AI Model Documentation

AI model documentation should explain what a model is intended to do, the evidence supporting that use, the conditions under which it was evaluated and the limitations that operators must respect. A model name, accuracy figure and link to a supplier page do not establish whether it is suitable for a particular business process.

For governance, documentation must also connect the model to the system that uses it. Inputs may be transformed, outputs may be filtered and another component may act on a prediction. The organisation needs to distinguish facts about the model from facts about the deployed workflow, especially when extending a successful pilot to another environment.

This guide helps Saudi technical owners, quality teams and governance reviewers build documentation within AI governance setup. It provides an original documentation structure and a packaging-inspection example showing why a strong aggregate result can hide a use-specific limitation.

Separate model, system and deployment documentation

A model record describes the component: task, version, training or supplier information where available, input and output specifications, evaluation and limitations. A system record explains how that component interacts with preprocessing, retrieval, rules, interfaces and people. A deployment record identifies the actual configuration, users, environment, permissions and operating owner.

These records should link rather than duplicate everything. A shared model can support several systems, and the same system can operate under different conditions. The model's published benchmark does not automatically become evidence for every downstream use. Preserve the path from component information to the local conclusion.

The original Model Cards for Model Reporting paper proposes documentation of intended use and evaluation conditions. The structure here extends the practical record to local system and deployment decisions; it is not a reproduction of the paper's example cards.

Write the intended use so an excluded use is recognisable

Describe the task, output consumer and permissible decision role. “Detect packaging defects” is less useful than “flag candidate carton-seam defects for a trained quality reviewer on a specified production line”. The latter distinguishes a screening aid from autonomous product release, process shutdown or safety certification.

List foreseeable uses that the evidence does not support. These might include a new material, camera position, product family, language or population. Explain the reason for exclusion where known: no evaluation, incompatible input, unassessed consequence or an observed failure. Avoid vague language that excludes everything while giving operators no practical boundary.

Connect the intended use to the use case classification and approval. A model card is informative; it does not itself grant authority to operate the system. The permission should identify the relevant documentation version and conditions.

Capture identity and provenance without inventing inaccessible facts

Record the model identifier, version or release reference, developer or supplier, source of the artefact, relevant licence or contract reference and local modifications. Include the date and basis of each supplier statement. If weights, training data or exact version information are unavailable, state that limitation clearly.

Separate known facts, supplier claims and local observations. A model advertised as multilingual may still require local evaluation of the intended Arabic task. A claimed training-data practice is not the same evidence as a contractual commitment or a demonstrated configuration. Keep those distinctions visible to the person making the deployment decision.

For modified models, record what changed and which evidence needs renewal. Fine-tuning, quantisation, prompt changes in a surrounding system or a different preprocessing pipeline can affect the operating result in different ways. Do not collapse them into one generic “customised” label.

Use a documentation record with decision-bearing fields

Original AI model and local-use documentation structure

Section	Useful content
Identity	Model, version, origin and evidence status
Task and role	Intended output and how people or systems use it
Inputs	Required fields, formats, units and preprocessing
Outputs	Meaning, thresholds, uncertainty and invalid-output handling
Development information	Available training or adaptation facts and known gaps
Evaluation	Dataset purpose, population, conditions, metrics and results
Limitations	Unsupported conditions and observed failure modes
System context	Rules, interfaces, human review and downstream actions
Deployment	Actual configuration, access, environment and owner
Operations	Monitoring, change triggers, fallback and retirement

Each field should help a reader judge or operate the use. Avoid filling gaps with generic language that could describe any model. Where information is not available, identify the owner of the question and the decision affected. A transparent limitation is more useful than a confident description with no source.

Reference controlled evidence rather than copying raw datasets into the document. The record may need access restrictions if it reveals sensitive source characteristics, security design or confidential supplier information. Provide an operator-facing summary that preserves the practical limits without unnecessary disclosure.

Describe evaluation populations and denominators

State why the evaluation set was created, how cases were selected and what conditions it covers. Distinguish development examples, tuning cases, diagnostic stress cases and evidence used for an acceptance decision. Results from a deliberately difficult set answer a different question from results from a representative operating sample.

Define each metric in terms of the decision. For a defect-screening aid, missed defects and unnecessary referrals have different consequences. An aggregate correct-classification percentage can conceal either. Report the underlying counts and relevant slices so reviewers can understand the tradeoff.

Record threshold settings and excluded or failed cases. A result that omits unreadable images or timeouts may overstate operational usefulness. Describe whether those cases are rejected to a human route, retried or included in a denominator. The documentation should make the evaluation reproducible enough for an authorised reviewer to challenge it.

Worked example: moving packaging inspection to a reflective-label line

Consider a fictional manufacturer using a model to flag possible carton-seam defects for a quality reviewer. The original configuration is evaluated on line A. A team proposes using it on line B, where camera conditions and reflective labels differ. The model identity is unchanged, but the local-use evidence may not transfer.

For illustration, each diagnostic set contains 120 labelled images: twenty defective and one hundred non-defective. On line A, the model flags eighteen defective images and four non-defective images. It misses two defects and correctly leaves ninety-six non-defective images unflagged. That is 114 correct classifications out of 120, or 95%.

On line B, it flags twelve defective images and two non-defective images. It misses eight defects and correctly leaves ninety-eight non-defective images unflagged. The aggregate is 110/120, about 91.7%, but defect detection falls from 18/20, or 90%, to 12/20, or 60%. These are fictional diagnostic results, not measured customer outcomes or estimates of production performance.

Illustrative counts reveal the limit of aggregate accuracy

Result	Line A	Line B
Defects correctly flagged	18	12
Defects missed	2	8
Non-defects incorrectly flagged	4	2
Non-defects correctly left unflagged	96	98
Total images	120	120
Correct classifications	114/120 = 95%	110/120 ≈ 91.7%
Defects detected	18/20 = 90%	12/20 = 60%

The documentation should preserve both sets and the conditions. Reflective labels are a plausible contributing difference, but this comparison alone does not prove causation. The next investigation may examine camera placement, illumination, packaging variation and labelling quality. The record should distinguish observed degradation from the hypothesis explaining it.

The organisation can continue to evaluate a revised line-B configuration without declaring the model generally unsuitable. It should also avoid treating line-A acceptance as permission for line B. The relevant decision concerns the local screening workflow and the evidence needed to make it useful within its approved role.

Document the meaning of scores and thresholds

Explain what an output score represents in the specific system. Do not call it a probability of correctness unless the evidence supports that interpretation. A confidence-like value can be useful for routing without being a calibrated probability. Operators need to know how the threshold was selected and what tradeoff it creates.

Record whether thresholds differ by use or environment and who may change them. In the packaging example, lowering a threshold may detect more defects while increasing unnecessary review. That is an operational tradeoff requiring evidence about reviewer capacity and missed-defect consequences, not simply a technical preference.

Include invalid and out-of-scope inputs. A blank image, obscured seam or unsupported product should have a defined disposition. Documentation that reports only numerical scores for successful cases leaves operators to improvise when the system encounters the conditions most likely to cause confusion.

Link limitations to operator behaviour

A limitation should say what the user must do differently. “Performance may vary” gives no instruction. “Use on the evaluated line configuration only; route obscured seam images to manual inspection” creates an observable operating boundary, provided that this is the actual authorised design.

Identify who receives the limitation and how their understanding is checked. The quality reviewer, release owner and maintenance team may need different details. A long technical appendix does not ensure that an operator sees the condition at the moment of use.

Connect limitations to the responsibility map. Someone must own input-quality checks, review capacity, source or camera changes and the decision to pause. The model developer cannot be assumed to own every downstream operational duty.

Keep supplier documentation and local evidence separate

For a purchased model, retain the supplier's current document and the version used in the local assessment. Add local evidence rather than editing the supplier's claims into a combined narrative that obscures their origin. Review differences between the advertised task and the actual use.

Ask for the information needed to support the decision, not automatically for every training record. The supplier evidence requirements can define acceptable documentation, limitations, change notice and support routes. If important information is unavailable, record the effect on evaluation and permission.

A supplier update may require reviewing local evidence even when the product name remains unchanged. Conversely, a change to the local camera or workflow can invalidate local conclusions while the supplier model stays the same. The documentation links both directions of change.

Maintain versions, review triggers and retirement history

Assign an owner to each documentation layer and preserve approved versions. Link model and system changes to the change-trigger process, including altered inputs, thresholds, users and downstream actions. A calendar update is useful for stale information but cannot replace event-based review.

At release, verify that the deployed configuration matches the documentation. At retirement, retain the history needed to interpret earlier outputs and any continuing obligations. Removing a model endpoint does not necessarily remove stored predictions or decisions made from them.

Use the AI inventory to connect records across uses. A single model update should be traceable to affected deployments, while a local limitation should not be incorrectly generalised to unrelated uses with different evidence.

Keep an evaluation lineage record that survives a corrected label

For the packaging case, store the result beside a compact record of how it was produced. The record should identify the line and camera configuration, image-set version, label-set version, model and preprocessing versions, threshold, evaluation script and person authorised to settle disputed defect labels. These are dependencies of the result. A screenshot of a percentage cannot show whether the same test would reach the same conclusion.

Illustrative evaluation lineage for the proposed line-B use

Record field	Entry or evidence required
Purpose	Diagnose transfer to line B; not estimate all production defects
Population	The identified 120-image diagnostic set, including its twenty labelled defects
Configuration	Linked camera, preprocessing, model and threshold records
Label authority	Quality reviewer responsible for resolving ambiguous seams
Accounting	All images reconciled as evaluated, rejected or unavailable
Decision limit	No inference that line-A permission covers line B

Suppose a quality review later finds that an image was assigned the wrong reference label. Preserve the original result as a historical evaluation, record the correction and recalculate the affected metrics with the revised label-set identifier. Explain whether the earlier decision relied on that result and requires reconsideration. Silently replacing the old percentage would make the original decision impossible to reconstruct; refusing to correct a known label error would preserve an unreliable conclusion.

Keep evaluation and threshold selection distinguishable. If the team repeatedly adjusts the threshold against the same diagnostic cases, describe that work as development or investigation. A subsequent acceptance argument needs suitable evidence for the intended claim, including cases that do not merely repeat what the team already optimised. The record should state this limitation without pretending that any fixed sample size guarantees a reliable production estimate.

When another reviewer receives the result, test whether they can access the referenced versions and understand excluded cases. Not every reader needs the sensitive images, but an authorised reviewer needs a way to verify the evidence. If access is unavailable, describe the limitation on verification rather than claiming unrestricted reproducibility.

Build an operator card from the limits that change behaviour

The short operating document should be derived from the controlled technical record, with an explicit link to its version. For carton inspection, its central question is what the reviewer should do with a particular image and output. Include the evaluated line configuration, the role of the flag, the manual route for an obscured seam, the required review before product disposition and the contact for a changed camera or material. Do not compress those conditions into a generic statement to use professional judgement.

Test the card with an operator who did not write it. Present an image from the approved line, an image with the seam obscured and an image from the proposed second line. Ask which evidence applies, what action is permitted and where the unresolved case goes. The exercise tests the usefulness of the documentation, not the operator's ability to remember a technical appendix. If two readers infer different release authority, revise the wording and verify the actual permission before relying on the card.

Different audiences can receive different detail without receiving conflicting boundaries. A maintenance engineer needs the camera and preprocessing dependencies; a quality reviewer needs dispositions and escalation; a governance reviewer needs the basis for the permitted use. Give each view the same controlling identifiers and exclusion rules. Restricted technical material can remain in an authorised repository while the practical operating limits remain available at the point of use.

Finally, assign a publication owner for the derived card. When local evidence changes, check whether the card, interface guidance and training example also need revision. Track acknowledgement where it supports the operating control, and test behaviour where misunderstanding would matter. Updating the model document alone is incomplete if the person deciding what happens to a carton still follows an obsolete instruction.

Test whether the documentation supports a real decision

Give a reviewer who did not write the document a proposed expansion. Ask them to identify supported and unsupported conditions, relevant evaluation counts, evidence gaps and the authority needed for the next step. If the answer depends on undocumented knowledge held by the model team, the record is incomplete.

Have an operator explain what happens with an unreadable input, an uncertain score and a changed environment. Compare that explanation with the approved operating design. The documentation should support consistent action, not merely satisfy a checklist of headings.

Discuss AI model documentation with Open for AI using a model and the workflow in which it will operate. The useful output is a linked, versioned explanation of capability and limits, supported by local evidence and clear enough to guide approval, operation and change.