oversight, explanations and user recourse
Make the reviewer’s role concrete: what evidence is visible, how much time review takes, what authority the reviewer has and how an output can be corrected. Test the handoff with ambiguous cases and peak workload. Adding an approval button does not establish that reviewers can detect or prevent mistakes.
Validate explanations against the system behavior and the source evidence they claim to describe. Test whether intended users understand the explanation and its limits. Provide an appropriate route to ask for help, correct source information or escalate a disputed outcome, with an owner and response process.
Explain the AI component where it helps users understand the workflow and make meaningful choices. Determine applicable notices, permissions and professional obligations for the actual jurisdiction and use with the responsible specialists. Avoid presenting a short generic regulatory list as an assessment of a product.
Verify data and operational controls
Document provenance, approved use, access and retention for inputs, outputs and diagnostic samples. Collect the data needed for the task and restrict unnecessary copies. Test retrieval permissions, exports, logs and deletion behavior across integrated systems. A private deployment or encryption setting alone does not prove the full workflow is controlled.
Version the model, prompts, preprocessing, data references and policy configuration. Test malformed inputs, expired access, unavailable sources, repeated requests and incomplete outputs. Specify fallback behavior and check that the system reports failure rather than silently acting on incomplete evidence.
Approve a bounded release and learn from incidents
Keep a release record with intended use, configuration, evaluation results, remaining limitations, control evidence and the named approval decision. Set a bounded initial scope and monitoring criteria. Define the conditions that require stopping, narrowing or rolling back the release.
Monitor task errors, overrides, complaints, accessibility issues and failed handoffs alongside latency and cost. Investigate incidents with the relevant source and configuration versions. Reassess affected controls after model, data or workflow changes. An unchanged model can still behave differently when its operating environment changes.
Use the MLOps release guide for operational detail, explore AI project scoping or discuss a bounded product evaluation.