The Tool Desk
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Start by defining the use and the consequences of failure
Before trying prompts or comparing scores, write down what the tool is expected to do and what people will do with its output. A system that is adequate for drafting a low-stakes summary may be unsuitable for a decision that affects someone’s access to a service. The acceptable level of error depends on the use and the consequences; there is no context-free accuracy threshold that establishes fitness or safety.
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Make a one-page evaluation brief
- Task: Describe the specific job, the input the tool receives, and the output it is expected to produce.
- Users and affected people: Identify who operates the tool and who may be affected by its outputs. Include perspectives from relevant subject-matter experts and users.
- Operating conditions: Note the languages, formats, environments, integrations, and other conditions expected in actual use.
- Failure consequences: Describe what could happen after an incorrect, incomplete, delayed, or inaccessible output. Distinguish errors that have different consequences.
- Decision and oversight: Specify who reviews outputs, what a reviewer can change, and how a person can ask for correction or contest an outcome.
- System identity: Record the product configuration and model or tool version, where available. Include relevant settings and connected services.
Set acceptance criteria only after these details are clear. A threshold is a decision informed by the task, evidence, and risk—not a universal pass mark. NIST’s AI Risk Management Framework treats trustworthy AI as contextual and socio-technical: datasets, model choices, organizational decisions, and human oversight all matter. The framework is voluntary, and NIST says its AI RMF 1.0 is being revised; check the current framework materials when planning an evaluation.
Build a realistic test set and measure task performance
A test is useful only if its examples resemble the conditions in which the tool will be used. NIST recommends clearly defined, realistic test sets representative of expected use, paired with documented measurement methods. The U.S. Intelligence Community’s AI Ethics Framework likewise asks whether data are representative and relevant to operational conditions. A clean demonstration or a few hand-picked examples cannot establish how a system will perform across the expected range of inputs.
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Choose examples before you inspect the results
- Describe the expected range of inputs. Include ordinary cases and foreseeable difficult cases from the intended setting, such as ambiguous wording, incomplete information, relevant format differences, and conditions that could change the answer.
- Set aside evaluation examples. Where feasible, keep the test set separate from material used to configure or tune the system. Otherwise, a strong result may reflect familiarity with examples rather than performance on new cases.
- Define the expected outcome. For each example, decide in advance what counts as correct, incorrect, incomplete, or requiring escalation. If the answer depends on context or judgment, define a review rubric and have qualified people assess outputs.
- Run the test on the version and configuration you intend to deploy. Keep inputs, settings, and conditions consistent when comparing tools. A result for one configuration does not automatically describe another.
- Record the method and evidence. Save the number and kind of examples, how they were selected, who judged the results, the system version, and known limits of the test.
Choose measures that reflect the task
Use measures that make the relevant errors visible. For classification or retrieval, precision and recall can describe different aspects of performance: precision concerns how many items identified by the system are relevant, while recall concerns how many relevant items it finds. If missing a relevant item is more harmful than flagging an irrelevant one—or vice versa—examine false negatives and false positives separately instead of relying on one combined figure.
For generated text, a single automatic score may not establish whether an answer is correct in context. Review outputs against the task’s criteria, including whether key information is missing, whether the answer is usable, and whether a person should have been referred to a human. Use more than one reviewer when the judgment calls for it, and document how disagreements are handled.
Report the result with its sample and method, not as a bare percentage. A small or narrow sample may fail to represent actual use, and even a favorable average can hide a critical kind of error. Do not present a result from one test set as a guarantee for every user, input, or future version.
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Check for harmful disparities instead of relying on an average
Bias testing asks whether the tool’s errors or outcomes differ in ways that matter to people affected by the task. Start by identifying the demographic or other groups relevant to the use case, then compare performance for those groups and operating conditions. The right groups depend on the decision and population; a generic list cannot substitute for that analysis.
Trace where a disparity could enter
- Data collection: Ask whether the examples reflect the people and conditions in the intended use, and whether important cases are missing.
- Labeling and reference answers: Examine how expected outcomes were assigned and whether those judgments could encode inconsistent assumptions.
- Model and product choices: Consider whether design or model-selection decisions affect which outputs are produced or how they are presented.
- Human use: Look at how staff interpret, rely on, or override outputs. A system’s effect depends partly on how it is used in the surrounding process.
For each relevant group, compare the same task measures used in the overall evaluation, including false-positive and false-negative patterns where those errors have different consequences. Also examine the outcomes people experience, not only whether a model output matches a reference label. Document which groups were tested, the limits of the available examples, and which groups or conditions were not evaluated. Do not describe a single average as proof that a tool is fair.
Differences can involve trade-offs: a mitigation that changes results for one group or error type may affect another measure. Record the change and its consequences rather than hiding it behind one fairness score. The Intelligence Community framework asks evaluators to consider biased outcomes and whether data are representative of, and relevant to, operational conditions.
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Review privacy across the whole data lifecycle
Privacy is not settled by a short label or by whether a tool asks for sensitive information in one screen. Map what information enters the system, what connected sources may be involved, what is retained or made available, and what controls users have. UNESCO’s Recommendation on the Ethics of Artificial Intelligence calls for privacy and data protection throughout the AI lifecycle. NIST identifies confidentiality, anonymity, and individual control as relevant privacy values.
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- List what users submit. Include text, files, recordings, images, identifiers, and other information that may be present in an input. Consider whether a seemingly ordinary prompt could contain personal or confidential details.
- Check integrations and connected sources. Identify whether the tool can draw on connected accounts, files, databases, or other services, and what information those connections expose to the workflow.
- Ask what happens after submission. Review the provider’s documentation and product settings for retention, access, reuse, deletion, and user controls. Distinguish what the terms say from what a particular setting changes.
- Verify the controls in the product. Do not assume a general privacy statement means a specific control is enabled or applies to your account, configuration, or use.
- Use minimized test data until safeguards are understood. Avoid using sensitive real-world information in an evaluation unless the applicable protections and handling have been established.
Record any unanswered questions as limits on the evaluation, not as assurances. A control that users cannot find, understand, or apply may not provide meaningful control in practice. Privacy protections can also involve trade-offs with accuracy in some conditions, as NIST notes; document relevant trade-offs rather than assuming one dimension can be optimized without affecting another.
Test accessibility in the complete user journey
A model’s accuracy score says nothing by itself about whether people can reach, understand, operate, and recover from errors in the tool. For a web interface, use the W3C Web Content Accessibility Guidelines (WCAG) as the established reference, and test the actual product journey rather than checking a statement of conformance alone. Include people with access needs and assistive technologies where possible.
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Walk through more than the main screen
- Reach the task: Check whether users can find and open the relevant features, including with assistive technologies.
- Enter and review information: Try the actual input methods and determine whether instructions, controls, and feedback are understandable.
- Use and interpret outputs: Check whether generated content is available in usable formats and whether important distinctions, warnings, or limitations are understandable.
- Correct problems: Test whether a person can revise an input, correct an output, retry safely, or recover when something goes wrong.
- Reach human help: For tasks that need review or escalation, check whether users can identify and reach the person or process responsible.
AI-specific questions belong in this journey too: can users understand the tool’s limitations, tell when a human should review an answer, and access an alternative format or a route to assistance? WCAG is a web accessibility reference, not a complete AI-specific test protocol. Do not claim that a single WCAG check settles every question about AI interactions, generated content, or the broader service.
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When choosing between tools, compare them on the requirements that matter for the defined use. Include the option of keeping the current process or not using AI; a tool does not become appropriate merely because it performs better than another candidate on one metric. NIST cautions that trustworthy characteristics can trade off and that their relative importance depends on context.
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- Group outcomes: Compare affected groups and conditions, considering whether the sample is adequate for the conclusions being drawn.
- Privacy: Compare collection, retention, reuse, access, deletion, and user control based on the specific product settings and documentation.
- Accessibility: Compare the end-to-end interface, outputs, correction paths, and access to human help.
- Repeatability: Consider whether documentation and version information are sufficient to reproduce and audit the evaluation.
- Accountability: Compare the availability of human review, correction, escalation, and clearly assigned responsibility.
- Operational burden: Weigh the staff time and other resources needed to test, oversee, and maintain the process against its expected benefits and potential harms.
Keep the comparison tied to the decision brief. A candidate with a better average score may still be a poor choice if it performs badly on a consequential error type, creates unacceptable privacy exposure, or cannot be used by part of the intended population.
Make the decision auditable and monitor the deployed system
Before deployment, document the evidence and the conditions under which the decision is valid. The U.S. Intelligence Community AI Ethics Framework says: “Every system must be tested for accuracy in an environment that controls for known and reasonably foreseeable risks prior to being deployed.” Its “Testing Your AI” section also emphasizes version and data provenance to support repeatability and audit.
Keep an evaluation record
- Intended task, users, affected population, operating conditions, and consequences of errors.
- Tool and model version, configuration, inputs, test data, and how the expected results were established.
- Measures, results, group comparisons, sample limitations, and any accuracy or fairness trade-offs.
- Privacy data-flow findings, accessibility findings, and unresolved limits.
- Decision rationale, responsible owner, human-review process, correction or contest route, and safeguards.
- Conditions that will trigger a new evaluation.
Assign an owner for oversight rather than treating approval as the end of the work. Monitor whether the deployed tool continues to perform as intended and whether the actual environment or use changes. Retest after changes to the model, interface, data, configuration, or task; a prior result does not automatically apply to a changed system. NIST identifies ongoing testing and monitoring as ways to confirm that deployed systems perform as intended.
Use a clear decision rule
Approve a tool only for the scope your evaluation supports. If a critical error, group disparity, privacy question, or accessibility barrier remains unresolved, narrow the use, add a safeguard or human review, or do not deploy it for that task. State what evidence would change the decision and who is responsible for checking it. That turns a test result into a bounded, accountable decision rather than a permanent claim that a tool is accurate, fair, private, or accessible in every setting.
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