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AI Act Instructions for Use Template

EU AI Act Art. 13 instructions for use for high-risk AI: intended purpose, accuracy metrics, group performance, oversight measures, logging, deployer checklist

AI・機械学習標準EU AI Act Art. 13 (as amended by 2026/1744)actinstructionsforuse

使い方: Use it when you provide a high-risk AI system and must hand deployers instructions for use under Article 13. Fill the quick reference and accuracy metrics from the technical documentation first. The main pitfall is quoting metrics without the test set, date and group breakdown.

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Instructions for Use: [High-risk AI system] [version]

Deployer-facing instructions for use for a high-risk AI system, organised on Article 13 of the EU Artificial Intelligence Act (Regulation (EU) 2024/1689 as amended by Regulation (EU) 2026/1744, the Digital Omnibus on AI). It covers provider identity, intended purpose, accuracy and its metrics, robustness and cybersecurity, known risks and misuse, performance for specific groups, input data, output interpretation, pre-determined changes, human oversight, resources and maintenance, and logging. Replace every [bracketed] value and every row marked Example. Under the amended Article 113, Chapter III Sections 1 to 3, which include Article 13, apply from 2 December 2027 for Annex III systems and from 2 August 2028 for Annex I systems; check the consolidated text. This is a documentation format, not legal advice: check the current official text and a qualified professional.

Document control

FieldValue
Document ID[IFU-001]
System name and version[Name, version, build]
Provider[Legal name]
Version of these instructions[1.0]
Status[Draft / Released / Superseded]
Owner[@product-owner]
Reviewers[@ml-lead], [@regulatory], [@technical-writer], [@security]
Linked technical documentation[Annex IV file ID]
Linked declaration of conformity[DoC ID]
Release dateYYYY-MM-DD
Languages available[List, per the Member States where the system is made available]
Format[Online help / PDF / in-product]

Approvals

RoleNameDecisionDate
Product owner[@name][Approve]YYYY-MM-DD
Regulatory lead[@name][Approve]YYYY-MM-DD
Quality manager[@name][Approve]YYYY-MM-DD

Revision history

VersionDateSystem versionChange summary
1.0YYYY-MM-DD2.4Example: First release for CRV Shortlist 2.4
1.1YYYY-MM-DD2.4.1Example: Added age-band performance table after deployer request

Contents

1. About these instructions

These instructions are for organisations that use [System name] under their own authority (deployers) and for the staff they assign to operate and oversee it. Read sections 2, 4.7 and 6 before first use.

Example: These instructions cover CRV Shortlist 2.4 by Tarnwick Analytics, a system that ranks job applications against criteria set by the recruiter. They do not cover the separate CRV Schedule Assist module, which has its own documentation.

2. Quick reference

ItemSummary
Intended purposeExample: Rank applications for a vacancy to help a recruiter decide whom to invite to interview
Not intended forExample: Automatic rejection, promotion or dismissal decisions, roles outside the supported job families, applicants under 18
Headline accuracyExample: Recall 0.80 and precision 0.67 for the "invite" band on the 2.4 validation set (section 4.2)
Main limitationExample: Lower recall for applicants aged 50 and over (0.70)
Human oversightExample: A recruiter must review the ranked list and may invite anyone regardless of rank
LogsExample: Ranking events and overrides, exportable by the deployer, retained 12 months by default

3. Provider identity (Article 13(3)(a))

ItemValue
Provider[Legal name, registered address] Example: Tarnwick Analytics
Contact for deployers[Support address, phone, portal]
Authorised representative in the EU (if applicable)[Name, address, contact]
Serious incident and risk reports[Dedicated channel]

4. Characteristics, capabilities and limitations (Article 13(3)(b))

4.1 Intended purpose (point (i))

[State the intended purpose, the specific context and the conditions of use, including users, persons affected, settings and geographic scope.]

Example: CRV Shortlist ranks applications for warehouse, driver and customer service roles in the EU. Recruiters set the job criteria and see a ranked list with three bands: invite, review, and low match. The system is intended for organisations that review each list with a trained recruiter.

4.2 Accuracy, robustness and cybersecurity (point (ii))

Example: Validation set of 4,000 historical applications with recruiter decisions, collected YYYY-MM-DD to YYYY-MM-DD; 800 were invited to interview by recruiters (the reference label).

MetricDefinitionValue (Example)
Recall (invite band)Share of reference invitees the system places in the invite band0.80 (640 of 800)
Precision (invite band)Share of invite-band applications that were reference invitees0.67 (640 of 960)
False positive rateShare of non-invitees placed in the invite band0.10 (320 of 3,200)
AccuracyShare of applications placed correctly in or out of the invite band0.88 (3,520 of 4,000)

What this means: about one in five suitable applicants is not in the invite band, so recruiters must look beyond it (section 6).

Robustness and cybersecurity itemValue
Robustness testsExample: Recall changes by no more than 0.02 when CVs are reformatted or translated
Known factors affecting performanceExample: Very short CVs, scanned CVs with OCR errors, job criteria with fewer than three requirements
Cybersecurity measuresExample: Prompt and file injection filtering on CV upload, tenant isolation, see security annex

4.3 Known and foreseeable risks (point (iii))

IDCircumstancePossible harmMitigation
RSK-01 (Example)Recruiter invites only the invite bandSuitable applicants missed, indirect discriminationOversight measure OM-01
RSK-02 (Example)Misuse to screen out applicants automaticallyDecisions without human reviewNot supported; auto-reject is disabled in the product
RSK-03 (Example)Criteria that act as proxies for protected characteristicsDiscriminationCriteria checker warns on terms such as graduation year

4.4 Explainability (point (iv))

Example: Each ranked application shows the three criteria that contributed most to its band. These are approximations of the ranking logic, not a full explanation; deployers can export them to answer requests under Article 86.

4.5 Performance for specific persons or groups (point (v))

Group (Example)Reference inviteesRecallFalse positive ratePrecision
Aged under 506900.82 (563)0.10 (284 of 2,710)0.66
Aged 50 and over1100.70 (77)0.07 (36 of 490)0.68
All8000.80 (640)0.10 (320 of 3,200)0.67

Data on disability and ethnic origin was not available in the validation set. [State how deployers should monitor these groups.]

4.6 Input data and datasets (point (vi))

ItemValue
Required inputExample: CV as PDF or DOCX up to 5 MB, application form fields 1 to 12, job criteria
Languages supportedExample: English, German, French, Spanish, Polish
Training, validation and test dataExample: Summary in technical documentation section 2.3; datasheet DS-007
Inputs to avoidExample: Photographs, dates of birth and other data not needed for the criteria

4.7 Interpreting the output (point (vii))

Example: Bands are a ranking aid, not a suitability decision. A low match band means the CV text matched few of the stated criteria; it does not mean the applicant is unqualified. Never send a rejection based on the band alone.

5. Pre-determined changes (Article 13(3)(c))

ChangeLimits assessedHow deployers are informed
Example: Quarterly recalibration of band thresholdsRecall in the invite band stays between 0.78 and 0.84 overall and no group falls below 0.68Release note and updated section 4.2
[None]

6. Human oversight measures (Article 13(3)(d))

IDMeasureBuilt into the systemDeployer action
OM-01 (Example)Review beyond the invite bandReview band shown by default; list cannot be closed before the review band is openedTrain recruiters to check the review band for every vacancy
OM-02 (Example)OverrideRecruiter can move any applicant to invite with one click; overrides are loggedGive recruiters authority to override
OM-03 (Example)StopAdministrator can pause ranking per vacancy or tenantName who may pause
OM-04 (Example)Automation bias warningBanner on first use each dayInclude in training

Competence needed by oversight staff: [Example: completion of the provider's 4-hour module, knowledge of the job criteria and of the limits in section 4.5].

See ai-data/human-oversight-design for the provider's oversight design record.

7. Resources, lifetime and maintenance (Article 13(3)(e))

ItemValue
DeploymentExample: Provider-hosted SaaS; no deployer hardware
Expected lifetime of this versionExample: Supported until YYYY-MM-DD
UpdatesExample: Security patches monthly; model recalibration quarterly (section 5)
Deployer maintenanceExample: Review job criteria every six months; keep user list current

8. Logging (Article 13(3)(f))

ItemValue
Events loggedExample: Ranking request, band per application, criteria used, model version, recruiter overrides, pauses
AccessExample: Export as CSV or via API by tenant administrators
Default retentionExample: 12 months, configurable from 6 to 36 months
How to readExample: Field dictionary in annex B

9. Deployer checklist

  • Intended purpose matches your use (section 4.1)
  • Oversight staff named, trained and given authority (section 6)
  • Input data relevant and representative for your vacancies (section 4.6)
  • Monitoring set up for the groups in section 4.5 and those without data
  • Log retention set to at least six months (section 8)
  • Workers and applicants informed (Article 26(7) and 26(11))
  • DPIA and, where required, FRIA use this document as input
  • Contact for risk and incident reports saved (section 3)

10. Glossary and references

TermMeaning
RecallShare of truly relevant cases the system finds
PrecisionShare of cases the system selects that are truly relevant
False positive rateShare of not relevant cases the system wrongly selects
DeployerThe organisation using the system under its authority
Substantial modificationA change not foreseen in the initial conformity assessment that affects compliance or the intended purpose

References (check the current consolidated text):

  • Regulation (EU) 2024/1689 (AI Act), Articles 12, 13, 14, 15, 26, 86, 113 and Annex IV: https://eur-lex.europa.eu/eli/reg/2024/1689/oj
  • Regulation (EU) 2026/1744 (Digital Omnibus on AI), OJ L 24 July 2026: http://data.europa.eu/eli/reg/2026/1744/oj
  • Related templates: ai-data/ai-act-technical-documentation, ai-data/human-oversight-design, ai-data/model-card, ai-data/ai-act-deployer-obligations, ai-data/ai-act-risk-management-system, ai-data/ai-act-declaration-of-conformity

Standard and licence note

Based on Regulation (EU) 2024/1689 (the Artificial Intelligence Act) as amended by Regulation (EU) 2026/1744 (Digital Omnibus on AI), Article 13 on transparency and provision of information to deployers, as published in the Official Journal of the European Union; EU legislative texts are free to reuse. The required content is summarised in original wording and no text of the Regulation is reproduced. Check the consolidated text on EUR-Lex for the binding wording, harmonised standards and Commission guidance; under the amended Article 113 the obligation applies from 2 December 2027 for Annex III systems and from 2 August 2028 for Annex I systems. All organisations, products and figures in examples are fictional. This is a documentation format, not legal advice. Template licence: MIT.

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