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OECD.AI Index Summary

Core Content

The OECD.AI Index is a comprehensive measurement framework designed to assess the implementation of the OECD Recommendation on Artificial Intelligence (AI), adopted in 2019 and revised in 2024. It serves as a policy-oriented tool to support evidence-based AI governance, facilitate international comparison, and guide future monitoring of progress toward trustworthy AI.

The Index is structured around five key policy areas: AI Research and Development, AI Enabling Infrastructure, AI Policy Environment, Jobs and Skills, and International Co-operation. It integrates 28 AI-specific indicators, sourced from official statistics, administrative records, surveys, and innovative data sources, to provide a holistic view of national AI ecosystems.

Main Objectives

• Monitor progress in implementing the OECD AI Recommendation.
• Facilitate cross-country comparison of AI policies and ecosystems.
• Support evidence-based decision-making for AI governance.
• Guide future monitoring of AI development and implementation.
• Provide a modular framework that allows for the integration of new metrics over time.

Key Components

The Index is composed of five main components, each reflecting a core policy area from the OECD AI Recommendation:

1. AI Research and Development (R&D): Measures investment and activity in AI research and innovation.
2. AI Enabling Infrastructure: Assesses the technological and digital infrastructure that supports AI development.
3. AI Policy Environment: Evaluates the regulatory and policy landscape for AI.
4. Jobs and Skills: Focuses on the labor market and workforce readiness for AI.
5. International Co-operation: Tracks global collaboration and alignment in AI governance.

Methodology

The OECD.AI Index employs a composite measurement approach, combining quantitative and qualitative indicators. The methodology includes the following steps:

• Data Processing: Aggregating and cleaning data from various sources.
• Missing Value Imputation: Filling in missing data to ensure completeness.
• Normalisation: Adjusting data to a common scale for comparison.
• Weighting and Aggregation: Assigning weights to indicators and combining them to form an overall score.
• Robustness and Sensitivity Analysis: Conducting statistical checks to ensure accuracy and reliability.

Data Sources

The Index draws on a wide range of data sources, including:

• Official Statistics: Governmental and national reports.
• Administrative Records: Data from public institutions and regulatory bodies.
• Surveys: National and international surveys on AI adoption and usage.
• Innovative Data Sources: Emerging datasets and AI-specific metrics.

Country Coverage and Gaps

Currently, the Index covers all OECD Member countries, with plans to expand to non-member Adherents and GPAI members where data is available. Some data gaps exist, particularly in less developed economies and for certain indicators, such as those related to the implementation of the AI principles.

Results

The Index provides results for the years 2023 and 2024, with scores ranging from 0.17 to 0.66 across countries and components. The results highlight significant variations in AI implementation and offer insights into areas where countries have made progress or require improvement.

A case study on Sweden is included, showing the country's performance across the five components. The Index also demonstrates how small changes in rankings can occur between years due to policy changes or shifts in other indicators.

Next Steps

The OECD.AI Index is expected to undergo annual updates and biennial reviews to ensure its relevance and accuracy. The Index will be expanded to include more indicators and refine its components through iterative calibration. An online interface is planned for launch on the OECD.AI platform, offering interactive visualisations and detailed resources for users.

Key Contributions

• Policy Alignment: The Index is directly aligned with the OECD AI principles and national policy recommendations.
• Comparative Analysis: It enables meaningful cross-country comparisons based on AI-specific metrics.
• Modularity: The framework is designed to be flexible and adaptable to new developments in AI.
• Robustness: Rigorous statistical checks ensure the Index's reliability and accuracy.
• Accessibility: The online interface will make the Index more user-friendly and accessible to a broader audience.

Stakeholders

• Policymakers: Primary users to assess national progress and identify areas for improvement.
• Researchers and Think Tanks: Interested in the data and insights provided for further analysis.
• Industry and Civil Society: Can use the Index to understand the AI landscape and contribute to global discussions.

Conclusion

The OECD.AI Index is a significant step toward creating a standardized and comprehensive framework for evaluating AI implementation and governance. By focusing on both technical and ethical aspects of AI, it supports the development of responsible AI policies and practices, ultimately contributing to the broader goal of trustworthy AI.