AI Documentation Requirements Matrix and Model Card Template

What documentation each system legally needs, and what you can skip. A requirements matrix by risk tier, plus the four templates that matter most: model card, data governance record, bias assessment, and human oversight arrangement.

Who this is for: The compliance owner deciding, per system, what to produce and what is genuinely optional.

Download the kit (Markdown) ↓4 artifacts. Every table also copies as CSV.

1. Documentation requirements matrix

Spreadsheet

Required, recommended, or optional for each document type at each tier.

Template

Document	High	Moderate (affects people)	Low
Model card	Required	Required	Optional
Data governance record	Required	Required	Recommended
Bias / fairness assessment	Required	Required	Not needed
Human oversight arrangement	Required	Required	Not needed
Decision log	Required	Required	Not needed
Risk assessment	Required	Required	Optional
Change / version history	Required	Required	Basic
Conformity assessment / FRIA	If triggered	If triggered	No

Worked example

System	Tier	Result of applying the matrix
Resume Screener	High	Every row required. FRIA triggered (Annex III use).
Support Copilot	Moderate (drafts refund decisions)	Model card, data record, bias assessment, oversight arrangement, decision log all required.
Ad Copy Generator	Low (no personal data, no decisions)	Model card optional, nothing else. "Why not" recorded in one line.

Acceptance criteria

✓Each system has been placed in a tier and the matrix applied, with the result recorded.
✓Where a document is skipped, the reason is written down (one line is enough).
✓The matrix cites the regulation behind each "required".

2. Model card template

Document

The core system description. One per model, refreshed on material change.

Template

Keep current. A stale model card is a finding.

Model name and version:
Owner:
Intended use, and explicitly out-of-scope use:
How it works: model type, base model, key parameters
Training or retrieval data: sources, cut-off, lawful basis, known gaps
Performance: metrics with dates and the evaluation set
Bias / fairness evaluation: method, results, thresholds, date
Limitations and failure modes:
Human oversight: where a person is in the loop and what they can do
Monitoring: what is watched in production and at what cadence
Change history: link to the version log

Worked example

Model: Resume Screener v2.4.0
Owner: D. Portillo, Head of Talent
Intended use: rank inbound applicants 1-5 for recruiter triage. Out of scope: automated rejection, offer decisions.
How it works: VendorLM-4 base, fine-tuned on 5 years of internal hiring outcomes; deterministic scoring head.
Data: internal hiring records to 2026-06; lawful basis legitimate interest with applicant notice; known gap: thin data for niche roles.
Performance: ranking accuracy 0.81 on the 2026-08 holdout set.
Bias evaluation: adverse-impact ratio by sex and race/ethnicity; lowest 0.88 (2026-08); threshold 0.80.
Limitations: degrades when a role's requirement mix changes; can over-weight tenure.
Human oversight: recruiter sees the score, can ignore it; low scores are not auto-filtered as of v2.4.
Monitoring: monthly adverse-impact check; quarterly accuracy review.
Change history: registry mdl-resume-screen, version log.

Acceptance criteria

✓The card names both intended and out-of-scope use.
✓Every performance and bias figure has a date and an evaluation set.
✓The card is refreshed whenever the versioning policy creates a new version.

3. Data governance record template

Spreadsheet

The provenance and lawful-basis record for the data a system uses.

Template

Field	Entry
System
Data set / feed
Data categories (incl. special category?)
Source and provenance
Lawful basis
Consent / notice mechanism
Minimization measures
Retention period
Cross-border transfer and mechanism
Data subject rights handling (access, deletion)
Last reviewed

Worked example

Field	Entry
System	Resume Screener v2.4
Data set	Applicant submissions + 5-yr internal hiring outcomes
Data categories	Name, contact, work history, education; special-category data possible in free-text CVs
Source	Submitted by applicants via the careers site; HRIS for outcomes
Lawful basis	Legitimate interest (recruitment), with applicant notice
Consent / notice	Privacy notice section 4, shown at application
Minimization	Free-text CV parsed to structured fields; raw CV not used for inference after parsing
Retention	Applicant data 12 months post-decision; model training extract anonymized
Cross-border	Processing in EU; base model inference in EU region
Rights handling	Access and deletion via the DSAR process; model does not retain per-applicant state
Last reviewed	2026-08-30

Acceptance criteria

✓Special-category data is explicitly flagged yes or no, not left blank.
✓The lawful basis is stated and matches the privacy notice.
✓The record has a review date and is refreshed when the data set changes.

4. Bias assessment template

Document

Method, findings, remediation, and a re-test schedule for any system that affects people.

Template

One per system that makes or informs decisions about individuals.

System and version:
Protected attributes assessed: and how they were obtained or inferred
Metrics used: (e.g. selection-rate ratio, equal opportunity difference), with the threshold for each
Evaluation data: source, size, time period, representativeness notes
Results: per attribute, per metric, against threshold
Findings: where disparity exceeds threshold, and the likely driver
Remediation: actions taken or planned, with owners and dates
Residual disparity accepted: by whom, with rationale
Re-test schedule: cadence and trigger events

Worked example

System: Resume Screener v2.4.0
Protected attributes: sex and race/ethnicity, from voluntary EEO self-identification, held separately from the model inputs
Metrics: adverse-impact ratio (threshold 0.80); score-distribution difference (flag if > 0.1 SD)
Evaluation data: 12 months of scored applicants, ~38,000 records
Results: adverse-impact ratio 0.88 (sex), 0.91 (race/ethnicity); score-distribution difference within flag
Findings: no disparity above threshold this cycle; prior cycle (v2.3) was 0.83 for sex, improved after re-weighting tenure
Remediation: none required this cycle; keep the tenure-weight cap from v2.3
Residual disparity accepted: ratio between 0.80 and 0.90 accepted by D. Portillo pending the next cycle
Re-test schedule: monthly, plus on any model change

Acceptance criteria

✓Every metric has a stated threshold set before results are read.
✓Findings above threshold have a remediation action with an owner and a date.
✓Any accepted residual disparity names who accepted it and why.
✓A re-test cadence and trigger events are set.

Governance controls this kit produces evidence for

Completing the artifacts above gives you a head start on the evidence requirements for these controls.

CMP-007

The requirements matrix and templates cover the EU AI Act technical documentation set for high-risk systems.

DGC-001

The data governance record documents training and input data provenance and lawful basis.

MON-003

The bias assessment template plus its re-test schedule are the fairness monitoring record.

ALC-004

The model card is the explainability documentation for the system.

HOC-001

Applying the matrix requires a documented risk tier per system.

This kit backs one playbook. Read the full guidance for the reasoning behind each artifact.

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