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Police departments should buy an AI tool only after defining the specific policing problem, establishing that its use is lawful and preferable to alternatives, and setting measurable criteria for success. Then they should test the tool in representative local conditions before full deployment, examine errors and impacts across affected groups, and put privacy, security, human review, monitoring, and exit rights into the contract and operating policy.
A vendor’s accuracy claim is a proposition to verify, not a procurement result. The right product is the one that performs acceptably for a defined mission, under the department’s actual conditions, with risks and costs the agency can govern throughout its use.
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What should a department decide before it solicits police AI?
Start with the operational decision, not the product. The department should be able to explain what problem it is trying to solve, who may be affected, what decisions the system could influence, and why an AI-based approach is preferable to available alternatives. The federal definition of AI covers systems that make predictions, recommendations, or decisions that influence real or virtual environments; that breadth means a tool can matter even when a person, rather than software, makes the final decision.
Describe the proposed use precisely. “Improve investigations” is too broad to evaluate. A usable statement identifies the workflow, intended users, inputs, outputs, and the decision the output may inform. Classify the system’s role—for example, assistive, investigative, prioritizing, or rights-impacting—and state which uses are prohibited. A tool that helps sort records has a different decision impact from one that steers attention toward a person or location.
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Before a request for proposals, document:
- The defined problem and the people, communities, or records affected.
- The legal authority and purpose for collecting, processing, and acting on the relevant information, as assessed for the department’s jurisdiction.
- Alternatives, including changes to workflow or staffing, and why AI is justified over them.
- An accountable owner, plus named roles authorized to approve, monitor, suspend, or retire the system.
- Measurable outcomes, such as task performance, error consequences, workload, and cost, with thresholds and review dates set for this use case.
There is no universal accuracy threshold or procurement template for every policing application. The department must set the threshold, test conditions, retention rules, and disclosure approach for the particular tool and applicable law. A single overall accuracy figure is not an adequate success measure when different errors have different consequences.
Who should evaluate a proposed tool?
Use a cross-functional team, because procurement, technical performance, rights, records, and day-to-day operations are connected. DOJ guidance calls for agencies to identify why AI is preferable and establish oversight, workforce, monitoring, and evaluation structures; a vendor meeting led only by a technology buyer will not cover those responsibilities.
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Agree on a decision process before reviewing offers. Record who scores each criterion, how conflicts are handled, and which risks are disqualifying rather than compensable. A high score for speed or aggregate accuracy must not offset unacceptable security or rights risks.
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What should a police AI solicitation ask vendors to disclose?
Require specific, reviewable evidence rather than broad assurances such as “ethical,” “secure,” or “accurate.” The solicitation should ask for information the department can check independently and reserve time and access for testing before any full deployment.
- Purpose and boundaries: intended uses, prohibited uses, assumptions about users, and known failure modes.
- Evidence: training and validation populations, data provenance, evaluation methods, subgroup metrics, error definitions, and independent test results. Ask whether published results were independently verified and how closely test conditions resemble the department’s workflow.
- Data handling: what data the tool receives or creates, who owns it, how long it is retained, how deletion works, whether it may be reused for other purposes, and how subcontractors handle it.
- Technical operation: security architecture, access controls, audit logs, accessibility, interoperability, explainability, and the information available to trace relevant inputs, outputs, and decisions.
- Change management: model versioning, update frequency, advance notice of changes, how a change can affect performance, and what events trigger a new evaluation.
- Operational and financial assumptions: required staffing, training, integration, maintenance, support, pricing assumptions, and references for comparable use.
Ask vendors to identify which claims the department can verify, what access or documentation verification requires, and what evidence they cannot provide. Producer testing may not be independently verified, and real operating conditions can change a tool’s performance. Missing evidence should therefore be recorded as a limitation to resolve or a reason not to proceed—not silently treated as proof that the claim is true.
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Score risk and performance together against the stated mission. Use a weighted matrix, but mark any minimum legal, security, safety, or rights requirements as pass-or-fail gates. Do not let a strong result in one category hide an unacceptable failure in another.
| Evaluation area | What to examine |
|---|---|
| Mission performance | Performance against the current workflow or a defined baseline, not a generic demonstration. |
| Errors and calibration | False positives, false negatives, calibration, the costs of each error, and the conditions in which errors occur. |
| Subgroup outcomes | Whether performance or error patterns differ across relevant groups; use locally appropriate analyses and document limits in the data. |
| Rights, privacy, and security | Risks created by collection, access, inference, disclosure, use, and retention—not just the vendor’s security summary. |
| Explainability and auditability | Whether authorized reviewers can understand the output’s role and trace inputs, outputs, versions, and human actions. |
| Human factors | Whether operators can interpret limitations, challenge an output, override it, and use the system without undue workload or automation bias. |
| Integration and reversibility | Interoperability with existing systems, data portability, transition effort, and the practical ability to stop using the tool. |
| Lifecycle burden | Total costs and staff time for procurement, integration, training, monitoring, updates, support, and exit—not only the quoted purchase price. |
| Vendor accountability | Transparency, independent evidence, update control, audit access, incident cooperation, and enforceable contractual remedies. |
There is no meaningful universal ranking without a specific mission and local test. If several products remain viable, compare their field-test results under the same protocol and baseline; score the practical ability to audit, govern, and leave each system alongside its task performance.
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How can a department test accuracy and bias before full adoption?
Run a documented field test in representative local workflows before deployment. Set the protocol and success, fairness, safety, and stop criteria in advance so the department cannot move the goalposts after seeing results. NAIAC’s Law Enforcement Subcommittee recommends developer training, testing, and auditing for accuracy, nondiscrimination, respect for rights, and cost-effectiveness; its checklist addresses the MAP and MEASURE stages of the NIST AI Risk Management Framework and recommends making field-test plans and results public.
Design the test before seeing the results
- Set the comparator. Define the current process or another suitable baseline. Specify what counts as success and which outcomes matter to the mission.
- Choose representative conditions. Use representative local data and workflows where lawful. State which populations, cases, users, locations, and operating conditions the test does and does not cover.
- Set the sample and analyses. Establish sample size, subgroup analyses, error taxonomy, and the method for measuring accuracy, calibration, false positives, false negatives, workload, and cost. The appropriate sample and thresholds depend on the application; no universal values are established.
- Check safety and security. Evaluate access, data handling, security controls, and potential misuse as well as task performance. Include scenarios in which the system fails or provides uncertain output.
- Set stop rules and review. Define unacceptable results, escalation steps, and who can halt the test. Where feasible, have an independent evaluator review the methods and findings.
Keep results disaggregated enough to reveal material disparities, while explaining data limitations and protecting sensitive information. “Bias testing” is not one universal metric: choose analyses relevant to the system, affected people, and decision. Assess how errors affect people in practice, not just whether a summary score meets a vendor-selected target.
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What should the contract and operating policy require?
Make the contract enforceable and connect it to operating procedures. The agency should be able to learn what the system did, detect material changes, respond to incidents, and stop using it without losing control of its records or becoming unable to transition.
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- Data rights and handling: ownership, permitted processing, confidentiality, retention, deletion, restrictions on secondary use, subcontractor controls, and return of data at termination.
- Audit and records: access to logs and documentation sufficient to trace relevant inputs, outputs, model versions, and decisions; records-retention responsibilities should be explicit.
- Security and incidents: required controls, notification timelines, cooperation in investigation, and responsibilities for remediation.
- Model changes: advance notice of updates, version records, access to change information, and re-test triggers when a change could alter performance or risk.
- Performance and human oversight: thresholds, reporting, human review and override, escalation, approved users and uses, and the agency’s right to suspend operation.
- Liability and exit: allocation of liability and indemnity, termination and suspension rights, data return, transition assistance, and practical remedies for breach.
- Training and support: initial and continuing training, documentation of limitations, user support, and the division of responsibilities between vendor and department.
Operating procedures should limit a pilot to approved people and purposes. They should prohibit unapproved inferences or autonomous enforcement decisions, explain when a person must review and may override an output, and specify how to escalate uncertainty or suspected harm. Train everyone responsible for acquiring or using the system. The UK Home Office says responsible deployment requires lawful, ethical, transparent use, robust assessment before use, clear accountability, and clear operating procedures. Those are useful governance principles, but local law and policy determine the department’s actual obligations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should a pilot be monitored, and when should a tool be paused?
A pilot is a controlled evaluation, not a shortcut to permanent use. Limit users and use cases in writing, preserve human review, and monitor the tool against the pre-set measures while recording operational context.
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Track accuracy and drift, subgroup outcomes, complaints, incidents, overrides, operator workload, costs, and downstream effects. Set review dates and pause conditions before launch. Examples of triggers to define for the particular use include a material performance decline, a security or privacy incident, an unexplained change in subgroup outcomes, or a vendor update that has not been evaluated. The department should identify the person with authority to pause use and the evidence required to resume it.
At the end of the pilot, decide explicitly whether to reject, revise and retest, continue under limited conditions, or proceed to a separately approved deployment. Publish an understandable description of the system’s use and a summary of field-test results where legal, privacy, and security restrictions allow. Federal guidance reproduced in the U.S. Code compilation states that agencies should regularly test AI applications against the relevant principles; monitoring should therefore continue after purchase rather than end with vendor acceptance.
What policy examples are relevant outside the United States?
Procurement rules and funding differ by jurisdiction, so a U.S. department should not treat another country’s policy as its legal authority. Still, the UK Home Office’s October 22, 2026 factsheet is a current example of a government emphasizing lawful, ethical, transparent deployment, robust pre-use assessment, accountability, and operating procedures.
The Home Office reported more than £50 million in direct grants for police AI projects since the 2024 election and announced £115 million for police adoption of AI and automation. Those are UK government figures and policy commitments, not a cost estimate, a performance result, or a procurement entitlement for a particular department. In the United States, the Federation of American Scientists reported more than 18,000 law-enforcement agencies in 2026; that scale reinforces why local purpose, legal review, and operating conditions cannot be assumed to be identical across agencies.
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Frequently Asked Questions
Should a police department create an inventory of AI systems it already uses?
Yes. An inventory helps identify tools acquired through contracts, pilots, or embedded features that might otherwise escape oversight. Record each tool’s purpose, owner, users, data, decision role, vendor, model version, and review status, then reconcile the inventory with procurement and IT records.
Can a police department use a vendor’s public accuracy score as its test result?
No. A score is interpretable only with its test population, task, threshold, error definitions, and conditions. Ask for the underlying evaluation details and validate the claims against the department’s own intended workflow.
What if a vendor will not provide enough information to audit its system?
Treat the missing access as a material procurement risk, not as a routine confidentiality detail. The department can seek contractual access, limit the proposed use, or decline to proceed if it cannot verify performance and meet its oversight duties.
Does human review make an AI-assisted police decision safe by itself?
No. Review is meaningful only if the person has enough information, time, training, and authority to question or override the output. Procedures should also make clear who records that review and how concerns are escalated.
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