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  • 24-April-2024

    English

    Transformative policies and anticipatory governance are key to optimising benefits and managing risks of new emerging technologies

    Science and technology ministers have highlighted the need for governments to develop co-ordinated approaches to harness the opportunities of new and emerging technologies, while better managing future risks, at their ministerial-level meeting at the OECD.

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  • 24-April-2024

    English

    OECD Agenda for Transformative Science, Technology and Innovation Policies

    Multiple crises are triggering turbulence, instability and insecurity in contemporary societies, with impacts on economies, the environment, politics, and global affairs. An effective response will require governments to be more ambitious and act with greater urgency in their science, technology and innovation (STI) policies to meet global challenges. Sustained investments and greater directionality in research and innovation activities are needed, and these should coincide with a reappraisal of STI systems and STI policies to ensure they are 'fit-for-purpose' to contribute to transformative change agendas. This policy paper provides a framework to support governments in making these assessments. It identifies six STI policy orientations for transformative change that should guide these assessments. It applies these orientations across multiple areas of STI policy, including R&D funding, the research and innovation workforce, and international R&D co-operation, and outlines a series of concrete policy actions STI policymakers can take to accelerate transformative change.
  • 24-April-2024

    English

    Framework for Anticipatory Governance of Emerging Technologies

    Emerging technologies can contribute to unprecedented gains in health, energy, climate, food systems, and biodiversity. However, these technologies and their convergence sometimes carry risks to privacy, security, equity and human rights. This dual-edged nature of emerging technology requires policies that better anticipate disruptions and enable technology development for economic prosperity, resilience, security and sustainable development. Drawing on prior OECD work and legal instruments, this framework equips governments, other innovation actors and societies to anticipate and get ahead of governance challenges, and build longer-term capacities to shape innovation more effectively. Its 'anticipatory technology governance' approach consists of five interdependent elements and associated governance tools: (1) embeding values throughout the innovation process; (2) enhancing foresight and technology assessment; (3) engaging stakeholders and society; (4) building regulation that is agile and adaptive; and (5) reinforcing international cooperation in science and norm-making. The emerging technology context determines how each of these elements is applied.
  • 23-April-2024

    English, PDF, 2,793kb

    Neurotechnology toolkit

    This toolkit aims to support policymakers in implementing the OECD Recommendation on Responsible Innovation in Neurotechnology.

  • 19-April-2024

    English

    Scientometrics

    This page provides information on OECD work on scientometrics and bibliometrics. This field has has evolved over time from the study of indices for improving information retrieval from peer-reviewed scientific publications (commonly described as the “bibliometric” analysis of science) to cover other types of documents and information sources relating to science and technology.

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  • 16-April-2024

    English

    The impact of Artificial Intelligence on productivity, distribution and growth - Key mechanisms, initial evidence and policy challenges

    This paper explores the economics of Artificial Intelligence (AI), focusing on its potential as a new General-Purpose Technology that can significantly influence economic productivity and societal wellbeing. It examines AI's unique capacity for autonomy and self-improvement, which could accelerate innovation and potentially revive sluggish productivity growth across various industries, while also acknowledging the uncertainties surrounding AI's long-term productivity impacts. The paper discusses the concentration of AI development in big tech firms, uneven adoption rates, and broader societal challenges such as inequality, discrimination, and security risks. It calls for a comprehensive policy approach to ensure AI's beneficial development and diffusion, including measures to promote competition, enhance accessibility, and address job displacement and inequality.
  • 10-April-2024

    English

    Artificial intelligence and the changing demand for skills in the labour market

    Most workers who will be exposed to artificial intelligence (AI) will not require specialised AI skills (e.g. machine learning, natural language processing, etc.). Even so, AI will change the tasks these workers do, and the skills they require. This report provides first estimates for the effect of artificial intelligence on the demand for skills in jobs that do not require specialised AI skills. The results show that the skills most demanded in occupations highly exposed to AI are management and business skills. These include skills in general project management, finance, administration and clerical tasks. The results also show that there have been increases over time in the demand for these skills in occupations highly exposed to AI. For example, the share of vacancies in these occupations that demand at least one emotional, cognitive or digital skill has increased by 8 percentage points. However, using a panel of establishments (which induces plausibly exogenous variation in AI exposure), the report finds evidence that the demand for these skills is beginning to fall.
  • 10-April-2024

    English

    Artificial intelligence and wage inequality

    This paper looks at the links between AI and wage inequality across 19 OECD countries. It uses a measure of occupational exposure to AI derived from that developed by Felten, Raj and Seamans (2019) – a measure of the degree to which occupations rely on abilities in which AI has made the most progress. The results provide no indication that AI has affected wage inequality between occupations so far (over the period 2014-2018). At the same time, there is some evidence that AI may be associated with lower wage inequality within occupations – consistent with emerging findings from the literature that AI reduces productivity differentials between workers. Further research is needed to identify the exact mechanisms driving the negative relationship between AI and wage inequality within occupations. One possible explanation is that low performers have more to gain from using AI because AI systems are trained to embody the more accurate practices of high performers. It is also possible that AI reduces performance differences within an occupation through a selection effect, e.g. if low performers leave their job because they are unable to adapt to AI tools by shifting their activities to tasks that AI cannot automate.
  • 5-April-2024

    English

    OECD Main Science and Technology Indicators

    A timely set of indicators that reflect the level and structure of the efforts undertaken by OECD member countries and selected non-member economies in the field of science and technology.

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  • 22-March-2024

    English

    Generative AI for anti-corruption and integrity in government - Taking stock of promise, perils and practice

    Generative artificial intelligence (AI) presents myriad opportunities for integrity actors—anti-corruption agencies, supreme audit institutions, internal audit bodies and others—to enhance the impact of their work, particularly through the use of large language models (LLMS). As this type of AI becomes increasingly mainstream, it is critical for integrity actors to understand both where generative AI and LLMs can add the most value and the risks they pose. To advance this understanding, this paper draws on input from the OECD integrity and anti-corruption communities and provides a snapshot of the ways these bodies are using generative AI and LLMs, the challenges they face, and the insights these experiences offer to similar bodies in other countries. The paper also explores key considerations for integrity actors to ensure trustworthy AI systems and responsible use of AI as their capacities in this area develop.
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