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Keep a risk-based record that connects an AI system’s approved purpose to the decision it actually informed and the human review that followed. Someone examining the record should be able to tell what the system was meant to do, who approved that use, what evidence and limitations were considered, what output was used, what a reviewer did, and how an affected person can seek intervention or challenge a decision where applicable. The specific legal duties and retention period depend on jurisdiction, system classification, sector, and decision context.
What should an AI decision record establish?
A useful record is more than a model card, approval email, or system log on its own. It connects governance, evidence, and what happened in an actual case. The ICO recommends documenting the stages behind the design and deployment of an AI decision-support system so an organization can explain how it made a decision; the documentation should be usable by readers with different technical backgrounds. See the ICO’s documentation guidance.
- Purpose and role: What task was approved, for whom, and whether AI recommends, ranks, supplies information to a decision-maker, or makes a decision automatically.
- Authority and rationale: Who approved the use, who owns it, what risks and evidence informed the decision, and what conditions apply.
- Operation: Which system and relevant configuration were used, what output was considered, and what the human reviewer did with it.
- Accountability over time: How the organization monitors performance and problems, handles challenges, and retrieves records for an explanation, audit, or appeal.
Scale the depth of the record to the possible impact. A low-impact recommendation generally needs less case-level documentation than a system that informs recruitment or another consequential decision. The ICO’s guidance discusses risk-based documentation and accountability in contexts where data protection rules apply; it does not establish one universal form for every organization or AI use.
First decide what role AI has in the decision
Write down whether the system provides support to a person or makes a solely automated decision. That distinction affects the controls and explanations an organization may need, including under applicable data-protection rules. Do not rely on a label such as “decision support”: describe what users actually do with the output, whether they can meaningfully disagree, and who has final authority. The ICO recommends making this distinction clear when assessing individual rights and automated decision-making safeguards in its individual-rights guidance.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallRecord the intended use and boundaries before deployment, then revisit them when the system, workflow, user group, or decision context changes. A system approved to summarize information for staff should not silently expand into ranking people or determining outcomes without a new assessment and approval.
A practical template for the record
The following fields are a practical starting point, not a universally prescribed legal form. Tailor them to the applicable legal, sector, contractual, privacy, and records-management requirements. Use a central use-case record for governance and link case-level records to it where the decision’s risk and applicable rules justify doing so.
1. Identify the use and accountable people
- Record ID, business owner, creation date, and date of last review.
- System and provider; deployment, model, or configuration version when known.
- Intended purpose, operational domain, user roles, affected people, and intended decision recipient.
- System role: recommendation, ranking, generated information, or automated decision; identify the human or organization responsible for the final outcome.
- Out-of-scope or prohibited uses, material assumptions, and alternatives considered.
The ICO’s documentation guidance identifies intended use, system function, decision recipient, specifications and alternatives, domain, testing and validation, and accountable roles as relevant documentation subjects.
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2. Record risk assessment and approval
- Jurisdictions and potentially applicable regulatory, data-protection, and sector requirements identified by qualified staff.
- Risk or impact assessment, affected rights, foreseeable misuse, mitigations, and residual risks.
- Approval decision, approver’s role, date, rationale, conditions, and any review or expiry trigger.
- Fit with the organization’s risk appetite and the events that require escalation or renewed approval.
The ICO says senior management should review and sign off intended use against risk appetite. Whether a particular GDPR accountability obligation or data-protection impact assessment applies depends on the processing and circumstances; record the organization’s assessment rather than assuming every AI use has the same legal status.
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3. Capture system evidence and safeguards
- A plain-language description of what the system can and cannot do.
- Relevant input and data context, handled in line with data-minimization and privacy controls.
- Validation and performance evidence relevant to the actual domain and population of use, along with known failure modes and monitoring thresholds.
- Reviewer-facing interface and controls for checking, escalation, correction, override, or safe interruption.
- Named roles for operation, human review, explanation, monitoring, and incident handling.
Keep enough context to interpret the evidence, including its limitations. Performance results from a different population, task, or deployment do not by themselves establish performance in the use being approved.
4. Log consequential decisions and human actions
For decisions where risk and applicable rules warrant case-level logging, link each entry to the approved use and relevant policy and system versions. Capture:
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- Case or decision ID and timestamp.
- The output actually considered and the material information available to the reviewer.
- Reviewer identity or role, review date, and action: accept, modify, reject, escalate, defer, or stop.
- A concise rationale and, where relevant, additional factors considered beyond the AI output.
- Any intervention, override, appeal, challenge, outcome change, or follow-up action.
These are recommended operational fields, not a claim that every field is legally required in every case. The ICO advises keeping records of requests for human intervention, people’s expressed views, contested decisions, and whether a decision changed. Its guidance also addresses meaningful review and analysis of why reviewers accept or reject outputs.
What makes human oversight meaningful?
A person’s presence in the workflow does not by itself make a decision meaningfully human-reviewed. The reviewer needs relevant information, sufficient understanding and training, enough time, and actual authority and organizational support to challenge, override, escalate, or stop the system as appropriate. A routine click-through or automatic agreement is weak evidence that the reviewer assessed the case.
The ICO warns that reviewers who routinely agree with AI outputs and cannot show genuine assessment may be treated, in the relevant UK GDPR context, as providing effectively solely automated decisions. Acceptance rates can prompt further review, but a rate alone does not prove oversight is effective. Preserve evidence of what the reviewer saw and did, including reasons for disagreement or additional factors considered, where appropriate.
For high-risk AI systems covered by the EU AI Act, Article 14 requires oversight designed to be effective and proportionate to risk, autonomy, and context. The European Commission’s AI Act Service Desk describes capabilities for assigned persons that include understanding system capacities and limitations, monitoring for problems, interpreting outputs, disregarding or reversing outputs, and intervening or stopping operation when appropriate. See Article 14: Human oversight. The appropriate controls depend on the system and use; do not treat a reviewer checklist as a substitute for usable interface design, training, authority, and escalation paths.
How should teams monitor, retrieve, and retain records?
Monitor the use, not just the model
Name an owner and set a review cadence suited to the risk. Where relevant, review errors, complaints, overrides, escalations, and changes in system behavior or deployment context. Define thresholds or events that trigger investigation, suspension, or renewed approval. A record of a human override is useful only if someone can assess what happened and whether the system or workflow needs correction.
Protect access and make records retrievable
Set access controls and integrity protections for approvals, system evidence, and case records. Establish a practical way to retrieve the explanation and relevant history for an audit, internal investigation, or individual challenge. Limit personal information in records to what is needed for the purpose and protect it under applicable privacy and security requirements.
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Set retention from applicable requirements
There is no universally established retention duration for all AI decision records in the cited sources. Determine the schedule from applicable legal, regulatory, contractual, and records-management duties, and document the rationale. A single fixed period stated as suitable for every jurisdiction and use would overstate what these sources establish.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which rules and frameworks apply?
Separate binding legal obligations from voluntary implementation resources. Applicability depends on jurisdiction, system classification, sector, and the specific processing or decision.
| Source | What it contributes | How to use it |
|---|---|---|
| European Union AI Act | For covered high-risk systems, the Act includes requirements concerning traceability logs, documentation, information for deployers, and human oversight. Article 14 specifies human-oversight provisions. | Confirm the system’s classification and current applicable legal text. The Article 14 Service Desk page is explanatory and not legally binding; implementation timing can change as amendments and schedules evolve. The European Commission’s AI Act overview provides the broader regulatory framework. |
| UK GDPR and ICO guidance | ICO guidance addresses explanation, accountability, individual rights, automated-decision safeguards, records of processing, and impact assessments where applicable. It also recommends recording intervention requests, expressed views, contests, and outcome changes. | Determine which data-protection duties apply to the particular processing. The ICO flags that its guidance is under review following the Data (Use and Access) Act; check the current guidance and law before relying on it. |
| NIST AI Risk Management Framework | AI RMF 1.0 is a voluntary framework for incorporating trustworthiness considerations into AI design, development, use, and evaluation. Its companion Playbook offers actions under Govern, Map, Measure, and Manage. | Use it as an organizational risk-management resource, not as a substitute for law or sector rules. NIST says AI RMF 1.0 is being revised. See the AI Risk Management Framework and AI RMF Playbook. |
One narrow EU rule is easy to overgeneralize: for specified high-risk systems in Annex III point 1(a), Article 14(5) provides for separate confirmation by at least two competent, trained, and authorized natural persons, subject to stated exceptions. It is not a general two-reviewer requirement for every AI-assisted decision. Check the applicable consolidated text and classification before applying that provision.
Putting the record into operation
- Map the real workflow. Identify where AI enters, what the output influences, who receives it, and who can make or change the decision.
- Classify the use and assess risk. Have qualified staff identify relevant jurisdictions, legal and sector duties, affected rights, likely harms, and the level of human control.
- Set approval conditions. Name an accountable owner and approver, document the rationale and constraints, and specify what change or incident requires renewed review.
- Design the evidence trail. Link system and policy versions to the approval record, then choose proportionate case-level fields for outputs, review actions, interventions, and challenges.
- Make review workable. Provide reviewers with training, time, relevant case information, an understandable view of system limitations, and authority to disagree or escalate.
- Test retrieval and monitoring. Confirm that authorized staff can reconstruct a decision and that monitoring findings lead to investigation, correction, or a revised approval when needed.
Keep the resulting record understandable to both technical and non-technical readers. Its purpose is not to collect every possible field; it is to make the approved use, the evidence behind it, and the organization’s actions traceable at a level appropriate to the risk.
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