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AI-assisted policing should be governed by a clear legal basis, tightly limited purposes, testing before and after deployment, accountable human review, traceable decisions, and meaningful ways for affected people to seek oversight or challenge errors. An AI result should not, by itself, justify an arrest or another consequential decision. The exact legal duties depend on the jurisdiction and the use: EU law prohibits certain practices and classifies specified permitted law-enforcement applications as high-risk, while U.S. Department of Justice guidance recommends controls for criminal-justice agencies without creating one binding rule for every police department.
Start with authority, purpose, and a boundary around use
Before acquiring or using an AI system, a police agency should be able to answer three questions: what legal authority permits this use, what specific task is the system meant to support, and what decisions must remain outside its role? A broad label such as “crime prevention” is not an adequate purpose statement. A defined purpose might be to organize a particular body of records for an investigator, or to assist with a specified identification task under a stated legal process. The system should not silently migrate from that purpose into a different one.
A use policy should identify the people, data, locations, decisions, and circumstances covered. It should also state what is prohibited, who may access the system, what records may be entered, and whether output can be shared or reused. Those boundaries make it possible to assess whether the use is lawful and suitable before deployment—and to detect later expansion that was never evaluated.
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AI systems can support very different activities: finding patterns in records, generating investigative leads, assessing a defined risk, or helping interpret evidence. These are not interchangeable. The agency should describe whether a system produces a lead, a recommendation, a classification, or an identification candidate, and specify who is authorized to act on it. “Decision support” should not become a way to obscure that an output is influencing a consequential decision.
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In particular, an AI-generated match or risk score is not the same thing as independently established grounds for action. A policy should name the decisions for which system output may be considered, those for which additional evidence or verification is required, and those for which the output cannot be used as the sole basis.
Apply the right jurisdiction’s rules
There is no single global AI-policing rulebook. An agency must check the law and policy applicable to its jurisdiction, the specific technology, and the particular decision. The European Union’s AI Act establishes prohibitions and requirements for specified categories of AI use. In the United States, the Department of Justice’s Artificial Intelligence and Criminal Justice, Final Report, dated 3 December 2024, gives recommendations for criminal-justice entities; it is not a universal statute binding every state and local police department.
That distinction matters in practice. A category being addressed by a regulatory framework does not itself give an agency permission to use it. Nor does federal guidance automatically replace applicable state, local, or other legal requirements. Agencies should record the legal analysis for each use and revisit it when the system, purpose, or governing rules change.
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What EU law says about policing uses
The EU AI Act uses a risk-based structure: some practices are prohibited, while certain other applications are treated as high-risk when their use is otherwise permitted under relevant EU or Member State law. The classification is not an authorization. A high-risk label does not make a use lawful if other law prohibits it.
The European Commission’s enforcement summary, last updated 6 October 2026, says that prohibitions on practices deemed unacceptable apply from 2 August 2026. The same summary says the general rules for high-risk systems listed in Annex III apply from 2 December 2027. These are different stages, not one combined start date. The Commission’s dates should be checked against the applicable law and any amendments when an agency plans or reviews a deployment.
Specific prohibited practices are not a ban on every predictive use
The cited EU prohibition includes individual criminal-offence risk assessment or prediction based solely on profiling. That is narrower than saying that every form of predictive policing is prohibited. Other systems may be treated differently, or may be restricted under other law. The proper question is what the particular system predicts, about whom, using which information, and for what action—not whether it has been given a broad “predictive policing” label.
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The Act also prohibits AI systems that create or expand facial-recognition databases through untargeted scraping of facial images from the internet or CCTV. That specific prohibition should not be confused with every use of facial recognition; biometric identification by law enforcement is subject to distinct conditions and applicable law.
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High-risk applications remain conditional
Examples of law-enforcement applications treated as high-risk, insofar as they are permitted under relevant EU or Member State law, include certain systems assessing the risk that a person will become a crime victim, evaluating the reliability of evidence in criminal proceedings, and specified profiling used to detect, investigate, or prosecute crime. The classification means that safeguards apply to a covered system; it does not establish that any particular deployment is necessary, proportionate, or otherwise lawful.
For law-enforcement biometric identification, the AI Act refers to the Law Enforcement Directive’s condition that processing be strictly necessary, protected by appropriate safeguards, and authorized by EU or Member State law. The directive’s principles include lawfulness, fairness and transparency, purpose limitation, accuracy, and storage limitation. Agencies need to assess the relevant legal conditions for their own use rather than treating technical capability as authority.
High-risk controls should be planned, not bolted on
The European Commission’s overview describes high-risk requirements that include risk management, data quality, logging, documentation, human oversight, robustness, cybersecurity, and accuracy. A public agency preparing a covered use should make these part of procurement, testing, training, and operational review. Waiting until a system is already embedded in daily work makes it harder to establish what data it relied on, whether controls work, and who is responsible for decisions.
Test the system in the setting where it will be used
A vendor’s general performance statement is not a substitute for an agency’s assessment of the actual use. Before deployment, the agency should document the data the system uses, the population and operating context for which it was evaluated, the validation methods and metrics, known limits, and the consequences of likely errors. The U.S. DOJ’s 2024 report recommends that agency policies address training data, validation metrics and processes, monitoring frequency, and system-specific risks.
Check the data and the conditions behind the result
Data quality is more than whether a file can be loaded. Review whether the inputs are relevant to the stated task, sufficiently accurate, and appropriate for the people and circumstances in scope. Record important gaps and limitations. If the system depends on a particular type of image, record, or other input, establish how missing, outdated, or inconsistent inputs affect its output.
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Validation should reflect operational conditions rather than an abstract demonstration. The agency should state what it measured, how the test set relates to the intended deployment, and what the results do—and do not—show. The available legal and guidance material does not establish a general accuracy rate, bias rate, or crime-reduction effect for AI policing. Agencies should not imply that a system has proven such an outcome unless they have relevant, substantiated evidence for that specific use.
Assess both kinds of error and their consequences
A false positive can focus suspicion on the wrong person, produce an unwarranted investigative step, or contribute to a liberty-affecting action. A false negative can fail to identify a relevant lead or miss a possible victim-support need. Which error matters most depends on the task, but both should be considered. A single overall accuracy number, where one exists, may not explain how the system behaves for different inputs or in the agency’s actual operating conditions.
Assess discriminatory effects and rights impacts alongside technical performance. The purpose is not simply to ask whether an output is often correct, but whether the way the tool is used can unfairly affect people, intrude on privacy, or make an error difficult to detect or contest. The assessment should lead to operational limits, additional verification, or a decision not to deploy—not just a report stored away from the people making decisions.
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Testing does not end at launch. The DOJ report recommends that policies specify monitoring frequency and be revisited as technology, best practices, and legal requirements change. An agency should set a review schedule and identify events that prompt an earlier review, such as a changed model or data source, a new task, a material change in operating conditions, a pattern of disputed results, or a security incident.
Monitoring should be able to answer whether the system is still being used within its approved purpose, whether its inputs and outputs remain suitable, and whether known limitations are appearing in practice. If the agency cannot establish that a use remains safe and lawful, it should have a clear route to restrict or pause that use while it investigates.
Make human review meaningful
Human oversight is not achieved by having a person click “approve” after seeing an AI result. A reviewer needs to understand what the system can and cannot establish, have access to information needed to check the output, and be able to reject it without pressure to defer to an automated recommendation. The agency should define who performs the review, what verification is required, and what records show that the reviewer exercised independent judgment.
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The DOJ recommends that AI outputs be reviewed and verified by a human, and that output not be the sole basis for a high-impact decision. Its examples include cause for arrest and sentence length. The operational details—such as required training, access to underlying evidence, time to review, and authority to override—are ways an agency can make that recommendation effective; they should not be presented as if every one were a separately quoted DOJ rule.
Match verification to the stakes
For a low-consequence administrative task, a review may focus on whether information was transferred correctly. When output could affect a person’s liberty, privacy, or treatment in a criminal proceeding, the agency should require more rigorous independent verification and a clear evidentiary basis beyond the AI output. A system’s confidence score, candidate list, or risk label should not be treated as proof merely because it appears precise.
Reviewers should know when the system is unsuitable—for example, when inputs fall outside the conditions it was evaluated for—and have a practical way to stop reliance on the result. Training should cover the system’s intended purpose, documented limitations, and the agency’s rules for verification and escalation. A reviewer who lacks the expertise or authority to question an output cannot provide meaningful oversight.
Keep decisions traceable and open to challenge
Accountability depends on records that let an agency reconstruct how a system was used and how a consequential decision was reached. The EU AI Act’s high-risk framework includes logging and documentation among its controls. More generally, a responsible agency policy should determine what to record for each use, how long records are retained under applicable law, who may inspect them, and how they support oversight.
For a consequential decision, the record should make it possible to distinguish the system’s contribution from the human decision-maker’s reasoning. It should capture enough about the relevant output, the verification performed, and the grounds for the action to permit meaningful review. Retention and access rules should be defined alongside the use, rather than improvised after a dispute arises.
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People affected by an AI-assisted decision need a practical way to raise a concern and have it considered by someone with authority to investigate. An agency should identify who receives complaints or review requests, what information can be examined, how a disputed output can be checked, and how the agency corrects the record or changes its action when appropriate. The precise disclosure and remedy duties depend on applicable law, but a system that cannot be meaningfully questioned creates an accountability gap.
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Oversight access should be designed for legitimate reviewers, including those responsible for internal controls and any appropriate external review. Sensitive operational details may require protection, but that should be narrowly tailored: secrecy should not erase the records needed to determine whether a use was authorized, a result was verified, or a person’s rights were affected.
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Public-facing information should explain, in plain language, what the system is for, the general kinds of data it uses, what role its output plays, and what safeguards or review routes apply. Where feasible and consistent with law, agencies should engage the public and relevant stakeholders before or during deployment. Consultation can surface concerns about context and effects that a technical evaluation alone will not resolve.
The DOJ report recommends public engagement where feasible and plain-language disclosures that do not reveal sensitive operational or law-enforcement information. An agency therefore needs to balance intelligible explanation with narrowly tailored protection of operational details. It can communicate purpose, boundaries, and accountability arrangements without disclosing information that would compromise a sensitive operation.
Notice should also be useful to people who need to understand a particular decision. General public information about a tool does not automatically explain how it affected an individual case. Agencies should determine what case-specific information can be disclosed under applicable law and what route exists to request review.
Compare safeguards by the decision at stake
The same safeguards do not carry the same weight for every task. Use this comparison to identify the questions an agency should resolve before relying on a system; it is not a substitute for a jurisdiction-specific legal determination.
| Application type | Main risk to examine | Core control questions |
|---|---|---|
| Investigative lead generation | A lead may be mistaken for evidence or become a basis for escalating scrutiny. | Is the purpose narrowly defined? What independent information must an investigator verify before acting? Can reviewers see the limitations and record why a lead was pursued? |
| Risk assessment, including victim-risk assessment | A label can influence attention, intervention, or treatment of an individual. | Is this use lawful in the jurisdiction? What data and population were evaluated? How are errors and discriminatory effects assessed, and what review is available? |
| Biometric identification | An incorrect identification can focus police action on the wrong person; biometric processing also raises significant rights and privacy questions. | What legal authority and necessity conditions apply? Is the result independently verified? Are access, purpose, retention, and oversight limited and documented? |
| Evidence reliability or profiling in criminal proceedings | An output may affect how evidence is interpreted or how a person is treated in a prosecution. | Is the application covered by applicable high-risk requirements? Can a reviewer evaluate its limitations and the underlying basis? Can the use be effectively scrutinized or contested? |
| High-impact decision, such as cause for arrest or sentence length | An AI output could improperly substitute for individual judgment and an adequate evidentiary basis. | Has a trained human independently reviewed and verified the output? Is the output prevented from serving as the sole basis? Is the actual basis for the decision documented? |
The EU examples in this table describe categories identified in the AI Act or Commission guidance only insofar as the relevant use is otherwise permitted. The table is an operational comparison, not a claim that all uses in a row have the same legal status everywhere.
A practical governance sequence for an agency
A workable program puts legal review, technical evaluation, operational limits, and accountability in one lifecycle. The sequence below turns those safeguards into decisions an agency can document and revisit.
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- Confirm authority. Identify the applicable legal basis and restrictions for that jurisdiction and application. Distinguish binding requirements from guidance and internal policy.
- Assess rights and risks. Examine privacy, civil-rights and discriminatory effects, error consequences, and whether people can understand or challenge a result. Decide whether risks can be controlled or whether the use should not proceed.
- Validate for the intended context. Document data, methods, metrics, limitations, and failure modes. Test whether the evaluation supports the actual operating setting, rather than relying only on broad vendor claims.
- Set operating controls. Name authorized users, required verification, decisions that cannot rest solely on AI output, training, escalation routes, and records to be kept.
- Explain and engage. Provide plain-language information about purpose and relevant data practices, and seek public or stakeholder input where feasible and consistent with law.
- Monitor and reassess. Set review frequency and triggers for reassessment. Update or suspend the use when its purpose, system, inputs, operating conditions, evidence, or legal context changes materially.
This sequence should be reflected in a policy that names owners for legal review, technical evaluation, operational approval, records, and monitoring. The DOJ report specifically recommends human-centered policies covering permitted and prohibited uses, training data, validation metrics and processes, monitoring frequency, and system-specific risks. Policies should be revisited as technology, best practices, and legal requirements change.
What a defensible policy should leave on record
- Authority and scope: the legal basis, approved purpose, permitted users, prohibited uses, and decisions the system may or may not support.
- Evidence and limits: input data, validation approach, relevant performance findings, known limitations, and the context in which those findings apply.
- Human responsibility: reviewer roles, training, verification expectations, authority to reject output, and rules for high-impact decisions.
- Accountability: logs and documentation, access and retention rules, monitoring responsibilities, complaint or review routes, and processes for correcting errors.
- Public explanation: the information shared with the public and affected people, and the limits on disclosure needed to protect sensitive operations.
If an agency cannot explain the authority for a use, establish what the system is fit to do, identify who checks its output, or reconstruct how it affected a decision, it lacks essential conditions for accountable deployment. The appropriate response is to resolve those gaps before relying on the system in consequential policing.
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