AI in policing: Benefits and challenges

The Government’s policing reform White Paper presents artificial intelligence (AI) as a catalyst for transformation. Predictive analytics, live facial recognition, AI-enabled call handling and the creation of a national Police.AI hub are all positioned as potential solutions to rising demand and finite resources. While the ambition is clear and very desirable, the operational reality is much more complex.

There is considerable focus on high-profile issues such as whether police use of AI is lawful, and whether the technology works in this particular context. However, the most fundamental challenge is often overlooked, and that is whether it, like any other tool, can be onboarded and operationalised in a way that complements and strengthens the existing systems and work of officers rather than undermining them.

The risk of treating AI as authoritative

AI presents a cultural risk to a degree that earlier technologies did not.

Its outputs are delivered in fluent, confident language and it appears authoritative, creating a subtle danger. The Agentic Form of AI is especially dangerous in this regard because individuals can easily fall in the trap of endowing it with greater abilities than it has. Assuming that if because something appears to perform a task like a person, its performance is equivalent to that of a person, can be consequential. Officers may begin to treat it as more than a tool and dangerously come to over rely on it as the answer in most situations without having a defensible basis to do so.

AI systems can generate inaccurate material, reflect historic bias, and present probability as certainty. An output framed as reasoned and justified analysis may in fact be a statistical inference built on incomplete or flawed data or contain complete hallucinations.

Operational judgment must remain central, with properly scrutinised AI supporting as appropriate rather than replacing it. Recent events illustrate this point. Earlier this year it emerged that an AI-generated “hallucination” had contributed to intelligence used to justify banning Maccabi Tel Aviv supporters from attending a football match against Aston Villa. West Midlands Police later confirmed that Microsoft Copilot had produced inaccurate information, including reference to a match that had never taken place. Access to the tool was subsequently restricted within that force.

The issue was not that AI was used, nor that officers acted improperly. The concern was that the system’s output formed part of an operational document without sufficient understanding of its limitations as well as scrutiny of the deployment and use of the tool.

For policing, that distinction matters. When AI-generated material forms part of an intelligence rationale, a safeguarding decision or a public order operation, the consequences extend far beyond efficiency. They affect the quality of operational decision making and the level of confidence the public places in those decisions.

Those decisions also remain just as liable to legal challenge as they would be if made entirely the traditional way, the importance of testing and assuring the standards of the evidence and decision making involved remains unchanged. But officers may find in practice their ability to do so is more challenging when ‘black box’ systems are relied upon.

Rather than avoiding AI, officers must understand how it works, what its limitations are, and how its outputs should inform rather than dictate operational judgment. AI is, and should be treated as, a tool like any other. It is not a universal solution. In the same way that any tool can be used for immense public good, it can equally be used improperly to the detriment of all. An insufficiently cautious approach in adoption and utilisation will likely not only lead to immediate case specific consequences but also risks wider suspensions or restrictions on future use, meaning for one step forward, two may then be taken backwards.

A service built on legacy architecture

There is also the problem of infrastructure that supports decades of legacy data gathered under different standards and stored on incompatible systems. Records created under older governance frameworks will not seamlessly translate into machine learning environments. Data quality will vary, audit capability can be inconsistent and access controls often lack the granularity modern compliance or systems demand to work reliably (or perhaps at all).

Forces are attempting to build modern analytical capability on ageing and divergent digital foundations. New platforms promise tiered access and improved oversight, however, many existing systems were never designed with granular privacy controls or cross system compatibility in mind. Without that foundation, reliably introducing AI into operational workflows becomes far more difficult.

The real challenge is cleansing, structuring and governing historical data so that AI systems are working with reliable information.

That transition requires sustained investment and national coordination.

Live facial recognition and proportionality

The use case of live facial recognition brings these tensions into sharp focus.

Debate often centres on privacy intrusion, which is right and necessary. But the counterpoint must also be considered. Where forces are attempting to locate high-risk missing persons or individuals subject to arrest warrants, failure to use available technology may also carry risk and can mean a greater not lesser interference with privacy.

Paper-based image circulation is not inherently more protective of privacy, as physical photographs can be copied or misplaced without audit. By contrast, a properly governed digital system can create secure and logged access with defined retention parameters. There is also the question of volume, to find a wanted person in a crowd might require 100 officers to be given a comparison photograph, or a secure closed system be supplied with a single digital copy that may never be viewed if no match is identified.

Whether paper or digital, the guiding principle remains necessity and proportionality. The Court of Appeal made that clear in R (Bridges) v The Chief Constable of South Wales Police [2020] EWCA Civ 1058, the landmark case examining the lawful use of live facial recognition in policing.

The more pressing operational question is how forces ensure the technology strengthens operational judgment. That means clearly defined watchlist criteria, documented deployment policies, assessments, legal mandates, and rationales, with real-time human oversight and transparent review processes.

When those elements are in place, technology can enhance and exponentially transform the way officers analyse information and make decisions in fast moving operational contexts.

The unseen operational friction

AI transformation is often discussed in strategic terms with typically less attention given to friction within core systems and organisational operations.

Consider the circulation of an arrest warrant across forces. Delays in updates between systems can create conflicting records where an individual may be detained shortly after a warrant is withdrawn or is released because the reason to continue to detain is not received in time. These incidents are rarely the product of misconduct, they are usually the result of fragmented data flows.

AI has the potential to reduce such delays through automated reconciliation and real-time validation. But only if forces invest in interoperability and shared standards at the national level.

The greatest gains will come from coordinated reform of networks and information architecture.

AI literacy and meaningful human review

Policing is not an AI-native organisation. Generations of officers have worked within analogue or early digital environments, and AI literacy cannot be assumed.

If officers do not understand how a system generates its conclusions, meaningful human oversight becomes difficult to demonstrate. UK data protection law limits fully automated decision making that has legal or similarly significant effects without appropriate safeguards including the right to genuine human involvement.

In practice, this means officers understanding how AI reaches its outputs and how those outputs should be weighed alongside professional experience and operational judgment. Any human review must therefore be informed and active rather than symbolic. When used in this way, AI becomes a tool that strengthens operational decision-making rather than something officers feel compelled to follow or something they can simply defer to

Reform must be structured, not reactive

Many AI tools are being introduced to ease immediate operational pressure. Assistance with report drafting or call triage is understandably attractive to overstretched teams. But introducing advanced systems into fragile digital ecosystems risks compounding problems rather than solving them, unless real care and time is taken to adapt and tailor deployments rather than rush in.

True transformation requires strategic planning. It requires investment not only in procurement, but in systems architecture, interoperability and governance. It also requires consistent national standards so that forces are not adopting powerful tools in uneven and reactive ways.

The debate around AI in policing is often polarised and presents the technology as either a civil liberties threat or a cure for overstretched resources. Both positions oversimplify the issue.

AI is neither magic nor menace. It is a tool and, when used alongside professional expertise and experience, it can strengthen how police forces analyse information and make operational decisions.

The Government’s transformation agenda will succeed only if forces are given the time, infrastructure and training to integrate these systems effectively into policing practice.

The future of AI in policing will not be determined solely by what the technology can do. It will depend on how well policing combines it with the judgment, experience and operational instinct that remain at the heart of effective policing.

Thomas Barrett is a Partner and Data Protection and Privacy specialist at law firm Weightmans.