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Measure AI accuracy and human-review costs as parts of the same government workflow evaluation. Compare outputs with an independently established reference set and the current process; assess task-appropriate errors, including their severity; and record the human time spent checking, correcting, escalating, adjudicating, and reworking outputs. Report those results alongside service outcomes, subgroup performance, and uncertainty. A model score alone cannot show whether the workflow is safe, useful, or less costly.
Define the task and what counts as success
Start by drawing a boundary around the task being evaluated. Specify what the AI does, what decision or service it supports, who retains decision-making authority, and what happens after the AI produces an output. If it drafts a summary, for example, measure the summary task directly; do not describe that score as the accuracy of the final public decision.
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Record the system and workflow versions, relevant data and configuration, expected case volume, task mix, affected groups, and the consequences of errors. Define the unit of evaluation—a case, classification, extracted field, recommendation, or generated summary—before calculating a metric. The unit determines what counts as a correct result and what denominator a score uses.
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Choose an evaluation design that answers the right question
Use a blind evaluation to estimate the quality of AI outputs independently of reviewer influence. Use a live, human-reviewed evaluation to assess the combined workflow: what reviewers catch or miss, how they change outputs, and how much work the process requires. These designs answer different questions and should not be blended into a single “accuracy” number.
| Design | What it measures | What to capture | Main limitation |
|---|---|---|---|
| Blind evaluation | AI output compared with a human or adjudicated reference, with evaluators not seeing the AI output while forming their own judgment. | Task-specific output metrics, disagreements with the reference, error types and severity, and uncertainty. | It does not show how reviewers perform when using AI in the real workflow or how much time that workflow takes. |
| Live human-reviewed evaluation | The operational human-AI process, including review, corrections, escalations, and downstream work. | Initial AI output and subsequent edits, errors caught and missed, review coverage, reviewer time, rework, and service outcomes. | Reviewer changes can improve final outputs, so post-review results do not represent standalone model performance. |
When both model capability and operational performance matter, report both designs separately. A blind comparison and a live pilot may use different cases, review conditions, and denominators; label each clearly.
Build a defensible reference set and select task-specific metrics
Make the cases representative and labels reviewable
Sample cases that reflect the intended deployment population and task mix, including routine, difficult, rare, and high-impact situations. Document how cases were selected, what was excluded, and where records are missing or ambiguous. Hold evaluation cases back from development or tuning so the results are not simply a measure of performance on familiar examples.
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Have qualified reviewers establish reference labels using written rules, and adjudicate disagreements rather than treating one reviewer’s judgment as unquestionable truth. Keep the labeling instructions and adjudication record. If reference judgments remain uncertain, say so and account for that uncertainty in the reported results. A reference set supports conclusions about its cases; it does not by itself prove how the system will perform in every operational setting.
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Match metrics to the output
- Classification: Report a confusion matrix and task-appropriate measures such as precision, recall, and F1. They answer different questions: for example, recall concerns the share of relevant cases found, while precision concerns the share of flagged cases that are relevant. Include subgroup error rates where the data and task permit.
- Extraction or matching: Assess exact or explicitly acceptable matches at the field or match level, along with omissions. State the denominator and what qualifies as an acceptable match.
- Generated summaries or themes: Assess coverage, factual correctness, unsupported claims, and material omissions against expert review. A fluent output may still leave out an important view or introduce a claim the source material does not support.
Do not call raw agreement, F1, or any other single measure simply “accuracy.” Name the metric, unit, denominator, dataset, and evaluation design. Report error severity as well as frequency: an uncommon consequential error may matter more to a service than many low-impact mismatches. Raw agreement can also hide class imbalance or minority errors. NIST recommends selecting methods and metrics in light of mapped risks, documenting test sets and tools, and recording characteristics that cannot be measured in the AI RMF Playbook’s Measure guidance.
Specify and test the human-review mechanism
“Human reviewed” is not a complete description of oversight. Document what reviewers see, what evidence they can inspect, whether they form an independent judgment before seeing the AI output, whether they can edit or reject it, how overrides are recorded, and which cases must be escalated. A blind comparison can help isolate output quality; a live pilot shows how the review arrangement works in practice.
For the live workflow, track how many outputs are reviewed and what share of all outputs that represents. Record reviewer changes, errors caught and missed, disagreements, escalations, adjudications, and downstream rework. Where ethically and operationally appropriate, test review with known or seeded errors to learn whether reviewers notice consequential failures. Approval clicks alone do not establish that review is effective.
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Review by exception may reduce routine checking, but evaluate the threshold that sends cases to review and the errors that pass below it. UK guidance for AI use in marking cautions that review should be clearly specified and that people checking AI outputs may not perform as they would on the original task. GAO’s accountability framework also includes workload assessments and questions about whether AI gives human users accurate, interpretable information. See the UK principles of AI use in marking and the GAO AI Accountability Framework.
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Measure review time and calculate local labor cost
Track the work by stage
Compare the AI-assisted process with the existing process using the same task mix and a defined measurement period. Record time by relevant stage, such as intake or setup, review, correction, escalation, adjudication, final quality assurance, and downstream rework. Include training and tool administration when they materially affect the workload. Record staff roles as well as hours: different roles may have different applicable labor rates.
Measure throughput and queue time alongside minutes per item. A reduction in reviewer minutes does not necessarily mean faster service if work has shifted to a bottleneck, such as escalation or final quality assurance. Also record how much work is completed and how review coverage changes; lower cost achieved by reviewing fewer cases is not equivalent to the same level of checking for less effort.
Separate ongoing effort from setup
For a local labor estimate, calculate review labor cost = measured reviewer hours by role × the agency’s applicable loaded hourly labor rate. Use the agency’s appropriate rate and state the period, volume, and roles included. Keep fixed setup, integration, and other one-time work separate from variable per-item effort so readers can see what recurs as case volume changes. This is a practical accounting method, not a cost formula prescribed by the cited agencies.
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| Published evaluation | Reported result | How to interpret it |
|---|---|---|
| UK Department for Transport and The Alan Turing Institute, Consultation Analysis Tool (CAT) v1.0 evaluation, 2025 | About 75% theme-generation recall in the blind evaluation; 90% recall in live pilots after structured human review. | The latter is a human-reviewed workflow result, not standalone model performance. Both figures concern CAT v1.0 and its evaluation datasets. |
| Same CAT v1.0 evaluation, 2025 | Theme-mapping F1 was 0.75 in the blind design; F1 was 0.93 when initial mappings were compared with human-adjusted mappings. | The comparison designs differ; the human-adjusted result should not be presented as a blind model score. |
| Same CAT v1.0 evaluation, 2025 | Over 92% overall raw agreement between CAT and human experts in both blind and non-blind designs. | The report discusses chance-corrected agreement separately and cautions that prevalence statistics should be interpreted as estimates. |
| Same CAT v1.0 evaluation, 2025 | Estimated annual savings of £1.5–4 million if CAT were scaled across DfT’s full consultation portfolio. | This is a modeled estimate for that tool and portfolio assumption, not a realized saving or a forecast for another agency. |
| Behavioural Insights Team, AI-assisted versus human-only evidence review, 2024 | One human-only rapid evidence review took 117.75 hours and one AI-assisted review took 90.5 hours, 23% less total time in that exercise. Draft revision took 27 hours in the AI-assisted review and 18.25 hours in the human-only review. | This was one review per approach on one topic; the authors state that the case-study results are not generalisable. |
The CAT figures are from the CAT v1.0 evaluation. The evidence-review time figures are from the BIT comparative study. Treat these as bounded examples of evaluation design, not universal performance or savings estimates. DfT’s separate CAT evaluation page provides further evaluation context.
Set limits, monitor results, and report the method
Before reviewing the results, define acceptable limits for overall and consequential errors, reviewer workload, subgroup differences, and relevant service outcomes. Set out what happens when a limit is exceeded, who can pause or change the system, and how the agency will verify corrective action. Thresholds should reflect the mission and consequences of error; the cited frameworks do not establish one universal acceptable accuracy or workload threshold.
After deployment, continue measuring model performance and reviewer behavior. Changes in data, model, task mix, policy, or workflow can make earlier results less informative. Document performance limits, corrective actions, uncertainty, test conditions, comparison baselines, and features that could not be measured. Seek independent assessment and input from domain experts, users, and affected communities where appropriate. NIST’s Measure guidance addresses monitoring and documentation; the UK Magenta Book describes proportionate quality assurance, including checking all outputs or a representative sample when checking all is impractical. Applicability and current requirements depend on jurisdiction and agency policy.
A useful report states the task and workflow boundary, system version, evaluation design, reference-set construction, metric definitions and denominators, error severity, reviewer protocol, time by stage, cost assumptions, baseline, uncertainty, dataset limits, and monitoring plan. Address privacy, accessibility, and applicable legal or ethics requirements in the workflow-specific assessment; these depend on the jurisdiction and use case.
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