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Governments should treat an AI purchase as a public-service decision, not a model-shopping exercise. Before deployment, define the problem, check whether AI is needed, assess likely impacts and legal obligations, test the system in realistic conditions, require evidence from suppliers, and document who accepts any remaining risk. Deployment should be conditional on that evidence and followed by continuous monitoring.
A benchmark score cannot show that a system is suitable for a particular agency, population or decision. A useful evaluation examines technical performance, context, affected people, operational controls and the agency’s ability to respond when the system fails. The process below is jurisdiction-neutral; statutory duties, approval thresholds and procurement rules must still be checked locally.
1. Define the public task before discussing a model
Start with the service outcome, not a proposed algorithm. Write down the administrative or operational problem, the people affected, the decision the system would support, and what a successful public outcome looks like. “Automate benefits decisions” is too broad; “help caseworkers identify incomplete applications for a named benefit, while a person makes the final decision” is testable.
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OECD guidance published in 2025 says governments should consider in advance whether AI is the best solution. Compare an AI system with process redesign, conventional software, better search or rules, additional staff, and a human-led service. An apparently lower-cost model may create review, appeal, accessibility or remediation work that makes it unsuitable.
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- Identify the public problem and the outcome residents need.
- List non-AI alternatives and explain why they were rejected or retained.
- Define what the system will not do, including decisions that remain exclusively human.
- Check whether the expected benefit is large enough to justify new privacy, security, equality and accountability risks.
2. Describe the proposed system and its boundaries
Create a plain-language system record before testing. It should identify the supplier, agency owner, operators, model or service version, connected systems, data sources, intended users, affected groups and every decision or recommendation the system can produce. Record whether outputs are generated, ranked, classified, predicted or used to trigger an action.
Specify operating limits
Document permitted inputs, prohibited inputs, retention periods, access controls, human review points, escalation routes and fallback procedures. State whether supplier staff can access prompts, records or logs, and how updates are approved. A system that changes model versions, retrieval sources or safety filters without notice needs a change-control requirement and a retest trigger.
NIST’s AI Risk Management Framework (AI RMF) treats risk management as spanning design, development, deployment, use and evaluation. Its core approach calls for documenting risks and potential impacts throughout that lifecycle, rather than producing a one-time approval memo.
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3. Use the right assessment for each question
“Assessment” is not one test. The OECD/UNESCO G7 toolkit distinguishes assurance mechanisms that answer different questions. Combining them prevents an agency from treating a successful accuracy test as proof of legal compliance or social acceptability.
| Assessment | Question it answers | Typical evidence |
|---|---|---|
| Risk assessment | What can go wrong, how severe could it be, and how likely is it? | Hazard register, likelihood and severity ratings, mitigations, residual-risk owner |
| Impact assessment | How could the use affect rights, equality, privacy, socioeconomic outcomes and public interests? | Affected-group analysis, rights analysis, consultation record, alternatives considered |
| Bias audit | Do performance or outcomes differ unfairly across relevant groups? | Disaggregated results, sampling plan, proxy and intersectional analysis, remediation tests |
| Compliance audit | Does the arrangement meet applicable law, policy and contractual duties? | Control mapping, records of processing, accessibility checks, procurement and security review |
| Conformity assessment | Does the system meet a specified standard or set of requirements? | Independent review, test report, declaration or certification where a recognized scheme applies |
| Formal verification | Can a defined property be shown to hold mathematically or exhaustively within stated assumptions? | Proofs, model checks or exhaustive analysis for a narrowly defined property |
Not every project needs every mechanism, and no item substitutes for the others. A bias audit may reveal disparate error rates while a privacy review finds unlawful data use; both findings matter.
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4. Write the evaluation plan before running tests
Turn the intended use into answerable evaluation questions. For each question, specify the evidence required, the pass or escalation rule, the reviewer and the decision it informs. Record the exact system version, configuration, prompts or workflows, data dates, test scenarios, user groups and environmental assumptions.
Set context-specific measures
Use task measures that reflect the public service: false-negative rates for missed safeguarding cases, calibration for risk scores, factuality and citation completeness for drafting, time saved without increased rework, or successful resolution for a service chatbot. Include error costs, not just averages. A 95% accuracy rate can be unacceptable if the 5% errors deny essential services to a small group.
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5. Test at three levels: model, attack and field context
NIST’s AI Research and Evaluation (ARIA) program describes three complementary levels: model testing, red-teaming and field testing. The structure is useful for government pilots, but it is an evaluation approach, not a universal certification or mandatory approval process.
Model testing
Measure task performance on representative, quality-checked data. Keep a locked test set separate from development data, document sampling and labeling, and report confidence intervals or uncertainty where appropriate. Test missing, ambiguous, out-of-distribution and low-quality inputs. Repeat tests after material model, prompt, data or integration changes.
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Red-teaming and adversarial testing
Ask skilled testers to expose unsafe, discriminatory, insecure or misleading behavior. Probe prompt injection, data exfiltration, inappropriate disclosure, fabricated sources, instruction conflicts, toxic content, and attempts to manipulate a decision. For predictive systems, stress input boundaries, rare cases and deliberate perturbations. Log the attack, system response, severity, fix and retest result.
Field testing
Run the complete workflow with representative staff, devices, languages, accessibility needs, data latency and upstream systems. Observe how people interpret outputs, whether they over-trust them, and whether workload, delays or error correction increase. A controlled pilot with a safe fallback is preferable to silently testing on live residents. Involve domain experts and, where decisions affect rights or access to services, suitable affected stakeholders.
6. Evaluate people, rights and operational harms
Technical robustness is only part of suitability. An impact assessment should examine who bears errors, who can challenge a result and whether the service remains accessible.
- Equality and bias: Compare error types and outcomes for legally or operationally relevant groups, including intersections where sample size permits. Investigate proxy variables and data gaps; do not treat equal average accuracy as proof of fairness.
- Privacy and data protection: Establish a lawful purpose, data minimization, retention, access, disclosure and deletion process. Test whether prompts, logs or outputs reveal personal or confidential information.
- Security and misuse: Assess authentication, authorization, supply-chain dependencies, model or data poisoning, prompt injection, extraction and denial-of-service risks.
- Human oversight: Give reviewers enough information, time and authority to reject an output. A nominal human-in-the-loop is ineffective if staff cannot inspect evidence or routinely rubber-stamp recommendations.
- Accessibility and language: Test assistive technology, reading levels, translation quality and communication channels used by the actual population.
- Due process and remedy: Provide notice where required, a way to obtain an explanation appropriate to the decision, correction and appeal routes, and a record of who made the final determination.
- Operational and reputational effects: Estimate downtime, vendor failure, staff training, public confusion, unequal availability and the consequences of an incorrect public statement.
7. Make procurement part of the evaluation
Procurement documents should require evidence and cooperation rather than promise that a product is “ethical” or “accurate.” Specify the intended use, prohibited uses, performance and robustness tests, documentation, audit access, incident reporting and change-notification duties.
Evidence to require from suppliers
- Model, data and system documentation, known limitations and intended operating conditions.
- Evaluation results with test data descriptions, subgroup findings and unresolved failures.
- Security architecture, privacy terms, retention settings and subcontractor information.
- Access to logs, relevant interfaces and test environments needed for independent evaluation.
- Notice and approval procedures for model, data, hosting or safety-policy changes.
- Service levels, support contacts, incident timelines, cooperation with investigations and secure exit or data-return arrangements.
UK procurement guidance: UK government guidance recommends agile procurement that can include a proof-of-concept before a solution goes live, an implementation plan and methods for ongoing evaluation. These are UK recommendations, not universal procurement law. Agencies elsewhere should map equivalent requirements to their own rules.
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Use staged procurement when uncertainty is high: discovery, a limited proof of concept, a controlled pilot and only then a production award or expansion. Pay for evaluation work explicitly and make a failed pilot an acceptable outcome.
8. Compare candidate systems on the same assumptions
When alternatives exist, hold the task, data conditions, human workflow and reporting format constant. The following axes are an editorial decision framework, not an official scoring formula.
| Axis | Comparison questions |
|---|---|
| Problem fit | Does this approach solve the defined public problem, and is AI necessary? |
| Contextual performance | How reliable is it on representative cases, edge cases and relevant groups? |
| Impact and risk | What harms, rights effects, bias patterns, privacy issues and security exposures remain? |
| Testability | Can the agency access documentation, logs, test interfaces and supplier cooperation before go-live? |
| Governance readiness | Are accountability, feedback, incident handling, appeals, fallback and monitoring workable? |
| Change resilience | Can the agency detect and retest material updates, data drift or altered operating conditions? |
Keep raw results and assumptions alongside any weighted assessment. A high composite score should not hide a non-negotiable failure, such as unlawful data use or the absence of a remedy for affected residents.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.9. Make a documented go/no-go decision
The accountable agency, not the supplier, should make and sign the deployment decision. The decision record should contain:
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- The system version, data and test conditions.
- Performance, red-team, field, impact, privacy, security and accessibility findings.
- Known limitations, residual risks, mitigations and the person responsible for each one.
- Conditions of use, human-review requirements, prohibited uses and fallback arrangements.
- Feedback, complaint, appeal and incident-reporting channels.
- The approval owner, review date and events that require retesting, suspension or withdrawal.
Possible outcomes are not limited to “approved” or “rejected.” An agency can approve a narrow use with controls, require more evidence, run a time-limited pilot, select a non-AI alternative or stop the project. Record the rationale so a later reviewer can understand what evidence was available and what assumptions have changed.
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10. Monitor after deployment
Pre-deployment evaluation is a gate, not the end of governance. OECD guidance calls for continued monitoring and audits to detect expected and unexpected risks. Establish a baseline and review schedule before launch.
Monitor the system and the service
- Track accuracy, error types, subgroup outcomes, uncertainty and abstention rates against the approved use.
- Watch for data drift, changes in demand, new languages, altered upstream systems and supplier updates.
- Record overrides, complaints, appeals, incidents, near misses, downtime and time spent correcting outputs.
- Sample decisions for quality and fairness; protect personal data while retaining enough evidence to investigate.
- Give staff and residents clear channels to report harmful or incorrect behavior.
Define triggers for human escalation, incident review, model rollback, temporary suspension and a fresh impact assessment. Re-evaluate when the purpose, population, data, model, supplier, integration or legal context changes. Publish appropriate information about the system and its safeguards so public accountability does not depend on trust in a vendor’s marketing.
U.S. NIST guidance: useful framework, not a legal approval
NIST released AI RMF 1.0 on January 26, 2023. NIST describes it as intended for voluntary use, and its framework page says the document is being revised. Its value is as a common structure for identifying, measuring, managing and governing risk across the lifecycle; it does not replace federal, state, local or sector-specific law.
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NIST’s ARIA materials describe model testing, red-teaming and field testing and list a pilot evaluation report, NIST AI 700-2, dated November 2025. Treat the pilot and described methods as resources for designing evaluations, not as a government-wide certification scheme.
What a defensible evaluation looks like
A defensible process can be summarized as: a clearly defined public problem; a documented reason to use AI; assessments matched to the risks; realistic and adversarial tests; evidence from the supplier; meaningful participation by domain experts and affected people; named accountability; explicit conditions and remedies; and monitoring that can stop or change the system. If any of those elements is missing, the agency may still proceed in some low-risk contexts, but it should state the limitation and accept the associated risk openly.
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