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AI and algorithmic tools can affect public life without making the final decision: they can identify people, generate investigative leads, or monitor public spaces. Whether they work fairly depends not just on the model, but also on the data, conditions of use, safeguards, and ability to challenge errors. Evidence from UK policing and U.S. federal agencies shows why laboratory accuracy or a human decision-maker alone is not enough to establish that a system is safe or fair.
How is AI used in government?
In the documented cases here, the relevant tools support identification, investigation, or monitoring—not necessarily an automated decision about a person’s eligibility or guilt. That distinction matters: an officer or official may make the final call, yet an algorithmic match or alert can still shape where attention goes and what happens next.
- UK policing: The Centre for Data Ethics and Innovation (CDEI) describes South Wales Police’s trial of live facial recognition in public spaces. The Information Commissioner’s Office (ICO) says its 2026 report covers five consensual audits of police forces in England and Wales using overt facial recognition.
- U.S. federal facial recognition: The U.S. Commission on Civil Rights says the Department of Justice used facial recognition to generate investigative leads and describes biometric uses by the Department of Homeland Security (DHS).
- U.S. public-space monitoring: The Government Accountability Office (GAO) reviewed more than 20 types of detection, observation, and monitoring technologies used by DHS agencies in fiscal year 2023.
These examples do not establish how widely civic AI is used across all governments, or how often it produces biased results. They concern particular agencies, tasks, and jurisdictions; they should not be treated as representative of government systems worldwide. The ICO’s report page describes the scope and purpose of its audits, but not detailed findings, so it does not support claims about what those audits concluded. (ICO, August 18, 2026; U.S. Commission on Civil Rights, September 19, 2024; GAO, December 3, 2024)
How can bias enter a civic system?
A system’s performance is shaped by the whole chain of use, not just its code. A model may inherit patterns in the data it learns from, perform unevenly across people or settings, or be used in a way that gives an error serious consequences. The CDEI warns that algorithmic decision-making can carry historic bias in past decisions forward, including in policing and local government.
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Data and representation
If the data used to develop or assess a system do not adequately represent the people it will encounter, measured performance may not describe how it works for everyone. GAO notes that meaningful real-world biometric performance has been less extensively studied than laboratory performance, in part because of the difficulty of obtaining useful samples across demographic groups. An absence of complete evidence is not proof that a system is biased, but it also cannot establish that performance is equitable.
Deployment and consequences
Laboratory results do not settle how a biometric tool performs in actual conditions. A match used as an investigative lead may prompt further scrutiny; monitoring technology can extend observation into public space. The relevant questions include what happens after an alert, how officials verify it, and what a person can do if it is wrong. GAO’s 2024 biometric report summarizes evidence and stakeholder concerns; it does not provide a causal estimate of the technology’s effects on communities.
Institutional choices
Decisions about where to deploy a tool, which people or places to monitor, how much weight to give an output, and when to suspend use can determine who bears error and surveillance costs. Human review is meaningful only if reviewers can scrutinize the output, have authority to reject it, and are not encouraged to treat a machine-generated result as conclusive.
What does the evidence establish—and what does it not?
Keep four claims distinct: a documented disparity, a risk that a disparity may occur, a failure to assess that risk, and proof that a specific system produced discriminatory results. They are not interchangeable.
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| Evidence | What it supports | What it does not establish |
|---|---|---|
| GAO’s biometric identification review, April 22, 2024 | Real-world biometric performance is less extensively studied than laboratory performance; stakeholders raised concerns about bias, privacy, surveillance, opacity, and unequal effects. They also identified possible convenience and improved access to benefits and services. | A causal estimate of community-level effects, or a general rate of biased civic AI. |
| GAO’s DHS monitoring review, December 3, 2024, with a June 2025 status update | GAO found DHS procedures did not assess bias risk across all the reviewed monitoring technologies and recommended stronger policies. The recommendation remained open after DHS requested closure in June 2025; GAO continued to consider it meritorious. | That every technology reviewed was biased, or that every DHS use caused discriminatory outcomes. |
| CDEI’s account of the South Wales Police case | The Court of Appeal found the live facial-recognition trial unlawful in 2020 because the force had not taken reasonable steps to consider possible race- or sex-related bias. | That the court found this particular algorithm was biased. CDEI says there was no evidence that it was biased in that way. |
Sources: GAO, Biometric Identification Technologies; GAO, DHS monitoring technologies; CDEI, Review into bias in algorithmic decision-making.
What does the South Wales Police case teach?
On August 11, 2020, the Court of Appeal found South Wales Police’s live facial-recognition trial unlawful. As CDEI explains, the problem was that the force had not taken reasonable steps to establish whether the software might contain race- or sex-related bias, as required by the Public Sector Equality Duty. The finding concerned the force’s failure to consider the possibility adequately—not proof that the specific algorithm produced discriminatory results.
The practical lesson is that a public body’s duty to assess potential discriminatory impact is not replaced by a vendor’s assurances or by the absence of a proven disparity. Assessment must be part of deciding whether and how to deploy a system. The case is specific to its legal context in England and Wales; it does not by itself state the rules governing agencies in other jurisdictions. (CDEI review)
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should a public body assess a civic AI system?
The sources do not establish one official scoring standard for every system. But they point to a practical set of questions for procurement, deployment, and continuing oversight. A responsible assessment should record evidence and owners for each area, rather than treating an accuracy figure as a complete answer.
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- Define the task and consequence. State what the system does—such as identifying a person, generating a lead, or monitoring an area—and what officials may do in response. Explain how an error could affect an individual.
- Examine data coverage. Identify which people and conditions are represented in development and evaluation data, where evidence is missing, and whether demographic performance has been assessed. Do not infer equal performance from an overall average.
- Test actual-use conditions. Assess performance in the intended deployment setting, not only in a lab. Document how officials verify outputs and what happens when confidence is low or a result is disputed.
- Assess privacy and surveillance impact. Specify where, when, and whom the system observes; what information is retained or shared; and what limits govern access and reuse. The broader monitoring footprint matters even when no automated final decision is made.
- Provide transparency and contestability. Decide what notice people receive, what reasons can be given for an action influenced by a system, and how someone can challenge an error or seek human review.
- Name the accountable owners. Assign responsibility for ongoing audits, incident reporting, remediation, and deciding whether to pause use. Oversight must continue after procurement, as conditions and uses can change.
These questions synthesize concerns raised by GAO, the U.S. Commission on Civil Rights, and CDEI; they are a decision aid, not a single government-approved test. (GAO biometric report; U.S. Commission on Civil Rights; CDEI review)
Who is accountable when an algorithm affects a public decision?
The deploying public body remains central to accountability because it chooses the task, operating conditions, safeguards, and response to errors. A system provider may supply technical evidence, but that does not answer whether a particular public use is justified, lawful, or adequately monitored. Officials should be able to explain how an output was used and who has authority to correct harm or stop deployment.
Oversight gaps can persist even when a government has policies for some tools. GAO found DHS procedures did not assess bias risk across all the monitoring technologies it reviewed, and its recommendation for stronger policies remained open following the agency’s June 2025 closure request. That finding concerns the reviewed DHS processes; it is not a finding that every DHS technology produced biased outcomes. (GAO, December 3, 2024; status updated after June 2025)
U.S. Commission on Civil Rights Chair Rochelle Garza put the fairness obligation plainly: “As we work to develop AI policies, we must ensure that facial recognition technology is rigorously tested for fairness, and that any detected disparities across demographic groups are promptly addressed or suspend its use until the disparity has been addressed.” (U.S. Commission on Civil Rights, September 19, 2024)
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