OECD AI Principles overview

The OECD AI Principles promote use of AI that is innovative and trustworthy and that respects human rights and democratic values. Adopted in May 2019, they set standards for AI that are practical and flexible enough to stand the test of time.

Principles for trustworthy AI

The OECD AI Principles were initially adopted in 2019 and updated in May 2024. Adherents updated them to consider new technological and policy developments, ensuring they remain robust and fit for purpose.

The Principles guide AI actors in their efforts to develop trustworthy AI and provide policymakers with recommendations for effective AI policies.

Countries use the OECD AI Principles and related tools to shape policies and create AI risk frameworks, building a foundation for global interoperability between jurisdictions. Today, the European Union, the Council of Europe, the United States, and the United Nations and other jurisdictions use the OECD’s definition of an AI system and lifecycle below in their legislative and regulatory frameworks and guidance. The principles, definition and lifecycle are all part of the OECD Recommendation on Artificial Intelligence.

Values-based principles

Inclusive growth, sustainable development and well-being

Human rights and democratic values, including fairness and privacy

Transparency and explainability

Robustness, security and safety

Accountability

Recommendations for policy makers

Investing in AI research and development

Fostering an inclusive AI-enabling ecosystem

Shaping an enabling interoperable governance and policy environment for AI

Building human capacity and preparing for labour market transition

International co-operation for trustworthy AI

Countries adhering to the AI Principles

The OECD Recommendation on AI is the first intergovernmental standard on AI. Today, there are 47 adherents to the Principles.

See list of adherents

An AI system is a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment.