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Measure AI accuracy and human-review costs as parts of the same government workflow evaluation. Compare outputs with an adjudicated reference set and the current process; choose metrics that fit the task and the consequences of errors; and record human time spent checking, correcting, escalating, and reworking results. Report those findings alongside uncertainty and service outcomes: a strong-looking score can hide costly review or serious errors.
Define the task and the boundary of the evaluation
Start by specifying what the AI does and where its work ends. An AI system that drafts a summary, extracts fields, or classifies cases should be scored on that task—not described as accurate at making the final public decision unless the evaluation actually measures that decision.
Document the system and workflow versions, data and configuration, downstream steps, expected workload, human decision-maker’s authority, affected groups, and the consequences of incorrect outputs. Define the unit being judged: for example, a document, an extracted field, a classification, or a generated theme. This keeps the metric’s denominator clear and prevents a task-level score from being mistaken for a measure of the whole service.
NIST’s voluntary AI Risk Management Framework calls for testing before deployment and regularly during operation, including under conditions similar to deployment. It also calls for documenting what cannot or will not be measured. NIST says AI RMF 1.0 is under revision, so check the current NIST AI RMF page for framework updates and check applicable agency policy separately. The framework’s measurement guidance is in the AI RMF 1.0 and the NIST AI RMF Playbook—Measure.
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Choose an evaluation design that answers the right question
A blind evaluation and a live, human-reviewed evaluation measure different things. Use the first to isolate output quality; use the second to assess the operational human-AI process. If a deployment decision depends on both model capability and workflow performance, report both rather than combining them into one score.
| Design | What it measures | What it does not establish by itself | Useful evidence to collect |
|---|---|---|---|
| Blind evaluation | AI outputs compared with a human or adjudicated benchmark without reviewers seeing the AI output while making their own judgment. | How much time live reviewers spend, which errors they catch, or how the combined workflow affects service outcomes. | Metric results by task and error type, reference-label quality, uncertainty, and comparison with the existing process. |
| Live, human-reviewed evaluation | The combined operational process, including review, edits, overrides, escalation, and downstream rework. | Standalone AI performance if human changes are reported as though they were original model outputs. | Initial AI output and final reviewed result separately, review effort, errors caught and missed, escalation, and service outcomes. |
In a live pilot, preserve the initial AI output so it can be distinguished from the reviewer’s final version. State whether reviewers see the AI result before forming their own judgment, what evidence they can inspect, whether they can edit or reject outputs, and which cases require escalation. UK guidance on AI use in marking emphasizes specifying the review process; checking AI work may not perform like doing the original task independently. See the UK AI use in marking principles.
Build a reference set and choose task-appropriate metrics
Make the test cases representative
Sample cases across routine work, difficult cases, rare situations, and high-impact outcomes. The sample should reflect the expected deployment population, task mix, and operating conditions. Have qualified reviewers establish reference labels, adjudicate disagreements, and record the rules they used. Keep evaluation cases separate from development or tuning data, and document inclusion criteria, missing or ambiguous cases, and limits on generalizing the results.
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Match the metric to the task
- Classification: report a confusion matrix and task-appropriate measures such as precision, recall, and F1. These distinguish different error patterns; name the metric and denominator rather than calling any one of them simply “accuracy.”
- Extraction or matching: assess exact or otherwise acceptable matches at the field level, including omissions. A document-level score can obscure which fields fail.
- Generated summaries or themes: assess factual correctness, coverage, unsupported claims, material omissions, and agreement with expert review. A single similarity or agreement score may not capture whether an output invents a claim or leaves out a material view.
NIST recommends selecting methods and metrics based on mapped risks, documenting test sets and tools, measuring under conditions similar to deployment, and recording characteristics that cannot be measured. See the NIST AI RMF 1.0 and its Measure Playbook.
Measure what human review actually does
A reviewer’s approval click is not evidence that review was effective. Record how many outputs were reviewed and what reviewers did with them. Where appropriate and safe, test the review process with known or seeded errors to see whether reviewers detect them; document the limits of that test.
- Outputs reviewed, and the proportion of eligible outputs reviewed.
- Edits, rejections, overrides, errors caught, and errors missed.
- Disagreements, escalations, adjudications, and downstream rework.
- Reviewer time by stage and role, plus throughput and queue time.
Review by exception may reduce labor, but it can also leave errors in cases that are not selected for review. Document the selection or threshold logic and evaluate its missed-error risk. GAO’s AI Accountability Framework includes workload assessments and asks whether AI provides accurate, interpretable information to human users. It is a 2021 federal framework, so use it as guidance alongside more recent, applicable agency policy.
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Calculate review labor and compare it with the current process
For the same task mix, record time under both the AI-assisted and current workflows. Include intake or setup, review, correction, escalation, adjudication, final quality assurance, and downstream rework; include training and tool administration when material. Measure throughput and queue time as well as minutes per item, since a new bottleneck can slow service even when an individual review is quicker.
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A practical local accounting calculation is:
Review labor cost = measured reviewer hours by role × the agency’s applicable loaded hourly labor rate
Keep one-time setup, training, and integration effort separate from recurring per-item work. State the volume and period used, the roles included, and whether the AI changed the amount of work completed or the share of outputs covered by review. The calculation converts measured labor into a local cost; it is not a cost formula prescribed by NIST or GAO.
For a transparent example of phase-by-phase time accounting, the UK Behavioural Insights Team compared one AI-assisted rapid evidence review with one human-only review on one topic. The AI-assisted exercise took 90.5 hours versus 117.75 hours for the human-only exercise—23% less total time in that comparison—but spent more time revising its draft (27 hours versus 18.25). The authors said the case study’s results were not generalisable. These are study-specific observations, not forecasts for other tasks or agencies. See the BIT comparative study.
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Before examining results, set acceptable limits for consequential errors, review workload, subgroup differences, escalation, and service outcomes. Compare AI-assisted performance with the existing process, include uncertainty and the scope of the evaluation set, and define who can pause or change the system if a limit is exceeded. Choose corrective actions in advance, such as expanding review, changing the workflow, or stopping use for a particular task.
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Continue checking performance and reviewer behavior during operation. Changes in data, model, task mix, or policy can make an earlier evaluation less relevant. The NIST Measure Playbook recommends documenting performance limits, corrective actions, pre- and post-deployment performance, and ongoing monitoring. For UK central-government evaluation, the Magenta Book advises proportionate quality assurance, including checking all outputs or a representative sample when checking every output is not feasible, with documentation and disclosure of AI use. Verify the applicable guidance and version for the relevant jurisdiction.
What published government figures show—and what they do not
The UK Department for Transport and The Alan Turing Institute’s 2025 evaluation of Consultation Analysis Tool (CAT) v1.0 illustrates why results need their metric, design, and workflow context attached. The figures below describe that tool and its evaluation datasets; they are not general performance or savings estimates for government AI.
| Published result | Design and interpretation |
|---|---|
| About 75% theme-generation recall in the blind evaluation; 90% recall in live pilots. | The 90% result followed structured human review; it is not standalone model performance. |
| Theme-mapping F1 of 0.75 in the blind design; 0.93 when comparing initial mappings with human-adjusted mappings. | These are different comparison designs and should not be presented as a like-for-like improvement in standalone model accuracy. |
| Over 92% overall raw agreement between CAT and human experts in both blind and non-blind designs. | The report treats prevalence figures as estimates and discusses chance-corrected agreement separately; raw agreement is not interchangeable with accuracy or F1. |
| Estimated annual savings of £1.5–4 million if CAT were scaled across DfT’s full consultation portfolio. | This is a modeled portfolio-wide estimate for CAT and that workload, not a realized saving or a transferable agency forecast. |
The evaluation describes human effort as particularly valuable for reviewing and validating generated themes and synthesizing final results for policy decisions. For the methods and qualifications behind these figures, see the CAT v1.0 Evaluation.
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Report enough detail for others to interpret the result
A useful evaluation report lets an agency, auditor, or affected stakeholder understand what was tested, what the numbers mean, and where they may not apply. Include:
- The task, unit of analysis, workflow boundary, system and configuration versions, and evaluation period.
- How cases were sampled, how reference labels were created and adjudicated, and which groups or operating conditions were represented.
- Metric definitions and denominators, error severity, uncertainty, and known measurement limits.
- Blind and live-review results separately, with reviewer authority, review coverage, interventions, missed errors, and rework.
- Labor by stage and role, local rate assumptions, fixed setup separated from recurring effort, volume, throughput, and queue time.
- Baseline comparisons, acceptance limits, corrective actions, monitoring owners, and applicable privacy, accessibility, legal, and ethics considerations.
This accounting makes it possible to distinguish a capable model from a workable service process—and to see whether apparent time savings depend on shifting effort, reducing review coverage, or accepting a different pattern of errors.
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