By Date

The provision of key health technologies and products such as vaccines and antibiotics is insufficient in purely competitive and volume-based markets, requiring new revenue streams for sustainability. Recent developments in health innovation suggest that innovative collaborative mechanisms can be effective in addressing this issue. In the domains of vaccines and antibiotics, these approaches should incorporate shared research investment, long-term access planning, the provision of manufacturing infrastructure, supply chains, and financial returns. Collaborative approaches such as subscription models could be piloted at the regional level, while other models could be developed to delink innovation, manufacturing, and access from sales volume and revenue. Finally, blended finance instruments from the development field could encourage greater collaboration among established and emerging stakeholders in health innovation. These stakeholders should work together to create, test, access, and implement more collaborative approaches to health innovation to share upfront investments, mitigate risks of failure, and accelerate market access.

This report examines privacy-enhancing technologies (PETs), which are digital solutions that allow information to be collected, processed, analysed, and shared while protecting data confidentiality and privacy. The report reviews recent technological advancements and evaluates the effectiveness of different types of PETs, as well as the challenges and opportunities they present. It also outlines current regulatory and policy approaches to PETs to help privacy enforcement authorities and policy makers better understand how they can be used to enhance privacy and data protection, and to improve overall data governance.

Policy makers are increasingly grappling with the stability implications of global value chains (GVCs), as widespread supply shortages following the COVID-19 pandemic and the Russian Federation’s large-scale aggression against Ukraine have disrupted the economic recovery and contributed to high inflation. This paper provides a tool to assess vulnerabilities in GVCs by drawing a detailed map of dependencies based on new indicators constructed from the OECD Inter-Country Input-Output tables. The key findings are as follows. First, GVC dependencies increase with both the size of foreign exposures and the length of foreign value chains. Second, in some industries, such as the automotive and ICT industries, vulnerabilities from high GVC dependence are amplified by high geographic concentration of suppliers or buyers. Third, the People’s Republic of China is the most critical choke point in GVCs across a broad range of industries, both as a dominant supplier and as a dominant buyer.

The transition to climate neutrality requires cost reductions in existing clean technologies to enable rapid deployment on a large scale, as well as the development of emerging technologies such as green hydrogen. This policy paper argues that science, technology, innovation, and industrial (STI&I) policies focusing on developing and deploying low-carbon technologies are crucial to achieving carbon neutrality. It notes however that the current level of innovation is insufficient to meet the net-zero challenge due to a policy emphasis on deployment rather than research and development (R&D) support. The paper explores the rationale for more ambitious STI&I policies targeted at R&D for climate neutrality and provides policy recommendations for an effective innovation policy for net-zero, including its interaction with the broader climate policy package.

Artificial intelligence (AI) is transforming economies and promising new opportunities for productivity, growth, and resilience. Countries are responding with national AI strategies to capitalise on these transformations. However, no country today has data on, or a targeted plan for, national AI compute capacity. This policy blind-spot may jeopardise domestic economic goals. This report provides the first blueprint for policy makers to help assess and plan for the national AI compute capacity needed to enable productivity gains and capture AI’s full economic potential. It provides guidance for policy makers on how to develop a national AI compute plan along three dimensions: capacity (availability and use), effectiveness (people, policy, innovation, access), and resilience (security, sovereignty, sustainability). The report also defines AI compute, takes stock of indicators, datasets, and proxies for measuring national AI compute capacity, and identifies obstacles to measuring and benchmarking national AI compute capacity across countries.

This report provides representative, cross-country estimates of the artificial intelligence (AI) workforce across OECD countries. The AI workforce is defined as the subset of workers with skills in statistics, computer science and machine learning who could actively develop and maintain AI systems. For countries that wish to be at the forefront of AI development, understanding the AI workforce is crucial to building and nurturing a talent pipeline, and ensuring that those who create AI reflect the diversity of society. This report uses data from online job vacancies to measure the within-occupation intensity of AI skill demand. The within-occupation AI intensity is then weighted to employment by occupation in labour force surveys to provide estimates of the size and growth of the AI workforce over time.

This report presents research and findings on accountability and risk in AI systems by providing an overview of how risk-management frameworks and the AI system lifecycle can be integrated to promote trustworthy AI. It also explores processes and technical attributes that can facilitate the implementation of values-based principles for trustworthy AI and identifies tools and mechanisms to define, assess, treat, and govern risks at each stage of the AI system lifecycle. This report leverages OECD frameworks – including the OECD AI Principles, the AI system lifecycle, and the OECD framework for classifying AI systems – and recognised risk-management and due-diligence frameworks like the ISO 31000 risk-management framework, the OECD Due Diligence Guidance for Responsible Business Conduct, and the US National Institute of Standards and Technology’s AI risk-management framework.

Artificial Intelligence (AI) scoring for constructed-response items, using recent advancements in multilingual, deep learning techniques utilising models pre-trained with a massive multilingual text corpus, is examined using international large-scale assessment data. Historical student responses to Reading and Science literacy cognitive items developed under the PISA analytical framework are used as training data for deep learning together with multilingual data to construct an AI model. The trained AI models are then used to score and the results compared with human-scored data. The score distributions estimated based on the AI-scored data and the human-scored data are highly consistent with each other; furthermore, even item-level psychometric properties of the majority of items showed high levels of agreement, although a few items showed discrepancies. This study demonstrates a practical procedure for using a multilingual data approach, and this new AI-scoring methodology reached a practical level of quality, even in the context of an international large-scale assessment.