---
title: "UK Police AI Transparency Index: What 10 Forces Disclose"
description: "Original research scores public AI disclosure across 10 UK police forces, with a transparent method, source trail and downloadable evidence workbook."
url: https://artificiallyconfident.com/uk-police-ai-transparency-index-pilot/
date: 2026-08-19
modified: 2026-08-19
author: "Andy"
image: https://artificiallyconfident.com/wp-content/uploads/2026/08/uk-police-ai-transparency-index-pilot.png
categories: ["AI Governance", "Research"]
type: post
lang: en-US
---

# UK Police AI Transparency Index: What 10 Forces Disclose

**This is the first release in Artificially Confident Research: original, source-linked work designed to make consequential AI systems easier to inspect rather than merely easier to discuss.**

Artificially Confident Research · Pilot study · Evidence checked 19 August 2026

## Research disclosure

Study design
Purposive pilot of ten UK territorial police forces with publicly reported AI or algorithmic activity.

What is scored
Public disclosure quality for one reference use case per force—not legality, effectiveness or the force as a whole.

Method
Eight criteria scored 0–2 using public official sources, with a rationale and URL retained for every score.

Important limitation
“No public evidence found” does not mean the underlying governance activity was not performed.

[Download the complete research workbook (.xlsx)](https://artificiallyconfident.com/wp-content/uploads/2026/08/uk-police-ai-transparency-index-pilot.xlsx)

Police forces are adopting systems that classify messages, search faces, support risk assessment and help staff handle information. The public argument often jumps straight to whether those systems are accurate, biased or lawful.

There is a more basic question first: can a member of the public find out what a force is using, why it is using it, what role a human plays and how the system is checked?

Artificially Confident reviewed public information for ten UK territorial police forces. This was a small, purposive pilot rather than a national league table. We selected forces with publicly reported AI or algorithmic activity, chose one reference use case for each, and scored the quality of the disclosure — not the quality of the technology.

The result is more nuanced than “transparent” or “secretive”. Some forces publish genuinely useful operational records. Others publish responsible-sounding principles without enough tool-specific evidence to let the public test those claims. And the government’s national algorithmic transparency repository does not currently operate as an inventory of police AI.

## What we measured

We used eight equally weighted questions:

1. Is the information easy to find?
2. Does it explain the purpose and operational phase?
3. Does it explain the human role?
4. Is ownership or a governance route visible?
5. Are data and privacy controls explained?
6. Are risks, limitations, testing or impact assessments disclosed?
7. Are the supplier, system and technical limits described?
8. Are monitoring, results, review or challenge routes visible?

Each question scored zero, one or two. A two required clear, accessible and tool-specific public evidence. A one meant partial, general or fragmented information. A zero meant that we did not find relevant public evidence through the defined official-source search route.

That last distinction matters. “Not publicly disclosed” is not the same as “not done”. Police forces may have internal assessments that are not published, and operational security can justify withholding some detail. This research is about what the public can verify.

The full methodology, criterion-level rationales and source URLs are preserved in the research workbook.

## The pilot results

| Force | Reference use case | Score / 16 |
| --- | --- | --- |
| Essex Police | Live Facial Recognition | 16 |
| West Yorkshire Police | Live Facial Recognition | 16 |
| Metropolitan Police Service | Live Facial Recognition | 15 |
| Greater Manchester Police | Live Facial Recognition | 15 |
| Hampshire and Isle of Wight Constabulary | DARAT | 15 |
| Thames Valley Police | DARAT | 15 |
| South Wales Police | Operator Initiated Facial Recognition | 14 |
| West Midlands Police | Orlo Shield and Assist | 13 |
| Kent Police | Live Facial Recognition | 12 |
| Avon and Somerset Police | Internal generative AI, including Microsoft Copilot | 10 |

These numbers should not be read as a ranking of the forces themselves. They are scores for the public disclosure surrounding one selected use case on one date. Facial-recognition deployments have attracted exceptional legal, political and public scrutiny, so it is unsurprising that their documentation is often more developed than disclosure for administrative generative AI.

That is itself an important result: transparency appears to be driven by the visibility and controversy of a use case, not yet by a consistent force-wide publication system.

## What good disclosure looks like

The strongest pages behave less like public relations and more like operational records.

[Essex Police’s Live Facial Recognition hub](https://www.essex.police.uk/police-forces/essex-police/areas/essex-police/au/about-us/live-facial-recognition/) combines planned deployments, a downloadable deployment history, policies, impact assessments and independent research. It explains its operating threshold and discusses different interpretations of 2026 performance studies. That is unusually valuable because it lets a reader see that assurance is not always a single, frictionless answer.

[West Yorkshire Police](https://www.westyorkshire.police.uk/about-us/how-we-work/facial-recognition/live-facial-recognition) publishes upcoming and previous deployments, explains the watchlist and deletion process, names the software, describes where a trained operator intervenes and links to policy, impact and legal material.

[Greater Manchester Police](https://www.gmp.police.uk/police-forces/greater-manchester-police/areas/greater-manchester-force-content/au/about-us/live-facial-recognition/) similarly explains the human decision point, data deletion, operating contexts and supplier, with linked impact and legal documents.

The [Metropolitan Police facial-recognition hub](https://www.met.police.uk/police-forces/metropolitan-police/areas/about-us/about-the-met/facial-recognition-technology/) distinguishes live, retrospective and operator-initiated systems, provides multi-year deployment records and links policy, data-protection, equality and system-performance material.

These disclosures are not proof that every deployment is correct. They are evidence that members of the public have something concrete to interrogate.

## The oldest national records are still informative — and visibly stale

The UK government’s [Algorithmic Transparency Recording Standard repository](https://www.gov.uk/algorithmic-transparency-records) contained 64 UK records at the evidence cut-off. Only two police organisations appeared in its organisation filter: Hampshire and Thames Valley Police jointly, and West Midlands Police.

The joint [DARAT record](https://www.gov.uk/algorithmic-transparency-records/hampshire-and-thames-valley-police-darat) is detailed. It identifies the team, senior responsible owner and developer; describes intended decision pathways; and publishes an extensive set of risks involving bias, fairness, missing data, model drift, feedback loops and system failure.

But the record describes a pre-deployment system and dates from the early ATRS pilot. The other police record, [West Midlands Police’s exploratory analysis of sexual convictions](https://www.gov.uk/algorithmic-transparency-records/west-midlands-police-exploratory-analysis-of-sexual-convictions), concerns a one-off analysis that is now retired.

This means the repository is useful as a disclosure format but not as a current map of police AI. A member of the public cannot use it to answer the simple inventory question: which operational AI systems are police forces using today?

That is not a breach of the current ATRS mandate. The government’s [scope policy](https://www.gov.uk/government/publications/algorithmic-transparency-recording-standard-mandatory-scope-and-exemptions-policy/algorithmic-transparency-recording-standard-atrs-mandatory-scope-and-exemptions-policy) makes the standard mandatory for specified central-government bodies, while recommending it across the broader public sector. Police forces are operationally independent and are not currently required to publish ATRS records.

The fair conclusion is not that forces are non-compliant. It is that the public lacks a consistent, current and central police AI inventory.

## General principles are useful, but they are not evidence of implementation

[Avon and Somerset Police](https://www.avonandsomerset.police.uk/about/freedom-of-information-and-publication-scheme/our-use-of-artificial-intelligence/) publishes a clear AI principles page. It says AI is subject to governance, impact assessment, legal and ethical review, monitoring and audit. It also states that generative-AI outputs must be checked and that tools such as Microsoft Copilot support internal productivity rather than autonomous operational decisions.

Those are sensible commitments. The transparency gap is that the page does not provide an inventory, deployment dates, named owners, linked tool-level assessments, test results or monitoring outcomes. Readers are told that controls exist, but are given limited evidence with which to examine how those controls worked for a particular system.

West Midlands Police provides a stronger tool-specific explanation for [Orlo Shield and Assist](https://www.westmidlands.police.uk/police-forces/west-midlands-police/areas/about-us/about-us/how-we-use-artificial-intelligence-support-tools-to-help-give-you-the-best-service-possible). It names the supplier, describes message sorting, moderation, drafting, summaries and image indicators, and repeatedly identifies the human review point. The remaining gap is assurance evidence: no tool-specific impact assessment, quantified testing, review date or results report is linked from the disclosure.

This difference is central to PolicyOps thinking. A policy statement says what should happen. An operational record shows what was decided, by whom, using which evidence, with what limits, and what happened next.

## The missing object is a decision record

The pilot suggests that police AI transparency does not primarily need more high-level principles. The National Police Chiefs’ Council has already endorsed a [Covenant for Using Artificial Intelligence in Policing](https://science.police.uk/news-and-events/resources/covenant-for-using-artificial-intelligence-ai-in-policing/), placing transparency, fairness and public confidence at the centre of the approach.

The missing object is a maintained decision record for each material system.

A useful record would say:

- what the system is and which operational phase it is in;
- the decision or workflow it influences;
- what a human must review and what they can override;
- who owns the deployment decision;
- which data sources are used and how long data is retained;
- what errors, bias and misuse risks were tested;
- which supplier and model version are in use;
- what thresholds or meaningful settings apply;
- what monitoring has found since deployment;
- when the record was last reviewed; and
- how a person can ask questions, complain or challenge an outcome.

Sensitive operational details can be withheld or generalised. The government’s ATRS policy already recognises exemptions and the need to avoid harmful disclosure. But a security exception should be a reasoned field in a record, not a substitute for the record itself.

This is where a PolicyOps approach becomes practical. The disclosure should be generated from the same governed workflow that approves, reviews and changes the system. Publication then becomes an output of operational governance, rather than an occasional communications exercise assembled after public pressure.

## What should happen next

This pilot is deliberately small. The next version should expand to all territorial forces, use a pre-registered search protocol, add a second reviewer for a sample of scores and publish a correction log. It should also distinguish three separate measures:

1. inventory coverage — how many known systems have a public record;
2. record quality — how complete each disclosure is; and
3. record freshness — whether the disclosure reflects the current operational system.

The most important of those may be freshness. A beautifully detailed pre-deployment record can become misleading if it is never updated after the system changes, launches or retires.

Police use of AI will remain contested. Better transparency will not resolve every disagreement, and it should not be treated as automatic legitimacy. It does something more basic and necessary: it gives the public, oversight bodies and police leaders a shared record of what is actually being operated.

That is the point at which debate can move from slogans to evidence.

## Research note

This article reports a purposive ten-force pilot using public official sources checked on 19 August 2026. Scores assess the disclosure for one reference use case per force. They do not assess legality, effectiveness or the totality of a force’s AI use. “No public evidence found” does not mean an activity was not performed. The complete score rationales, source links and methodology are retained in the accompanying research workbook.

## Method, corrections and reuse

The workbook contains the scoring rubric, all 80 criterion-level rationales, official source links, evidence dates, confidence flags, limitations and reproduction instructions. This page is a dated research record. Material corrections will be logged rather than silently substituted.

For questions, corrections or evidence we may have missed, use the [contact page](https://artificiallyconfident.com/contact/). Our wider publication standards are explained in the [editorial method](https://artificiallyconfident.com/editorial-method/).