Papers & Publications

This technical paper introduces a novel methodology for identifying “AI patents” – patents protecting inventions related to artificial intelligence.

This report explores 200 government AI use cases AI across 11 core functions. It highlights the unique opportunities, risks, and challenges governments face, and offers insights into the enablers, safeguards, and engagement strategies needed to ensure trustworthy AI.

This paper explores the concept of openness in artificial intelligence (AI), including relevant terminology and the various levels of openness that exist.

Looking ahead, there is significant potential for AI to help improve the performance of social programmes – including through predictive analytics, enhanced outreach and better-tailored interventions – but governments must continue to build trust and foster transparency when using AI.

The paper studies the expected macroeconomic productivity gains from Artificial Intelligence (AI) over a 10-year horizon in G7 economies.

Through a review of theoretical literature and early empirical evidence, including novel descriptive analysis, this study suggests that generative AI has considerable potential to qualify as a new general-purpose technology (GPT).

Research on the impact of generative AI on productivity, innovation, and entrepreneurship highlights the extent to which AI automates tasks, enhances skills, and transforms business operations. Findings suggest that AI’s effectiveness depends on the user’s experience and the task carried out, with human-AI collaboration being key to maximising its potential.

Privacy-enhancing technologies (PETs) foster trust in the collaborative development and sharing of AI models while safeguarding privacy, intellectual property, and sensitive information. They can reduce the need for additional data collection, facilitate data-sharing partnerships, and address AI governance risks.

Empirical evidence about AI markets helps show if potential AI users benefit from favourable market developments regarding prices, quality and variety. Extensive data collection covers the characteristics of Generative AI models, like performance and pricing, as well as developers, cloud providers, and downstream applications.