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• Create Date August 25, 2025
• Last Updated August 25, 2025

AI Model Card Development and Management Procedure

Subtitle: Establish a repeatable, compliance-ready workflow with the AI Model Card Development and Management Procedure for transparent and auditable model documentation.

AI Model Card Development Procedure: [Download Now]

The AI Model Card Development and Management Procedure provides organizations with a step-by-step framework for creating, managing, and maintaining model cards. By implementing this procedure, teams ensure transparency, regulatory alignment, and consistency in documenting AI models throughout their lifecycle.

Key Benefits:

• ✅ Lifecycle Coverage: From initiation through approval, publication, updates, and archival.

• ✅ Compliance Alignment: Explicitly mapped to EU AI Act Annex IV, NIST AI RMF, and ISO/IEC 42001.

• ✅ Cross-Functional Workflow: Involves data science, ethics officers, compliance, legal, and governance councils.

• ✅ Audit-Ready Documentation: Includes checklists, review forms, and regulatory mapping appendices.

• ✅ Governance Integration: Supports risk assessments, ethical reviews, and compliance verification processes.

Who Uses This?
Data science teams, compliance officers, AI ethics committees, and governance leaders requiring a standardized AI Model Card Development and Management Procedure for regulatory and operational assurance.

Why This Matters

AI regulators and frameworks increasingly require model-level documentation to support accountability, transparency, and risk management. Without a structured AI Model Card Development and Management Procedure, organizations risk fragmented documentation, compliance failures, and lack of oversight for high-risk AI. This procedure provides a repeatable governance workflow for organizations of all sizes.

Framework Alignment

This procedure aligns with:

• EU AI Act Annex IV — Technical documentation obligations for high-risk systems.

• NIST AI RMF — Trustworthy AI documentation requirements.

• ISO/IEC 42001 — Governance, lifecycle, and risk treatment documentation.

• ISO/IEC 23894 — Risk management integration.

• OECD AI Principles — Transparency, accountability, and fairness.

Key Features

• Defined Responsibilities: RACI for data scientists, ethics officers, compliance, legal, QA, governance councils.

• Initiation Criteria: Identifies when a Model Card is required (production models, high-risk, retraining, scope expansion).

• Information Gathering: Collects technical specs, datasets, training methods, use cases, risks, and user impact.

• Draft Development: Populates structured Model Card templates with automation and performance visualizations.

• Review Workflow: Includes technical, ethical, compliance, business, and product reviews.

• Approval & Publication: Defines approval gates, publication in centralized repositories, and internal/external distribution.

• Maintenance & Updates: Covers periodic reviews, incident-driven updates, version control, and archival.

• Audit Tools: Appendices with checklists, review forms, and regulatory mapping to EU AI Act Annex IV, NIST, and ISO standards.

Comparison Table

FAQ Section 

Q1: What is the AI Model Card Development and Management Procedure?
A: It is a structured governance procedure that defines how organizations create, review, approve, and maintain model cards across the AI lifecycle.

Q2: Which regulations and frameworks does it align with?
A: It aligns with EU AI Act Annex IV, ISO/IEC 42001, ISO/IEC 23894, and NIST AI RMF.

Q3: Does it cover review and approval workflows?
A: Yes. It includes technical, ethical, legal, compliance, and product reviews with a formal approval workflow.

Q4: Does it support audit readiness?
A: Yes. It includes appendices with checklists, review forms, regulatory mappings, and workflow diagrams.

Q5: Is it scalable for SMEs and large enterprises?
A: Yes. SMEs can use core workflows, while enterprises can integrate into MLOps pipelines, model registries, and CI/CD processes.

Q6: What format is best for using this procedure?
A: Documents are best viewed and used via Microsoft Word or Excel. Formatting may not fully display in Google Docs or other editors.

Ideal For

• Data Science & MLOps Teams

• AI Governance Committees

• Legal & Compliance Teams

• Risk & Audit Managers

• Chief AI Officers (CAIOs)

• Ethics & Responsible AI Leads