Yes—but only if you kept more than the AI’s output. To explain an AI-assisted decision six months later, you need a record of the decision’s context, the system and information involved, the output, the human reviewer’s reasoning, and any explanation or follow-up given to the affected person. A raw log or model-generated explanation alone does not establish why the final decision was sound.
What should you document when AI helps make a decision?
Build a record that lets a later reviewer follow the chain from the system’s role to the human decision and its consequences. The list below is a practical governance checklist, not a universal legal requirement: required records depend on jurisdiction, system classification, and each organization’s role.
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- Purpose and scope: State what the AI system was intended to do, which decision it supported, and who received or was affected by the decision.
- System context: Identify the system, its role in the process, and the documentation needed to interpret its output and limitations. If a vendor supplied the system, you may need to obtain relevant information from that vendor.
- Relevant data and provenance: Record the inputs or data sources that materially shaped the recommendation, along with context needed to understand them. Avoid retaining personal data that is not necessary.
- Output and human review: Preserve the output used and identify the reviewer or decision-maker. Note whether the person accepted, changed, or rejected the recommendation.
- Reason for the final decision: Record the human rationale, relevant evidence, and policy or criteria applied. This makes the decision understandable, rather than merely replayable.
- Explanation and follow-up: Note what explanation was given, to whom, when, and through which channel. Record any correction, appeal, or subsequent action where relevant.
- Ownership, access, and retention: Specify who is responsible for the record, who can access it, and how long it is kept under applicable law and organizational policy.
NIST says that maintaining data provenance and supporting attribution of AI decisions can assist transparency and accountability. Its framework also treats accountability and transparency as organizational and human concerns, not just properties of a model. See the NIST AI Risk Management Framework and its AI RMF 1.0.
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A system log can help establish what happened technically: for example, that the system produced an output or that an event occurred. It does not necessarily show why a person relied on that output, whether they considered other evidence, or why they reached the final outcome. A defensible record connects technical traceability to human judgment and the communication made to the decision recipient.
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That distinction matters because an explanation produced by a model is not, by itself, proof that the decision was correct or justified. A later reviewer needs the decision criteria, evidence, human interpretation, and any override—not just a plausible-sounding account generated after the fact.
The UK Information Commissioner’s Office (ICO) recommends documentation that supports an explanation to the decision recipient and an audit trail showing who received explanations and how they were provided. Its guidance also recommends documenting the process and choices behind developing, acquiring, and deploying decision-support systems. The ICO guidance is official but is marked as under review following legislative changes; check its current status and applicable law before relying on it.
How detailed should the record be?
Scale documentation to the risk and impact of the decision. A low-impact recommendation, such as which film to watch, generally calls for less documentation than a consequential decision such as recruitment. The ICO advises a risk-based approach; the specific record should be proportionate to the effects on people and the organization’s obligations.
For consequential decisions, make sure a reviewer can identify the applicable criteria, the information considered, how the AI recommendation affected the outcome, and who was accountable for the final choice. For any level of impact, do not collect or retain unnecessary personal data: documentation needs to be weighed against data-protection requirements.
Does the EU AI Act require six months of AI logs?
Not for every AI system or every organization. Under Article 12 of the EU AI Act, high-risk AI systems must technically allow automatic recording of events over the system’s lifetime. The logging capabilities must support traceability appropriate to the system’s intended purpose, including risk-related situations and monitoring. Article 19 requires providers to retain automatically generated logs insofar as those logs are under their control, for an appropriate period of at least six months, unless applicable EU or national law provides otherwise—particularly data-protection law.
That is a scoped legal duty, not a blanket rule that every AI-assisted decision must be retained for six months. Whether it applies depends on the Regulation’s scope, the system’s classification, the actor’s role, and control of the logs. The provider’s retention duty should not automatically be assigned to the organization making the final decision. Check the exact use case and actor obligations in the official text of Regulation (EU) 2024/1689.
The Act also distinguishes logs from technical documentation: Article 18 requires providers to keep specified technical documentation available to competent authorities for 10 years. That is not the Article 19 log-retention period, nor does it mean all decision records must be retained for 10 years.
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| Framework | What it establishes | What it does not mean |
|---|---|---|
| NIST AI Risk Management Framework | Voluntary guidance for managing AI risks across design, development, use, and evaluation; it emphasizes trustworthiness, transparency, and accountability. | It is not, by itself, a legal retention mandate. |
| EU AI Act | Binding requirements within the Regulation’s scope, including logging and retention duties for covered high-risk systems and specified actors. | It does not create a universal six-month retention rule for every AI system or decision. |
NIST states that the AI RMF is intended for voluntary use. Its resource page reports that AI RMF 1.0 is being updated, so confirm the current edition when applying the framework. Voluntary guidance can help shape a recordkeeping process, but it does not replace applicable legal duties.
Use six months as a practical test, not a guarantee
Ask whether someone who was not part of the original decision could reconstruct its path from the record: what the system was meant to do, what information and version were involved, what it returned, how a person handled the recommendation, why the final decision was made, and what the affected person was told. If the record cannot answer those questions, retaining a raw log for six months may still leave the decision difficult to explain.
Set retention and access rules alongside that recordkeeping process. Applicable data-protection law and organizational policy may limit what can be kept or for how long; the appropriate period depends on the circumstances, not a single universal number.
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