What is the Algorithmic Transparency Recording Standard?

It is a UK standard that asks public bodies to publish a structured record of algorithmic tools used in decisions affecting the public — describing the tool, the data, the human oversight, and the rationale. Higher-stakes uses are expected to publish a record.

How does the Public Sector Equality Duty apply to AI?

Public bodies must have due regard to eliminating discrimination and advancing equality. For AI that means testing for adverse impact across protected characteristics and documenting that you considered equality before and during deployment.

Does this tool store my answers?

No. The checklist runs entirely in your browser. Nothing you select or describe is uploaded or saved by the tool.

Public Sector AI Compliance Checker

Q: Is this official government guidance?

No. It is an educational checklist that summarises widely published UK public-sector AI principles such as the CDDO guidance, the Algorithmic Transparency Recording Standard, and the Public Sector Equality Duty. Always confirm current obligations with your legal and data-protection teams.

Public sector AI compliance checker

Deploying AI in government or public services carries obligations that private companies do not face: transparency to the public, the Public Sector Equality Duty, freedom-of-information exposure, and heightened expectations of human oversight. This checker walks you through those obligations as a structured checklist tailored to your use case and the stakes of the decision, then gives you a readiness summary you can take into a sign-off meeting.

How it works

You describe your department type, the AI use case, and how high the stakes are for affected individuals. The tool presents a checklist drawn from published UK public-sector AI principles — CDDO guidance, the Algorithmic Transparency Recording Standard, the Public Sector Equality Duty, data-protection (DPIA) duties, FOI implications, and human-in-the-loop requirements. Each item is weighted by your stated stakes, so a high-stakes benefits or enforcement decision surfaces stricter obligations than a low-stakes internal triage tool. As you tick items off you get a live readiness score and a list of the gaps that remain.

Tips and notes

Match rigour to stakes. A chatbot that drafts internal notes needs far less scrutiny than a model that influences a benefits or visa decision — proportionality is a principle, not a loophole.
Publish a transparency record for public-facing decisions. The Algorithmic Transparency Recording Standard expects higher-stakes tools to be documented and disclosed; assume your record may be requested under FOI.
Do the DPIA before, not after. A Data Protection Impact Assessment is a legal requirement for high-risk processing and is far cheaper to do up front.
Keep a human accountable. “The algorithm decided” is not a defensible position for a public body — name the human who owns the outcome.