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Cities should audit AI-enabled surveillance as a deployed system, not as a product label or a single accuracy score. A useful audit defines what the technology does and what decisions follow from it, tests the relevant error types under realistic operating conditions, examines results across demographic groups where lawful and feasible, checks data and operational controls, assesses privacy and rights impacts, and publishes findings with accountable corrective actions.
Facial recognition offers the clearest available example of demographic-performance testing, but “AI surveillance” also includes systems that detect, track, classify, or analyze people, vehicles, and events. There is no single accuracy measure that applies to all of them. The audit has to fit the task, the people affected, and the consequences of a wrong result.
Start by defining what is being audited
An audit begins with an inventory broad enough to capture the whole deployment. A camera may collect images, a separate analytics system may generate an alert, a database may supply reference records, and a staff member or another system may act on the result. Looking only at the algorithm can miss the source of an error or the point where a tentative match becomes a consequential decision.
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- The technology and AI functions involved, including connected cameras, analytics, databases, vendors, and downstream decision processes.
- The department or third party responsible, the stated purpose, locations and duration of use, and the people likely to be affected.
- What data is collected, purchased, or generated; who can access it; where it is stored; and with whom it is shared.
- The system’s claimed effectiveness measures, the version or configuration in use, and the actions staff are authorized or expected to take after an alert.
Portland Resolution 37608 is one municipal policy example: it directs work on a citywide surveillance-technology registry describing technology, purpose, responsible bureau or third party, and methods for assessing effectiveness. It also calls for annual reporting, oversight strategies, privacy impact assessment procedures, and public and equitable processes. That policy is a model of governance elements, not evidence that every city has adopted or implemented them.
Inventory boundaries matter. Include systems used by contractors on the city’s behalf and relevant data or services obtained from outside the department. Note when a component, data source, or operational step is unknown; an audit cannot reliably judge performance if the city cannot establish what was actually deployed.
Define the task, decision, and potential harm
Before measuring performance, state the task in plain language. Is the system detecting an event, tracking an object across video, classifying an image, verifying a claimed identity, or identifying an unknown person? What happens after a flag: a human review, closer monitoring, a stop, denial of a service, or another action? Record who makes that decision and what evidence they see.
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Build a harm model around both incorrect alerts and missed events. A false alert could lead to additional scrutiny, surveillance, a mistaken accusation, or another adverse action. A missed detection could also have consequences, depending on the system’s purpose. NIST emphasizes that the impact of an error is application-dependent: the same technical error rate does not imply the same level of public harm in every deployment.
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Write down the intended use, prohibited uses, escalation process, and any human review requirement before interpreting test results. If the actual workflow differs from the stated purpose—for example, if a tool intended to generate investigative leads is treated as proof—the audit should describe that gap as an operational finding.
Measure accuracy as separate, task-specific errors
For facial recognition, report false positives and false negatives separately. NIST defines a false positive as treating two different people as the same person; a false negative is failing to match two photos of the same person. Neither figure alone is a complete description of “accuracy.” The error rate, threshold, task, input images, and decision process all affect what the result means in practice.
A city’s performance report should identify the test population and conditions, the algorithm and version, the threshold used, image quality, and whether the task was one-to-one or one-to-many. Report the denominator and rate for each error type, not just a percentage described as “accurate.” Where data and applicable law permit, disaggregate results across relevant demographic groups and explain the limits of those comparisons. Averages can obscure materially different error patterns.
NIST’s 2018 Face Recognition Vendor Test (FRVT) evaluation, as described on its testimony page, covered 127 algorithms from 39 commercial developers and one university, tested using 26 million mugshot images of 12 million individuals. That figure describes the scope of that evaluation, not the expected performance of a city deployment. NIST’s 2019 demographic-effects evaluation covered 189 algorithms from 99 developers using four photo collections containing 18.27 million images of 8.49 million people.
In the 2019 evaluation, NIST found demographic differentials, but their size and direction varied across algorithms and applications. For one-to-one verification among the tested algorithms, false-positive rates across demographics often varied by factors of 10 to more than 100; false-negative differences often varied by factors below 3. These are findings from that evaluation, not predictions for every current product, configuration, population, or real-world use. NIST’s concise warning is that “Different algorithms perform differently.” A city should not infer that one vendor, product category, or evaluation result represents all systems.
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Compare independent evaluation evidence where available, but check whether its task and operating conditions resemble the city’s. A laboratory result on one image collection does not by itself establish performance on live video in a particular location. If a vendor or department makes an accuracy claim, ask for the method, test data, version, threshold, and error definitions behind it. If these are unavailable, mark the claim as unverified rather than treating it as a measured fact.
What the error measures mean in practice
| Measure | What it captures | Audit question |
|---|---|---|
| False positive | For facial recognition, two different people are treated as the same person. | How often does an incorrect match occur for this task, threshold, and population, and what action follows? |
| False negative | For facial recognition, two photos of the same person are not matched. | How often is a genuine match missed, and what consequences follow from a miss? |
| Group-level difference | Whether measured errors differ across demographic groups in the tested setting. | Which groups and conditions were included, what data limitations apply, and are results statistically meaningful for the intended use? |
These questions are specific to facial-recognition matching. Other surveillance tasks need measures suited to what the system claims to do; there is no universal metric established for every kind of AI surveillance.
Test the deployment conditions, not just a benchmark
Benchmarks can help compare algorithms, but they do not substitute for testing the system as configured and used by the city. The audit should identify the actual camera placement, image quality, lighting and environmental conditions, database, operating threshold, and staff workflow relevant to the deployment. Record changes to software, configuration, or data sources that could make an earlier result no longer representative.
For live facial recognition, a NIST-hosted Organization of Scientific Area Committees (OSAC) technical document describes performance metrics and how to measure them for its specified passive live facial-recognition concept of operations. It also discusses proportionality, human rights, privacy, and privacy by design. Its scope should not be expanded to every surveillance system or every live-recognition deployment; a city should check whether the described concept matches its own.
Where a test cannot reproduce real operating conditions, state the gap rather than implying the benchmark proves field performance. Document uncertainty, test limitations, and any restrictions needed while evidence is incomplete. A system that cannot be evaluated adequately may warrant a narrower use, additional safeguards, or a pause in deployment until the city can verify its claims.
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Audit reference data, records, and operational controls
Performance depends on more than the recognition algorithm. Review how reference records are created, who can add or change them, how long entries remain, and how errors are identified and corrected. A stale or inaccurate database can undermine an otherwise well-measured algorithm.
UK local-authority surveillance guidance, updated August 25, 2026, covers public-space CCTV, automatic number plate recognition (ANPR), facial recognition, body-worn video, drones, and vehicle-borne video. Its practical questions include whether recognition databases are accurate and current, regularly validated, and correctable; whether image records have integrity safeguards; and whether complete audit trails exist. These are useful audit prompts, but legal duties and local rules must be assessed for the city’s own jurisdiction.
Inspect the full record lifecycle and access controls. Establish what is retained, for how long, who can view or export it, how it is secured, and how deletion is carried out. Check whether system logs let reviewers reconstruct a search or alert: who initiated it, what data was used, which result appeared, what action followed, and whether anyone overrode or corrected the result. The audit should note missing logs or records that cannot be independently checked.
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A system can meet a performance target and still be unjustified or poorly controlled. The city should separately assess the purpose and necessity of the deployment, whether its scope is proportionate to that purpose, what data can be minimized, how long it is retained, who may access it, and how misuse can be detected and remedied. Identify foreseeable effects on people who are not suspected of wrongdoing as well as on those directly subject to an alert.
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Public participation is most useful early enough to shape whether and how a system is used, not merely to announce a decision already made. Include communities likely to bear the impacts in discussions about purpose, locations, safeguards, and acceptable risks. Portland Resolution 37608 calls for public participation and equitable processes in surveillance governance; it provides a policy example rather than proof of a particular outcome.
Use frameworks as guidance, then check binding rules
The NIST Artificial Intelligence Risk Management Framework (AI RMF) 1.0 is a voluntary framework intended to help organizations incorporate trustworthiness considerations into AI design, development, use, and evaluation. It can help a city organize risk identification and governance questions, but it is not itself a legal mandate. NIST Special Publication 1270, Towards a Standard for Identifying and Managing Bias in Artificial Intelligence, can inform questions about people, data, institutional context, and impacts. It emphasizes that harmful impacts can arise even without harmful intent.
Neither publication replaces legal analysis. The city must determine which binding laws, local policies, procurement terms, records obligations, and rules governing data and the specific use apply in its jurisdiction. The answer may depend on the department, technology, affected population, data source, and intended action. A framework can structure the review; it cannot establish that a deployment is lawful.
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If the city is choosing among systems or reviewing several deployments, compare them on the same task and, as far as possible, the same operating conditions. Do not rank one-to-one verification results against one-to-many identification results as if they measured the same capability. Use a comparison record that makes evidence and unknowns visible:
- False-positive and false-negative performance, reported separately and tied to the relevant task, threshold, test data, and conditions.
- Performance across relevant demographic groups, including the limitations of the available samples and comparisons.
- Image and data quality, database accuracy, validation frequency, correction procedures, and record lifecycle controls.
- The consequences of a false alert or missed event, including what human review can and cannot prevent.
- Privacy-by-design, retention, security, access, audit-trail, transparency, and public-participation safeguards.
- Independent evaluation evidence and whether the city can require, verify, and enforce corrective action.
Use “not established” when the city lacks evidence for a comparison rather than filling gaps with assumptions. NIST’s evaluations show why: tested algorithms differed, and demographic effects were not uniform. A product name or broad category is not a reliable substitute for system-specific evidence.
Publish findings and make corrections enforceable
A public report should let residents and decision-makers understand what was tested, what was not, and what the city will do about the findings. At minimum, publish the inventory entry, stated purpose, audit method, test population and limitations, group-level results where lawful and appropriate, data and operational findings, privacy and impact assessment, and unresolved questions. Explain how the system’s actual use compares with its authorized purpose.
Every corrective action should have an owner, deadline, and means of verification. Examples include correcting a reference database, changing access permissions, strengthening review procedures, restricting a use until testing is complete, or discontinuing a deployment that cannot meet its stated requirements. Public reporting without a mechanism to track completion does not show whether identified risks were addressed.
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Portland’s resolution offers a municipal example of inventory, effectiveness assessment, annual reporting to city council, oversight strategies, and privacy impact assessment procedures. Cities can adapt those elements to their own authority and processes; the resolution does not establish a universal requirement or a completed audit result for other jurisdictions.
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