AI in policing: uses, risks and public trust

Why this matters for police officers and staff

Police work produces a great deal of paperwork, records and communication. AI tools can help with some of it. You will also meet AI from the other side: in complaints that include faked recordings, in scams reported by the public and in debates about surveillance technology.

Policing is different from most government work because it involves powers over people's liberty, privacy and safety. Mistakes can lead to wrongful suspicion, wrongful arrest or the loss of evidence that a victim depended on. Police work also relies on public cooperation and that depends on people believing the police act lawfully and fairly. Any use of AI that weakens that belief can make policing harder, not easier. This module sets out the main uses, the main risks and the principles for the rest of the pathway.

How AI is discussed in policing

Around the world, AI in policing is discussed in roughly three groups. This is a general picture, not a description of what any force in Pakistan does.

• Administrative support. Drafting reports and letters, summarising long documents, organising rosters, translating and answering routine public queries. This is the lowest risk if case data is kept out of public tools (module 4).
• Investigation support. Searching large amounts of lawfully obtained digital material, transcribing recordings, spotting links in data or enhancing images. Useful, but the output can be wrong and must be verified before it is relied on.
• Surveillance and prediction. Computer vision such as facial recognition (module 2) and tools that try to predict where crime will happen or who may offend. These carry the highest risks to rights and are the most contested.

The main risks

• Wrong output treated as fact. AI can produce hallucinations, mistranscribe a recording or mismatch a face. In policing, an error can attach suspicion to an innocent person.
• Bias. Tools trained on past records can repeat past patterns. If some areas or groups were policed more heavily before, a tool may direct even more attention there. This is bias built into the data.
• Privacy. Collecting and analysing data about people who are not suspected of anything is a serious intrusion and needs a clear legal basis.
• Evidence integrity. Material processed by AI tools may be altered and fake material may be presented as real (module 3).
• Data leaks. Case details pasted into public AI tools can reach people they should never reach, including suspects.
• Over-trust. Automation bias makes people accept a confident machine answer too quickly, especially under pressure.

Why public trust matters

People report crime, give witness statements and accept police instructions partly because they trust the police. Technology that people see as secret, unfair or unaccountable can damage that trust quickly, even if it was introduced with good intentions. Transparency about what tools are used for, clear accountability for decisions and routes to complain and seek redress all help protect it.

Principles for this pathway

1. Lawful authority first. Any use of technology that affects people's rights needs a clear legal basis. If you are unsure, ask your legal adviser before acting.
2. Humans decide. Arrest, charge, use of force and evidential judgements are made by authorised people, not tools.
3. Verify before relying. Treat AI output as a lead to check, never as proof.
4. Protect case data. No victim, witness, suspect or case details in public AI tools.
5. Record and explain. Be able to say what a tool did and why you acted as you did.

Try it

Find a published news article about AI in policing in any country. Using the three groups above, decide which group the use falls into. List the risks from this module that apply and the questions you would want answered before such a tool was used.

Check yourself

1. What are the three broad groups of AI use in policing?
2. Why can a tool trained on past police records be biased?
3. Why does public trust matter when police use AI?

Answers: (1) Administrative support, investigation support, surveillance and prediction. (2) It can repeat past patterns, so areas or groups policed more heavily before may receive even more attention. (3) Policing depends on public cooperation and technology seen as secret or unfair can damage the trust that cooperation relies on.