Hierarchy of skill categories - AI SKILL

Hierarchy of skill categories AI.

Live data from OECD.AI partners - OECD.AI

These visualisations leverage data from LinkedIn and Adzuna to show trends in the demand and supply of AI talent across countries and sectors over time. These data provide valuable insights into the countries and industries at the forefront of AI adoption and the skills in the highest demand. Data covers AI talent migration trends, which can inform industrial, labour market and education policies.

Explanation: This tree diagram outlines the approach taken to extract AI and machine learning skills from millions of job ads in different languages. Clicking on the circles will display the top skills that make up an IT skills category or AI subcategory.

NOTE: It is important to highlight that data sources and methodologies may differ from those used in OECD research, and therefore results may vary. As such, this section includes full and transparent methodological information. Because methods for measuring AI are still evolving, there remain definitional and other issues that can influence empirical results. Efforts to develop clear definitions, taxonomies and classifications are underway, as are efforts to compile accurate and comparable indicators. Given the current evolving situation, showing trends in a timely manner based on transparent methodologies by partner institutions can be of value to policy makers.

OECD.AI visualisations using data from these data sources were developed by the AI Lab at the Slovenian Jožef Stefan Institute (JSI). Data sources and methodologies on OECD.AI may differ from those previously used for OECD research, and therefore results may vary. Because methods for measuring AI are still evolving, there remain definitional and other issues that can influence empirical results. Efforts to develop clear definitions, taxonomies and classifications are underway, as are efforts to compile accurate and comparable indicators.

Given the current evolving situation, showing trends in a timely manner based on transparent methodologies by partner institutions can be of value to policy makers.  

The “Trends & data” section of OECD.AI aims to help policy makers develop, implement and improve policies for AI. To do so, OECD.AI showcases AI-related data and metrics over time from as many high quality sources and vantage points as possible to allow countries and stakeholders to compare policy responses, engage in international co-operation, monitor progress and develop best practices.

The below notes aim to provide a full and transparent account of the sources and methodology used to construct the “Live data” visualisations on OECD.AI:

• AI news: Event Registry
• AI research: Microsoft Academic Graph | Elsevier (Scopus)
• AI jobs and skills: LinkedIn | Adzuna
• Investments in AI: Preqin
• AI software development: GitHub
• AI education: Studyportals | Coursera
• AI search trends: Google Trends
• AI demographics: Stack Overflow Survey
• AI knowledge transfer: Stack Overflow