International Standards

ISO/IEC 22989: Artificial Intelligence Concepts and Terminology

ISO/IEC 22989 provides a comprehensive set of concepts and terminology related to artificial intelligence. This standard aims to create a common language for AI technologies, facilitating better communication and understanding among stakeholders. It covers definitions of key AI terms, ensuring consistency and clarity across the industry.

ISO/IEC 23053: Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)

ISO/IEC 23053 outlines a framework for developing and implementing AI systems that utilize machine learning. This standard provides guidelines for the lifecycle of ML-based AI systems, including data preparation, model training, deployment, and maintenance. It aims to standardize processes to improve the reliability and effectiveness of AI solutions.

ISO/IEC 20546: Information Technology – Big Data – Overview and Vocabulary

ISO/IEC 20546 defines terminology and concepts related to big data, which is critical for AI development. This standard covers the characteristics of big data, its lifecycle, and related technologies. By standardizing big data terminology, ISO/IEC 20546 helps ensure that AI systems can effectively leverage large datasets.

IEEE 7000: Model Process for Addressing Ethical Concerns During System Design

IEEE 7000 provides guidelines for incorporating ethical considerations into the design of AI and other systems. This standard emphasizes the importance of addressing ethical issues such as bias, fairness, and transparency throughout the development process. It aims to promote responsible and ethical AI practices.

ISO/IEC JTC 1/SC 42: Artificial Intelligence

ISO/IEC JTC 1/SC 42 is a joint technical committee focused on standardizing AI technologies. This committee develops international standards for AI, covering areas such as data, algorithms, trustworthiness, and societal impacts. The work of JTC 1/SC 42 is crucial for creating a harmonized approach to AI governance and deployment.

ISO/IEC 27001: Information Security Management

ISO/IEC 27001 provides a framework for managing information security, which is essential for AI systems that handle sensitive data. This standard outlines requirements for establishing, implementing, maintaining, and continually improving an information security management system (ISMS). Ensuring the security of AI systems is critical for protecting data privacy and integrity.

ISO/IEC 27701: Privacy Information Management

ISO/IEC 27701 extends ISO/IEC 27001 to include requirements for a privacy information management system (PIMS). This standard provides guidelines for managing personal data and ensuring compliance with privacy regulations such as GDPR. Implementing ISO/IEC 27701 helps organizations protect personal data in AI systems.

NIST AI Risk Management Framework (NIST RMF)

The NIST AI Risk Management Framework provides guidelines for managing risks associated with AI systems. This framework emphasizes the importance of identifying, assessing, and mitigating risks throughout the AI lifecycle. NIST RMF helps organizations ensure the safety, security, and reliability of their AI technologies.

OECD AI Principles

The OECD AI Principles are a set of guidelines designed to promote the responsible development and use of AI. These principles emphasize fairness, transparency, accountability, and human-centered values. Adopting the OECD AI Principles helps organizations align their AI practices with internationally recognized ethical standards.

ITU-T Y.2060: Overview of the Internet of Things

ITU-T Y.2060 provides an overview of the Internet of Things (IoT), which is closely related to AI. This standard defines key IoT concepts, including architecture, technologies, and applications. Understanding IoT standards is essential for developing AI systems that interact with connected devices and sensors.

ISO/IEC 25012: Data Quality Model

ISO/IEC 25012 defines a model for data quality, covering characteristics such as accuracy, completeness, and consistency. This standard is crucial for ensuring the quality of data used in AI systems, which directly impacts their performance and reliability. Implementing ISO/IEC 25012 helps organizations manage data quality effectively.

ISO 31000: Risk Management Guidelines

ISO 31000 provides guidelines for risk management, applicable to AI and other technologies. This standard outlines principles and a framework for identifying, assessing, and managing risks. Adopting ISO 31000 helps organizations create robust risk management processes for their AI systems.

ISO/IEC 38500: Governance of IT

ISO/IEC 38500 provides principles for the effective governance of IT, including AI technologies. This standard emphasizes the importance of leadership, structure, and processes to ensure that IT supports organizational goals. Implementing ISO/IEC 38500 helps organizations align their AI strategies with broader business objectives.

The GDPR is a comprehensive data protection regulation enacted by the European Union. This regulation sets stringent requirements for the collection, use, and storage of personal data, impacting AI systems that process such data. Compliance with GDPR is essential for ensuring data privacy and protecting individuals' rights.

CCPA: California Consumer Privacy Act

The CCPA is a data privacy law that provides California residents with rights over their personal information. This regulation requires organizations to implement measures for data transparency, access, and deletion, affecting AI systems that handle personal data. Adhering to CCPA helps organizations protect consumer privacy and comply with legal obligations.