ISO/IEC 23053:2022 - Framework for AI systems using machine learning

Summary

ISO/IEC 23053:2022 cuts through the AI hype by establishing a clear, standardized vocabulary and conceptual framework specifically for machine learning-based AI systems. This isn't another high-level AI ethics document—it's a technical standard that provides the foundational architecture and terminology needed to describe, design, and discuss ML systems consistently across organizations and industries. Think of it as the common language that engineers, regulators, and business leaders need to have productive conversations about AI systems that actually work.

What sets this apart from other AI standards

While most AI frameworks focus on principles or risk management, ISO/IEC 23053 tackles the fundamental problem of definitional chaos in the AI field. It's the first international standard to systematically map out how machine learning components fit within broader AI systems architecture.

Key differentiators:

• Technical specificity: Goes beyond buzzwords to define actual system components and their relationships
• ML-focused scope: Specifically addresses machine learning implementations rather than AI in general
• Architectural approach: Provides a blueprint for understanding how ML systems are structured and interconnected
• Implementation-neutral: Describes what systems do, not how to build them or what values they should embody

This standard serves as the missing foundation layer that other AI governance frameworks assume you already have.

Core framework components you need to know

The standard breaks down ML-based AI systems into distinct, interconnected elements that form a comprehensive ecosystem view:

Data handling pipeline: From raw data ingestion through preprocessing, training datasets, and data governance mechanisms that ensure quality and compliance throughout the ML lifecycle.

ML model architecture: The computational core including algorithms, training processes, validation methods, and model versioning—essentially everything that turns data into predictions.

System integration layer: How ML components connect with existing enterprise systems, APIs, user interfaces, and external data sources to create functional business applications.

Operational infrastructure: The runtime environment including compute resources, monitoring systems, security controls, and maintenance processes that keep ML systems running reliably.

Governance and oversight mechanisms: The human and automated processes for quality assurance, performance monitoring, bias detection, and compliance verification.

Who this resource is for

AI system architects and engineers who need standardized terminology to design and document ML systems that can be understood across teams and organizations.

Technical program managers overseeing AI implementations who require a common framework to communicate requirements, progress, and issues to both technical teams and business stakeholders.

Compliance and risk professionals working with AI systems who need structured approaches to assess and document ML system components for regulatory reporting or audit purposes.

Procurement and vendor management teams evaluating AI solutions who need consistent criteria and language to compare different ML system architectures and capabilities.

Standards bodies and regulators developing AI-specific requirements who need precise technical definitions as building blocks for more detailed regulations.

How to put this framework into practice

Start by conducting a terminology audit of your current AI documentation and processes. Map your existing ML system descriptions to the ISO framework components to identify gaps and inconsistencies in how different teams describe the same systems.

Use the standard's architectural model to create system documentation templates that ensure consistent description of ML components across all your AI projects. This creates a foundation for better technical communication and knowledge transfer.

Implement the framework as your vendor evaluation criteria when assessing ML platforms or services. Require suppliers to describe their offerings using ISO 23053 terminology, making it easier to compare capabilities and identify integration requirements.

Leverage the standardized vocabulary in your governance processes. Risk assessments, compliance reviews, and audit procedures become more systematic when everyone uses the same technical language to describe ML system components.

Build the framework into your AI project planning process. Use the component model as a checklist to ensure you're considering all necessary elements when designing new ML systems or upgrading existing ones.