Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)

Summary

ISO/IEC 23053:2022 breaks new ground as the first international standard to establish a unified vocabulary and structural framework for describing AI systems that use machine learning. Rather than prescribing specific implementation requirements, this standard provides the foundational language and conceptual model that organizations worldwide can use to consistently describe, analyze, and communicate about their AI/ML systems. It's essentially the Rosetta Stone for AI governance—creating common understanding across industries, jurisdictions, and technical disciplines.

The framework's building blocks

The standard organizes AI/ML systems around four core perspectives that work together to provide a complete system description:

• Functional perspective focuses on what the system does—its inputs, outputs, and intended functionality without getting into implementation details. This helps stakeholders understand system capabilities and limitations.
• Implementation perspective describes how the system works—the ML algorithms, data processing methods, and technical architecture. This level provides the detail needed for technical assessment and risk analysis.
• Lifecycle perspective traces the system from conception through retirement, covering development phases, deployment considerations, and ongoing maintenance requirements.
• Trustworthiness perspective addresses reliability, fairness, transparency, and other qualities that determine whether the system is suitable for its intended use. This connects directly to emerging AI governance requirements.

Who this resource is for

• AI product managers and system architects who need to document AI/ML systems for internal governance, regulatory compliance, or stakeholder communication will find this framework invaluable for creating consistent, comprehensive system descriptions.
• Risk and compliance professionals can use this standard to establish systematic approaches for AI system assessment, ensuring they capture all relevant aspects when evaluating systems against regulatory requirements or internal policies.
• Standards organizations and regulators developing AI-specific requirements can reference this framework to ensure their guidance aligns with established international terminology and system models.
• Enterprise AI governance teams implementing organization-wide AI oversight will benefit from the structured approach to system categorization and description that this standard provides.

What makes this different from other AI standards

Unlike prescriptive standards that tell you what to do, ISO/IEC 23053:2022 gives you the language and structure to describe what you're already doing. It's technology-agnostic and doesn't favor specific ML approaches or vendor solutions.

The standard deliberately avoids creating new terminology where existing concepts already work well. Instead, it builds on established IT and systems engineering vocabulary while adding the AI/ML-specific elements that weren't covered before.

Most importantly, it's designed to work alongside other standards rather than replace them. Whether you're implementing ISO 42001 for AI management systems or following NIST's AI Risk Management Framework, this standard provides the underlying descriptive foundation.

Getting value from the framework

Start by using the standard's terminology consistently across your AI documentation. This immediately improves communication between technical teams, business stakeholders, and governance functions.

Apply the four perspectives systematically when documenting new AI/ML systems. You don't need to use every element the standard defines, but having the complete framework ensures you don't miss critical aspects.

Use the implementation perspective's detailed breakdowns when conducting technical risk assessments or preparing for external audits. The structured approach helps demonstrate thorough system understanding.

Leverage the lifecycle perspective to identify governance touchpoints and decision gates that might otherwise be overlooked in traditional software development processes.

Quick reference essentials

• Document type: Descriptive framework standard (not requirements)
• Scope: AI systems that use machine learning technology
• Key innovation: First international standard for AI/ML system description
• Relationship to other standards: Foundational—designed to support other AI standards
• Implementation effort: Low to medium—primarily affects documentation and communication practices
• Best used with: ISO 42001, NIST AI RMF, sector-specific AI guidance