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The digital economy is growing, with producers increasingly using digital technology to revolutionise their production processes, and with new business models being created based on the digital transformation. To improve the visibility of digitalisation in macroeconomic statistics, the Digital Supply and Use Tables (SUTs) framework has been developed under the auspices of the OECD’s Informal Advisory Group (IAG) on Measuring GDP in a Digitalised Economy. In the Digital SUTs framework, three dimensions are introduced for measuring the digital economy: the nature of the transaction (the 'how'), the goods and services produced (the 'what'), and the new digital industries (the 'who'). The OECD Handbook on Compiling Digital SUTs explains these three dimensions and includes examples. It also presents the high priority indicators that have been agreed by the IAG and includes recommended templates for producing the outputs.

Responsible science communication is crucial for fostering public trust in science and promoting evidence-based policymaking. However, in an evolving landscape shaped by the digital transformation and complex crises like the COVID-19 pandemic, science communication faces new challenges including widespread mis- and disinformation. To address these challenges, science communicators should follow key principles for responsible science communication including transparency, inclusivity, integrity, accountability, freedom and autonomy, and timeliness. Policymakers in turn are encouraged to promote these principles, invest in science communication capacity, establish crisis communication structures, support scientists in public communication, and promote scientific and digital literacy.

In 2019, the OECD Council adopted the Recommendation on Artificial Intelligence (the 'OECD AI Principles'). These include five values-based principles and five recommendations for OECD countries and adhering partner economies to promote responsible and trustworthy AI policies. This report takes stock of initiatives launched by countries worldwide to implement the OECD AI Principles which were reported to the OECD.AI Policy Observatory as of May 2023. It provides an overview of national AI strategies, including their oversight and monitoring bodies, expert advisory groups, as well as their monitoring and evaluation frameworks. It also discusses the various regulatory approaches that countries are adopting to ensure AI trustworthiness, such as ethics frameworks, AI-specific regulations, and regulatory sandboxes. Additionally, the report offers policy examples for each of the ten OECD AI Principles to facilitate cross-learning among policymakers.

Artificial intelligence (AI) offers tremendous benefits but also poses risks. Some of these risks have materialised into what are known as 'AI incidents'. Due to the widespread use of AI in various sectors, a surge in such incidents can be expected. To effectively monitor and prevent these risks, stakeholders need a precise yet adaptable definition of AI incidents. This report presents research and findings on terminology and practices related to incident definitions, encompassing both AI-specific and cross-disciplinary contexts. It establishes a knowledge base for identifying commonalities and encouraging the development of AI-specific adaptations in the future.

This report analyses the demand for positions that require skills needed to develop or work with AI systems across 14 OECD countries between 2019 and 2022. It finds that, despite rapid growth in the demand for AI skills, AI-related online vacancies comprised less than 1% of all job postings and were predominantly found in sectors such as ICT and Professional Services. Skills related to Machine Learning were the most sought after. The US-focused part of the study reveals a consistent demand for socio-emotional, foundational, and technical skills across all AI employers. However, leading firms – those who posted the most AI jobs – exhibited a higher demand for AI professionals combining technical expertise with leadership, innovation, and problem-solving skills, underscoring the importance of these competencies in the AI field.

This paper presents new evidence on the size and direction of governments’ R&D funding response to the COVID-19 pandemic through the exploration of a novel data infrastructure, the OECD Fundstat initiative for the analysis of government-funded R&D projects. The document reports on the exploratory development and application of automatic classification tools to detect relevant COVID-19 R&D funding, map salient topics and classify and allocate project funding according to priorities in the WHO COVID-19 R&D Blueprint, as well as comparing results with similar analysis of scientific publication output data. The results provide new insights on which areas of enquiry were prioritised by governmental R&D funding bodies.

This document reports on the final output of the OECD microBeRD+ project. Drawing on the outcomes of previous work, this study presents new evidence on the impact of business R&D support policies – tax incentives and direct forms of support – on business R&D investment (R&D input additionality) and the innovation and economic performance of firms (R&D output additionality). The report also provides an exploratory analysis of R&D spillovers.

This report assesses the continued relevance of the OECD Recommendation on Cross-Border Co-operation in the Enforcement of Laws Protecting Privacy, originally adopted in 2007. It examines whether the Recommendation has kept pace with the evolving needs of Privacy Enforcement Authorities (PEAs) in light of significant technological and legal changes over the past 15 years. While the principles underlying the Recommendation are seen to remain solid, the report highlights several gaps and challenges for cross-border enforcement co-operation. It concludes that the OECD could either provide additional guidance to support the implementation of the Recommendation or consider revising it to better address current challenges.

Generative artificial intelligence (AI) creates new content in response to prompts, offering transformative potential across multiple sectors such as education, entertainment, healthcare and scientific research. However, these technologies also pose critical societal and policy challenges that policy makers must confront: potential shifts in labour markets, copyright uncertainties, and risk associated with the perpetuation of societal biases and the potential for misuse in the creation of disinformation and manipulated content. Consequences could extend to the spreading of mis- and disinformation, perpetuation of discrimination, distortion of public discourse and markets, and the incitement of violence. Governments recognise the transformative impact of generative AI and are actively working to address these challenges. This paper aims to inform these policy considerations and support decision makers in addressing them.