ISO-IEC 23053:2022

Approaching AI systems through ML

Standard ISO/IEC 23503 establishes an Artificial Intelligence (AI) and Machine Learning (ML) framework for describing a generic AI system using ML technology. This framework describes the system components and their functions in the AI ecosystem. The standard is applicable to all types and sizes of organizations that are implementing or using AI systems, including public and private companies, government entities, and not-for-profit organizations.

Benefits of ISO/IEC 23503

ISO/IEC 23503 provides a unified framework for describing AI system components and functions, promoting clarity, interoperability, and collaboration across industries. Scalable to organizations of all sizes, it supports the entire AI lifecycle, enhances compliance, and simplifies risk management. By aligning with international standards, it fosters innovation and ensures systematic management of AI systems, enabling organizations to optimize performance and meet strategic goals effectively.

Frequently Asked Questions (FAQs)

Q. How does ISO/IEC 23503 align with other standards?

ISO/IEC 23503 integrates with standards like ISO/IEC 22989 for lifecycle management and ISO/IEC 23894 for risk management. This alignment ensures a comprehensive and compatible approach to managing AI systems.

Q. What industries can use ISO/IEC 23503?

The framework is applicable across diverse industries, including healthcare, finance, retail, and government. Any organization implementing AI systems with ML technologies can benefit from adopting this standard.

Q. How do we get started with ISO/IEC 23503?

Organizations can begin by assessing their current AI systems, mapping components to the framework, and aligning functions with organizational goals. This process ensures smooth integration into workflows.

Q. Is ISO/IEC 23503 applicable to non-ML AI systems?

While the framework is specifically designed for systems using machine learning, its principles can also provide a foundational structure for managing other types of AI systems. However, its full benefits are best realized in ML-focused applications.