The Tool Desk
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The process below is jurisdiction-neutral. Legal duties, approval thresholds and procurement rules depend on the country, sector and use case, so this workflow complements—not replaces—local legal and policy review.
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What a pre-deployment evaluation must establish
An evaluation should answer five practical questions:
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- Need: Does the proposed AI address a defined public problem better than a non-AI alternative?
- Suitability: Does it perform the intended task reliably under real operating conditions?
- Impact: What could happen to people, rights, equality, privacy, security and public resources?
- Control: Can the agency explain, supervise, contest, correct and stop the system?
- Evidence: Is the information sufficient for an accountable official to accept residual risk?
No single benchmark, audit or certification answers all five. The OECD’s 2025 guidance places the choice of solution before deployment: governments should first consider whether AI is the best way to achieve the desired public outcome.
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1. Define the public task and compare alternatives
Write a precise use-case statement
Describe the service or administrative task in ordinary language. Identify who is affected, what outcome the agency wants, what decision the system will inform, and what it will not decide. “Use AI to improve benefits processing” is too broad; “prioritize applications for manual review using information already submitted” is testable.
Record the affected groups, service geography, languages, operating hours, legal authority and consequences of an error. Distinguish assistance from automated decision-making. A drafting tool for internal correspondence has a different risk profile from a system that recommends who receives housing, immigration status or healthcare.
Compare non-AI options
Evaluate process redesign, additional staff, conventional rules-based software, statistical methods and a human-led approach alongside AI. Include implementation time, cost, reliability, accessibility and risk. If a simpler option achieves the public outcome with fewer unknowns, choosing AI requires a clear justification.
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Create a system record before testing. It should remain versioned so that results can be tied to the exact model, configuration and data used.
- Supplier, developer, operator and accountable government owner.
- Model type, release or version, interfaces, connected tools and update process.
- Inputs, training-data provenance where available, retention, data flows and access controls.
- Outputs, confidence information, human review points and downstream actions.
- Permitted, prohibited and out-of-scope uses.
- Known limitations, dependencies, service-level assumptions and failure behavior.
- People who can override, suspend, correct or retire the system.
NIST’s AI Risk Management Framework (AI RMF) treats risk management as spanning design, development, deployment, use and evaluation. Its documentation emphasis is useful even where the framework is not adopted formally.
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3. Assess risks and wider impacts
Start with a risk register that links each hazard to an affected group, likelihood, severity, existing controls, proposed mitigation, owner and evidence needed. Consider inaccurate or unavailable service, discrimination, privacy and data-protection failures, cyberattack, prompt or data poisoning, misuse, vendor outage, unsafe automation, financial loss and damage to public trust.
Then conduct an impact assessment suited to the service. Examine effects on rights, equality, access, dignity, socioeconomic outcomes, workers and communities—not only model behavior. Include indirect effects, such as people changing how they apply for a service because they believe an automated system will judge them.
Different assurance activities answer different questions
| Activity | Primary question | Typical evidence |
|---|---|---|
| Risk assessment | What can go wrong, and how severe could it be? | Hazard register, likelihood and severity ratings, mitigations |
| Impact assessment | How could the use affect people, rights, equality or public interests? | Stakeholder analysis, rights and equality analysis, distributional findings |
| Bias audit | Are outcomes or errors materially different for relevant groups? | Disaggregated performance, error and outcome analysis |
| Compliance audit | Does the system and its operation meet applicable law and policy? | Control tests, records, legal and policy mapping |
| Conformity assessment | Does a defined specification or standard appear to be met? | Independent or internal assessment against stated criteria |
| Formal verification | Can specified properties be proven for a defined system or component? | Mathematical or formal proofs, where feasible |
The OECD/UNESCO G7 toolkit distinguishes these mechanisms; they are complementary, not interchangeable. A bias audit cannot establish cybersecurity, and a compliance review cannot prove that a model works for every affected population.
4. Plan the evidence before running tests
Write an evaluation protocol that states the decision questions, pass conditions, test data, scenarios, reviewers and handling of uncertainty before results are available. This prevents a team from changing the standard after seeing a favorable score.
Specify what will be tested
- The exact system version, prompts, configuration, retrieval sources and connected tools.
- Representative cases, edge cases, rare events, languages, accessibility needs and relevant demographic groups.
- Expected operating conditions, including workload, latency, missing data, network outages and human hand-offs.
- Measures of correctness, completeness, calibration, consistency, safety, privacy, security, usability and equity.
- What constitutes an unacceptable failure, a required human review or a retest.
- Who can access raw results, who validates them and who has authority to accept residual risk.
Do not invent a universal accuracy threshold. The sources do not establish one threshold that makes every government system acceptable; an appropriate level depends on the task, consequences, alternatives and jurisdiction.
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5. Test the system at several levels
NIST’s Assessing Risks and Impacts of AI (ARIA) describes three evaluation levels: model testing, red-teaming and field testing. The program is an evaluation approach, not a universal government certification or mandatory approval process. Its central lesson is to test technical and contextual robustness, not accuracy alone.
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Model and task testing
Use a held-out, task-relevant dataset and document how it was sampled. Measure the outcomes that matter for the service: for example, false negatives as well as overall accuracy when missing an eligible person is especially harmful. Report uncertainty, confidence calibration, abstention behavior and performance by relevant groups. Check data leakage, duplicate cases and changes between training and evaluation data.
Red-teaming and stress testing
Ask trained testers and domain specialists to seek harmful or misleading behavior. Probe ambiguous wording, adversarial inputs, privacy extraction, unsafe recommendations, prompt injection, out-of-distribution cases and attempts to bypass safeguards. Test degraded conditions such as missing fields, language variation, high volume and unavailable external services. Record every failure, its reproducibility, severity and remediation.
Field and workflow testing
Run a controlled pilot or shadow mode using realistic workflows before the output can affect a person. Observe staff decisions, escalation rates, time saved, workarounds, accessibility and whether users misunderstand confidence or explanations. Involve affected communities and independent subject-matter experts where decisions concern rights or access to services. A model that performs well in a laboratory may fail when records are incomplete, policies change or staff rely on it too heavily.
6. Build evaluation into procurement
Procurement is part of assurance, not an administrative step after testing. Requirements should give the agency enough access and cooperation to evaluate the system before go-live and throughout its life.
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- Require documentation of capabilities, limitations, data practices, updates and known incidents.
- Specify access to test environments, logs, outputs, evaluation evidence and qualified supplier personnel.
- Require the supplier to support agency-designed tests, red-team exercises and reproducible retesting after updates.
- Define data ownership, confidentiality, retention, security controls, audit rights and subcontractor disclosure.
- Set notification periods for material model, data or service changes and define when approval must be revisited.
- Include service suspension, exit, data return and transition provisions.
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 contract terms to their own rules.
A staged competition can make evidence a condition of progression: shortlist on documented suitability, test finalists on the same scenarios, conduct a controlled pilot, then approve only the configuration that passed. Avoid allowing each supplier to choose different datasets or metrics when results will be compared.
7. Make a documented deployment decision
The accountable agency—not only the vendor or technical team—should sign a decision record. It should contain:
- The public problem, alternatives considered and reason AI is justified.
- The approved purpose, prohibited uses, affected groups and operating boundaries.
- The tested version, data, scenarios, metrics, results and known uncertainties.
- Risk and impact findings, unresolved issues and compensating controls.
- Human oversight design, appeal or correction routes and accessibility arrangements.
- Named decision owner, operational owner, technical owner, privacy/security contacts and incident lead.
- Conditions for launch, retesting triggers, suspension authority and review date.
Possible outcomes are not limited to “pass” and “fail.” The agency can reject the system, require redesign, approve a limited pilot, approve use only with human review, or approve a bounded deployment subject to explicit controls. Any residual risk accepted should be stated plainly, with the official who accepted it.
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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.
Define a monitoring plan
- Track performance, error types, disparities, abstentions, overrides, complaints, appeals, incidents, security alerts and service outcomes.
- Monitor data and concept drift, policy changes, supplier updates and changes in user behavior.
- Sample cases for expert review; do not rely only on automated dashboards.
- Provide clear channels for staff and affected people to report harm or request correction.
- Set thresholds that trigger investigation, additional testing, rollback or suspension.
- Publish appropriate summaries so oversight bodies and the public can understand how the system is used.
Keep an audit trail of model versions, prompts or rules, decisions, overrides, incidents and corrective actions. Schedule periodic reassessment even when no incident has occurred.
How to compare candidate systems or an AI and non-AI approach
Use identical tasks, assumptions and evidence requirements for every option. The following axes are a practical synthesis of the cited guidance, not an official scoring formula.
| Comparison axis | Questions to ask |
|---|---|
| Public-purpose fit | Does it solve the stated problem, and is AI necessary? |
| Contextual performance | How reliable is it for real cases, edge cases and relevant groups? |
| Potential impact | What harms, rights effects, inequalities and operational consequences could result? |
| Testability | Can the agency inspect, challenge, reproduce and retest behavior? |
| Documentation and supplier cooperation | Will the supplier disclose limitations, support testing and notify changes? |
| Governance readiness | Are accountability, feedback, incident response and monitoring in place? |
Roles that keep evaluation independent and usable
Separate, as far as practical, the team building or buying the system from the people who challenge its evidence. Include service owners, frontline staff, data protection and security specialists, legal and procurement officers, accessibility and equality experts, independent evaluators and representatives of affected communities. Give reviewers enough authority to delay deployment and enough access to reproduce findings.
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U.S. NIST guidance: useful framework, not a legal approval checklist
NIST released AI RMF 1.0 on January 26, 2023. NIST describes it as intended for voluntary use, and its AI RMF page indicates that the framework is being revised. It should not be described as a binding legal requirement or a government certification.
“The Framework users and AI actors should consider and encompass trustworthiness characteristics during pre-design, design and development, deployment, use, and test and evaluation of AI technologies and systems.”
National Institute of Standards and Technology, AI Risk Management Framework FAQs
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NIST’s ARIA evaluation structure has three levels—model testing, red-teaming and field testing. NIST lists the ARIA 0.1 pilot evaluation report as NIST AI 700-2, dated November 2025. These materials can structure an agency’s evidence plan, but they do not create a universal pass mark or replace applicable U.S. federal, state or local requirements.
Quick Recap
Common failure modes and corrective actions
| Failure mode | Why it fails | Correction |
|---|---|---|
| Choosing a model before defining the problem | Performance is optimized for a tool rather than a public outcome. | Restart with alternatives analysis and a bounded use case. |
| Relying on a vendor benchmark | The benchmark may not represent local data, users or consequences. | Require independent, task-specific and disaggregated testing. |
| Testing only average accuracy | Rare but severe failures and group disparities disappear in the average. | Measure consequential errors, edge cases, calibration and subgroup outcomes. |
| Treating an audit as proof of safety | Each assurance activity covers only particular questions. | Combine risk, impact, bias, compliance and technical evaluations as appropriate. |
| No change-control trigger | Updates can invalidate pre-deployment evidence. | Contract for notice, versioned records and mandatory retesting. |
| No route to challenge a decision | People cannot correct bad data or contest harmful outcomes. | Provide accessible human review, correction and appeal processes. |
| Unowned monitoring | Drift and incidents remain invisible until harm is widespread. | Name an operational owner, metrics, thresholds and suspension authority. |
A concise pre-deployment checklist
- Public problem, desired outcome and non-AI alternatives are documented.
- Purpose, boundaries, version, data flows, operators and affected groups are recorded.
- Risk, impact, privacy, security, equality and accessibility questions have owners.
- Evaluation protocol, scenarios, metrics and failure thresholds were set before testing.
- Model, red-team and realistic workflow or field tests have been completed as appropriate.
- Results include uncertainty, subgroup findings, limitations and unresolved risks.
- Procurement terms secure evidence, testing cooperation, update notice, logs and exit rights.
- Decision owner has documented conditions, residual-risk acceptance and stop triggers.
- Monitoring, incident response, feedback, appeal and periodic reassessment are funded and assigned.
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