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The OECD AI Principles call for AI actors to be accountable for the proper functioning of their AI systems in accordance with their role, context, and ability to act. Likewise, the OECD Guidelines for Multinational Enterprises aim to minimise adverse impacts that may be associated with an enterprise’s operations, products and services. To develop ‘trustworthy’ and ‘responsible’ AI systems, there is a need to identify and manage AI risks. As calls for the development of accountability mechanisms and risk management frameworks continue to grow, interoperability would enhance efficiency and reduce enforcement and compliance costs. This report provides an analysis of the commonalities of AI risk management frameworks. It demonstrates that, while some elements may sometimes differ, all the risk management frameworks analysed follow a similar and sometimes functionally equivalent risk management process.

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.

The OECD Privacy Guidelines are the first internationally agreed-upon set of privacy principles and are recognized as the global minimum standard for privacy and data protection. They are a solid foundation for building effective protection and trust for individuals, and also for developing common international approaches to transborder data flows. Since their adoption, they have influenced legislation and policy in OECD countries and beyond. This document reproduces the two existing explanatory memoranda that accompany the OECD Privacy Guidelines. The first, published in 1980, was developed alongside the original version of the OECD Privacy Guidelines to help in their interpretation and application. The supplementary Explanatory Memorandum was developed to provide context and rationale for the revisions to the OECD Privacy Guidelines made in 2013.

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.

Stimulus packages adopted following the COVID-19 pandemic – such as the US Inflation Reduction Act and NextGenerationEU - have been presented as an opportunity to 'build back better' and accelerate the transition to a low-carbon economy while re-igniting the economy. But this revival of industrial policy has also raised concerns about the potential for a global green subsidy war. OECD analysed funding measures worth USD 1.3 trillion announced around the world in 2020-21 to support development and diffusion of low-carbon technologies. These measures can trigger substantial greenhouse gas emissions reductions while boosting the growth of the clean tech sector in all regions and reducing dependence over fossil fuel imports. This policy brief summarises key findings from our analysis and offers additional recommendations to policymakers.

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.