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This U.S.-focused guide lays out a practical process for agencies, investigators, procurement teams, and forensic labs. It is not jurisdiction-specific legal advice; applicable rules on procurement, privacy, records, evidence disclosure, civil rights, and permitted uses vary and should be checked with current local counsel and policy.
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What does it mean to evaluate an AI tool for an investigation?
Evaluation is evidence-gathering about whether a system is suitable for a defined use, under realistic conditions, and whether its risks can be governed. It is not a general judgment that a product is “accurate” or that AI is useful to law enforcement. A tool may perform differently depending on its task, input data, users, local population, workflow, and the decisions its output could influence.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesThe National Artificial Intelligence Advisory Committee (NAIAC) recommends field testing law-enforcement AI tools, including assessment of accuracy, nondiscrimination, rights-respecting use, and cost-effectiveness. The U.S. Department of Justice’s December 3, 2024, final report on artificial intelligence and criminal justice specifically advises forensic science service providers to procure tools with a demonstrated acceptable level of accuracy. Neither recommendation supplies one accuracy threshold that applies to every investigative application. The agency has to set acceptance criteria that match the task and consequences.
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Keep three questions distinct throughout the process:
- Does the tool perform its stated function? Measure the output against an appropriate reference, document errors, and test the conditions in which it is expected to operate.
- Is the proposed use appropriate? A tool that can generate a lead is not automatically suitable to establish a fact, support a consequential decision, or substitute for an examiner’s judgment.
- Can the agency manage the system responsibly? Staff need workable review procedures, privacy and security controls, training, accountability, and a way to respond when the tool fails or causes harm.
These are connected but not interchangeable. Good measured performance does not settle whether a use is lawful, fair, secure, or operationally justified.
How should an agency define the intended use?
Start by writing a short use specification before reviewing vendors or setting up a test. A statement such as “use AI to help investigations” is too broad to identify suitable evidence or meaningful performance measures.
Describe the task and its boundaries
Record what investigative task the system is intended to support, who will use it, what inputs it receives, what it produces, and which decisions could be influenced by that output. State whether the output is a lead, a triage aid, an analytic result, or something that could be treated as evidence. Specify what the tool is not authorized to do. For example, an agency should decide in advance whether a result may only prioritize material for a qualified person to inspect or whether it could be cited in a report.
Identify the points where a human must independently review the result. “Human in the loop” is not an adequate procedure by itself: name the role, the review action, the information available to the reviewer, and how the reviewer records disagreement or uncertainty. The reviewer should not be expected to approve an output without enough context to assess it.
Translate the use into testable questions
For each intended use, specify what a successful result means and which failure modes matter most. A wrong result can have different consequences depending on whether it creates a lead, excludes a possibility, or influences a decision about a person. The evaluation plan should therefore make explicit which errors matter, how serious they are, and how the agency will detect them.
Define the expected operating conditions too: the kinds of cases and inputs, data quality, user roles, workflow, and foreseeable exceptions. NIST’s AI Risk Management Framework describes risk management across design, development, deployment, use, and testing. NAIAC emphasizes mapping the use and measuring performance in the field. Both approaches point to the same practical starting point: evaluate the system in relation to a particular use rather than treating a product label as its use case.
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Ask for documentation, then test the tool independently under controlled conditions. Vendor materials can help explain what the product is designed to do, but a vendor benchmark does not establish that it will perform adequately in a particular agency’s setting. NAIAC notes that sterile tests may differ from operational performance and that producer testing may not be independently verified.
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Request documentation from the provider
Before a pilot, request enough information to understand how the claimed results were obtained and what they do—and do not—show. Useful material includes:
- System documentation describing the intended function, inputs, outputs, dependencies, and known limitations.
- Test protocols, including how cases or examples were selected, what reference results were used, and how errors were counted.
- Information about the test data’s characteristics and its relevance to the proposed local use, including which groups and operating conditions were represented or absent.
- Performance results, failure cases, and any analysis of variation across relevant populations or circumstances.
- Information about independent testing or review, so the agency can distinguish external verification from the provider’s own assessment.
- Details needed to evaluate data handling, security responsibilities, retention, deletion, access, and any use of agency data for further training.
Ask the provider to explain limitations in terms investigators and reviewers can use. A number without its test population, method, reference standard, and conditions is not enough to establish local suitability. If a claim cannot be independently interpreted or checked, treat that as an unresolved evidence issue rather than assuming the claim applies.
Run a controlled, representative test
Design a field test around data that reflects the intended agency use and operating conditions. The test set should not be selected only because it makes the system look favorable. Decide how cases will be sampled, how correct results will be established, who will review disagreements, and how the test will preserve appropriate privacy and security safeguards.
Record the system version, configuration, input characteristics, user actions, and operating conditions. The point is not paperwork for its own sake: without these details, the agency may be unable to tell whether a later result came from a changed system, changed data, a different workflow, or random variation.
Where meaningful for the task, count false positives and false negatives separately and capture other failure modes. Review examples of both successful and unsuccessful outputs. Record whether the tool fails visibly, produces an apparently confident but wrong result, or cannot process an input. Examine how often users need to correct, repeat, or abandon the workflow. These observations help assess both the tool and the practical cost of relying on it.
Use an evaluation set distinct from any data used to tune or configure the system, where the design allows. The agency should be able to explain how the test cases relate to real use and whether the reference answers are sufficiently reliable. If the test does not resemble the agency’s cases, users, and conditions, a strong result may not generalize.
How can investigators tell whether a tool is accurate and reliable?
There is no single score that answers this question for every application. Accuracy means little without knowing what counts as a correct result and how errors are distributed. Reliability also concerns whether performance is consistent across cases, users, data conditions, and time.
Choose metrics that match the task
Define the outcome the tool is meant to produce and select measures that reveal the errors that matter. For a task where false positives and false negatives have different consequences, report them separately rather than hiding both inside one overall measure. Include failure-to-process cases and other recurring failure modes rather than excluding them from the denominator without explanation.
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For any reported measure, document its calculation, test population, reference method, and conditions. Explain what is included and excluded. Where uncertainty or confidence information is produced, determine whether that information has been tested and whether users can interpret it. Do not treat a confidence score as proof that a result is correct.
Look at distributions and examples, not just averages. A single overall score can mask very different error patterns among relevant groups, circumstances, or input qualities. Review cases near decision boundaries, unusual cases, and cases where the output conflicts with other information. Have qualified staff assess whether the observed outputs are usable for the narrowly defined purpose.
Assess whether results generalize
A vendor’s result or a result at another agency may not transfer to a local population, workflow, data environment, or task. Check how closely the test conditions match the intended use. If the agency plans to use the system with a population, case type, input quality, or operating condition not represented in testing, that is a limitation to resolve—not a reason to assume comparable performance.
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NIST’s AI Risk Management Framework Measure playbook warns that an unvalidated system, or one that fails validation, may be inaccurate or unreliable or may generalize poorly beyond its training data and settings. In practical terms, validation needs to be tied to the specific system configuration and intended conditions. Document what has been validated, what has not, and what evidence would justify extending the approved use.
How should an agency check for bias and harmful impacts?
Assess demographic performance and potential harms as part of validation, not as a separate issue to revisit after selection. Ask which populations and circumstances are represented in the training and evaluation data, which are missing or thinly represented, and how errors vary across relevant groups. The right comparisons depend on the particular tool and use; the agency should explain why the selected groups and conditions matter.
Do not infer fairness from an aggregate accuracy figure. A system may have an acceptable average while making more errors for a particular group or in a particular circumstance. Examine error rates and failure cases across relevant categories, and note where the test sample is too limited to support a conclusion.
Consider harms that can enter through records, proxies, or established investigative practices as well as through model behavior. A system may reproduce or amplify patterns in the material it receives even if that was not the intent of its designers. NIST’s 2022 publication, Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (SP 1270), discusses harmful impacts throughout technology processes, including unintended ones. That makes data provenance, missing groups, and the consequences of deployment part of the evaluation—not merely properties of the model.
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What privacy, security, and explainability questions belong in procurement?
Procurement should establish how information flows through the system and who is responsible for protecting it. Map what data the tool receives, where it is processed, who can access it, how long it is retained, whether it is shared, and whether it is used for further training. Specify deletion, access control, incident handling, and security responsibilities in procurement terms and operating policies rather than leaving them as informal expectations.
Ask what evidence supports an output and whether staff can inspect or reproduce the result sufficiently for the intended review. Investigators and supervisors need to understand what the system considered, what limitations apply, and what uncertainty remains. If the output cannot be meaningfully reviewed, the agency should restrict its role accordingly or decide it is unsuitable.
NIST’s AI Risk Management Framework identifies privacy, security and resilience, accountability and transparency, and explainability and interpretability among characteristics of trustworthy AI. Translate those principles into specific questions for each proposed use: what protections are in place, how access is logged, how a result can be audited, and how a user can recognize when an output should not be relied on.
How should human review work in practice?
Human oversight is meaningful only when people have the competence, time, authority, and information to assess an output. Train users on the tool’s capabilities, failure modes, limitations, and appropriate scope before they rely on it. Training should include how to recognize uncertainty, how to document a disagreement, and when to seek another qualified review.
Set out when the output must be corroborated and what counts as adequate corroboration for the task. A generated narrative or score should not silently become a substitute for independent assessment. Define how the result is communicated in reports and to later reviewers, including any known limitations relevant to interpretation.
The Administrative Conference of the United States (ACUS), in its guidance on using algorithmic tools in regulatory enforcement, recommends assessing whether tools devolve decision authority to automated systems and ensuring users understand how to assess outputs before relying on them. For an agency, that means asking not just whether a person technically clicks “approve,” but whether the procedure preserves a real opportunity to question the output.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should departments compare more than one tool?
Compare candidates on the same intended task, data, test protocol, and operating conditions. Otherwise, apparent differences may come from unlike tests rather than meaningful differences between systems. Use the same review rules and acceptance criteria for each candidate.
| Evaluation area | What to compare | Evidence to retain |
|---|---|---|
| Validity and reliability | Performance on representative data, repeatability, and limits on generalization. | Test protocol, data characteristics, results, limitations, and relevant version or configuration. |
| Error profile | False positives, false negatives, failure cases, and differences across relevant groups and conditions. | Disaggregated results where meaningful, case-level review, and documented gaps in evidence. |
| Operational fit | Workflow effects, data requirements, user burden, integration needs, and support for the defined role. | Observed task steps, time or effort required to use and review outputs, and operational constraints. |
| Explainability and reviewability | Whether users can inspect inputs and outputs, understand limitations or uncertainty, and review the basis for conclusions. | Sample outputs, review procedures, and records of how disagreements or errors are handled. |
| Privacy and security | Data access, retention, secondary use, safeguards, and incident response. | Data-flow description, written responsibilities, access and retention rules, and response procedures. |
| Governance | Human review, training, auditability, transparency, and corrective action. | Approved-use policy, assigned roles, training plan, audit procedure, and escalation criteria. |
| Total cost and demonstrated value | Costs of testing, training, oversight, integration, and monitoring as well as license costs; compare these with demonstrated operational value. | Agency-specific cost and workload estimates and the evidence supporting the claimed value. NAIAC identifies cost-effectiveness as an outcome to assess; the cited guidance provides no price benchmark. |
A candidate that performs well in a narrow test may still be a poor operational choice if it requires unsupported data, adds review burdens the agency cannot meet, or lacks workable governance. Conversely, a system should not be rejected or accepted on a broad feature claim when its demonstrated fit for the defined task is what matters.
What should forensic labs do differently?
Forensic analysis demands method-specific validation and competent examination. The DOJ’s December 3, 2024, final report advises forensic science service providers to validate tools before deployment using representative data and contexts, monitor them after deployment, assess the quality and representativeness of model-building data, and procure tools only when acceptable accuracy has been demonstrated.
Do not treat a general software demonstration as validation for a forensic method. Identify the specific analysis, evidence types, conditions, and examiner role the tool will support. Keep validation records tied to the version and configuration actually used, and define how an examiner documents the tool’s contribution and limitations in casework.
NIST’s November 21, 2022, Digital Investigation Techniques: A NIST Scientific Foundation Review notes that digital forensic methods have established computer-science foundations when appropriately applied, while also cautioning that not all evidence may be found, recovered deleted files may include extraneous material, and software revisions can change the meaning of artifacts. These cautions reinforce the need for examiner competence and method-specific validation; they do not establish that any particular AI product is validated.
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How should monitoring and governance continue after launch?
Approval should specify who owns the system after deployment and what would trigger a review. Document approved uses, validation evidence, responsible roles, audit procedures, incident escalation, and criteria for restricting or suspending use. Assign a person or team to monitor performance and ensure that reports reach someone empowered to act.
Continue measuring relevant performance and reviewing incidents, complaints, corrections, and cases in which outputs were disputed. Revisit validation when software, data, population, workflow, or intended use changes. A change may make old test results less relevant even if the product name remains the same. DOJ’s forensic recommendations call for post-deployment monitoring, while NIST’s risk-management materials emphasize measurement and monitoring over the AI lifecycle.
Set out what happens when results fall short: who investigates, whether use pauses, how affected records or cases are reviewed, and what evidence is required before resuming. A monitoring plan without an escalation route can identify a problem without containing it. The agency should preserve enough documentation to reconstruct which system and process produced an output.
What should an evaluation record contain?
A concise, auditable record makes decisions easier to explain and future reassessment possible. Include:
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- The defined task, approved users, inputs, outputs, prohibited uses, and decisions the tool may influence.
- The system version and configuration tested, the test protocol, data characteristics, reference method, and operating conditions.
- Measured results, error analysis, relevant group or circumstance comparisons, failure examples, and limitations or untested conditions.
- Privacy and security findings, data-handling terms, reviewer procedures, training requirements, and the accountable roles.
- The decision to proceed, narrow, defer, or reject use; its rationale; monitoring responsibilities; and revalidation or suspension triggers.
This record should distinguish observed evidence from assumptions and unresolved questions. If a use expands later, the agency can compare that proposed use with what was actually tested instead of treating the original approval as blanket authorization.
How to make the decision
Proceed only when the evidence supports the specific intended use, the remaining limitations are understood, and the agency can meet its obligations for human review, privacy, security, training, and monitoring. Narrow the use or collect additional evidence when the test is incomplete or the operating context differs materially from the validation conditions. Do not deploy a system for a consequential role merely because it is available or performs well on a vendor-selected benchmark.
There is no universal accuracy threshold supplied by the cited guidance for every investigative AI application. Acceptance criteria must be set for the task, the possible consequences, and the real operating conditions. For jurisdiction-specific obligations, agencies should verify current local legal and policy requirements.
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