Publications

Recent advances in AI technology, particularly generative AI, have raised many questions about data governance and privacy. However, AI and privacy policy communities often address these issues independently, with approaches that vary between jurisdictions and legal systems. These silos can generate misunderstandings, add complexities in regulatory compliance and enforcement, and prevent capitalising on commonalities between […]

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.

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.

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.

In May 2023, G7 Leaders identified topics for discussion in the Hiroshima Artificial Intelligence (AI) Process and called for an early stocktaking of opportunities and challenges related to generative AI. This report presents the results of a questionnaire to G7 members developed to support the stocktaking and help guide G7 discussions on common policy priorities about generative AI. It also provides a brief overview of the development of generative AI over time and across countries. The report and questionnaire results represent a snapshot in time: they are indicative of trends identified in the first half of 2023 in a rapidly evolving area of technology. The report helped inform and structure discussions of the G7 Hiroshima AI Process.

This report focuses on regulatory sandboxes in artificial intelligence (AI), where authorities engage firms to test innovative products or services that challenge existing legal frameworks. Participating firms obtain a waiver from specific legal provisions or compliance processes to innovate. It highlights positive impacts like increased venture capital investment in fintech start-ups. It points out challenges, risks, and policy considerations for AI sandboxes, emphasizing interdisciplinary cooperation, building AI expertise, regulatory interoperability, and trade policy. It also addresses the importance of comprehensive criteria for eligibility and assessing trials, as well as the impact on innovation and competition.

AI language models are a key component of natural language processing (NLP), a field of artificial intelligence (AI) focused on enabling computers to understand and generate human language. Language models and other NLP approaches involve developing algorithms and models that can process, analyse and generate natural language text or speech trained on vast amounts of data using techniques ranging from rule-based approaches to statistical models and deep learning. The application of language models is diverse and includes text completion, language translation, chatbots, virtual assistants and speech recognition. This report offers an overview of the AI language model and NLP landscape with current and emerging policy responses from around the world. It explores the basic building blocks of language models from a technical perspective using the OECD Framework for the Classification of AI Systems. The report also presents policy considerations through the lens of the OECD AI Principles.

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 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.