Template-type: ReDIF-Paper 1.0
Author-Name: Philippe Lorenz
Author-Name: Karine Perset
Author-Name: Jamie Berryhill
Title: Initial policy considerations for generative artificial intelligence
Abstract: 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.
Keywords: AI, artificial intelligence, generative artificial intelligence, mis- and disinformation
Creation-Date: 2023-09-18
Number: 1
Handle: RePEc:oec:comaaa:1-EN

Template-type: ReDIF-Paper 1.0
Author-Name: Francesca Borgonovi
Author-Name: Flavio Calvino
Author-Name: Chiara Criscuolo
Author-Name: Lea Samek
Author-Name: Helke Seitz
Author-Name: Julia Nania
Author-Name: Julia Nitschke
Author-Name: Layla O’Kane
Title: Emerging trends in AI skill demand across 14 OECD countries
Abstract: 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.
Classification-JEL: C81; J23; J24; O33
Keywords: Artificial Intelligence, Online vacancies, Skills
Creation-Date: 2023-10-17
Number: 2
Handle: RePEc:oec:comaaa:2-EN

Template-type: ReDIF-Paper 1.0
Author-Name: OECD
Title: The state of implementation of the OECD AI Principles four years on
Abstract: 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.
Creation-Date: 2023-10-27
Number: 3
Handle: RePEc:oec:comaaa:3-EN

Template-type: ReDIF-Paper 1.0
Author-Name: OECD
Title: Stocktaking for the development of an AI incident definition
Abstract: 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.
Keywords: Artificial intelligence, AI incident, AI risks
Creation-Date: 2023-10-27
Number: 4
Handle: RePEc:oec:comaaa:4-EN

Template-type: ReDIF-Paper 1.0
Author-Name: OECD
Title: Common guideposts to promote interoperability in AI risk management
Abstract: 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.
Keywords: AI, AI interoperability, artificial intelligence, OECD AI Principles, OECD Guidelines for Multinational Enterprises, responsible AI, trustworthy AI
Creation-Date: 2023-11-07
Number: 5
Handle: RePEc:oec:comaaa:5-EN

Template-type: ReDIF-Paper 1.0
Author-Name: Flavio Calvino
Author-Name: Chiara Criscuolo
Author-Name: Hélène Dernis
Author-Name: Lea Samek
Title: What technologies are at the core of AI?: An exploration based on patent data
Abstract: This report outlines a new methodology and provides a first exploratory analysis of technologies and applications that are at the core of recent advances in AI. Using AI-related keywords and technology classes, the study identifies AI-related patents protected in the United States in 2000-18. Among those, “core” AI patents are selected based on their counts of AI-related forward citations. The analysis finds that, compared to other (AI and non-AI) patents, they are more original and general, and tend to be broader in technological scope. Technologies related to general AI, robotics, computer/image vision and recognition/detection are consistently listed among core AI patents, with autonomous driving and deep learning having recently become more prominent. Finally, core AI patents tend to spur innovation across AI-related domains, although some technologies – likely AI applications, such as autonomous driving or robotics – appear to increasingly contribute to developments in their own field.
Classification-JEL: C81; O31; O33; O34
Keywords: Artificial Intelligence, Innovation, Patents
Creation-Date: 2023-11-13
Number: 6
Handle: RePEc:oec:comaaa:6-EN

Template-type: ReDIF-Paper 1.0
Author-Name: Chloé Touzet
Title: Using AI to support people with disability in the labour market: Opportunities and challenges
Abstract: People with disability face persisting difficulties in the labour market. There are concerns that AI, if managed poorly, could further exacerbate these challenges. Yet, AI also has the potential to create more inclusive and accommodating environments and might help remove some of the barriers faced by people with disability in the labour market. Building on interviews with more than 70 stakeholders, this report explores the potential of AI to foster employment for people with disability, accounting for both the transformative possibilities of AI-powered solutions and the risks attached to the increased use of AI for people with disability. It also identifies obstacles hindering the use of AI and discusses what governments could do to avoid the risks and seize the opportunities of using AI to support people with disability in the labour market.
Classification-JEL: J14; J18; J20
Keywords: Artificial Intelligence, Disability, Employment
Creation-Date: 2023-11-24
Number: 7
Handle: RePEc:oec:comaaa:7-EN

Template-type: ReDIF-Paper 1.0
Author-Name: OECD
Title: Explanatory memorandum on the updated OECD definition of an AI system
Abstract: In November 2023, OECD member countries approved a revised version of the Organisation’s definition of an AI system. This document contains proposed clarifications to the definition of an AI system contained in the 2019 OECD Recommendation on AI (the “AI Principles”) to support their continued relevance and technical soundness. The goal of the definition of an AI system in the OECD Recommendation is to articulate what is considered to be an AI system, for purposes of the recommendation.
Keywords: AI Principles, AI system, artificial intelligence, OECD Recommendation on Artificial Intelligence
Creation-Date: 2024-03-05
Number: 8
Handle: RePEc:oec:comaaa:8-EN

Template-type: ReDIF-Paper 1.0
Author-Name: OECD
Title: Generative artificial intelligence in finance
Abstract: The rapid acceleration in the pace of AI innovation in recent years and the advent of content generating capabilities (Generative AI or GenAI) have increased interest in AI innovation in finance, in part due to the user-friendliness and intuitive interface of GenAI tools. The use of AI in financial markets involving full end-to-end automation without any human intervention remains largely at development phase, but its wider deployment could amplify risks already present in financial markets and give rise to new challenges. This paper presents recent evolutions in AI in finance and potential risks and discusses whether policy makers may need to reinforce policies and strengthen protection against these risks.
Creation-Date: 2023-12-15
Number: 9
Handle: RePEc:oec:comaaa:9-EN

Template-type: ReDIF-Paper 1.0
Author-Name: Brian Anderson
Author-Name: Eric Sutherland
Title: Collective action for responsible AI in health
Abstract: Artificial intelligence will have profound impacts across health systems, transforming health care, public health, and research. Responsible AI can accelerate efforts toward health systems being more resilient, sustainable, equitable, and person-centred. This paper provides an overview of the background and current state of artificial intelligence in health, perspectives on opportunities, risks, and barriers to success. The paper proposes several areas to be explored for policy makers to advance the future of responsible AI in health that is adaptable to change, respects individuals, champions equity, and achieves better health outcomes for all. The areas to be explored relate to trust, capacity building, evaluation, and collaboration. This recognises that the primary forces that are needed to unlock the value from artificial intelligence are people-based and not technical. The OECD is ready to support efforts for co-operative learning and collective action to advance the use of responsible AI in health.
Creation-Date: 2024-01-19
Number: 10
Handle: RePEc:oec:comaaa:10-EN

Template-type: ReDIF-Paper 1.0
Author-Name: OECD
Title: Using AI in the workplace: Opportunities, risks and policy responses
Abstract: AI can bring significant benefits to the workplace. In the OECD AI surveys of employers and workers, four in five workers say that AI improved their performance at work and three in five say that it increased their enjoyment of work. But the benefits of AI depend on addressing the associated risks. Taking the effect of AI into account, occupations at highest risk of automation account for about 27% of employment in OECD countries. Workers also express concerns around increased work intensity, the collection and use of data, and increasing inequality. To support the adoption of trustworthy AI in the workplace, this policy paper identifies the main risks that need to be addressed when using AI in the workplace. It identifies the main policy gaps and offers possible policy avenues specific to labour markets.
Creation-Date: 2024-03-15
Number: 11
Handle: RePEc:oec:comaaa:11-EN

Template-type: ReDIF-Paper 1.0
Author-Name: Gavin Ugale
Author-Name: Cameron Hall
Title: Generative AI for anti-corruption and integrity in government: Taking stock of promise, perils and practice
Abstract: 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.
Creation-Date: 2024-03-22
Number: 12
Handle: RePEc:oec:comaaa:12-EN

Template-type: ReDIF-Paper 1.0
Author-Name: Alexandre Georgieff
Title: Artificial intelligence and wage inequality
Abstract: 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.
Classification-JEL: J21; J23; J24; O33
Keywords: Artificial intelligence, Employment, Skills
Creation-Date: 2024-04-10
Number: 13
Handle: RePEc:oec:comaaa:13-EN

Template-type: ReDIF-Paper 1.0
Author-Name: Andrew Green
Title: Artificial intelligence and the changing demand for skills in the labour market
Abstract: 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.
Classification-JEL: J23; J24; J63
Keywords: Artificial intelligence, Labour demand, Skills
Creation-Date: 2024-04-10
Number: 14
Handle: RePEc:oec:comaaa:14-EN

Template-type: ReDIF-Paper 1.0
Author-Name: Francesco Filippucci
Author-Name: Peter Gal
Author-Name: Cecilia Jona-Lasinio
Author-Name: Alvaro Leandro
Author-Name: Giuseppe Nicoletti
Title: The impact of Artificial Intelligence on productivity, distribution and growth: Key mechanisms, initial evidence and policy challenges
Abstract: 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.
Classification-JEL: O15
Keywords: Artificial intelligence, Competition, Productivity
Creation-Date: 2024-04-16
Number: 15
Handle: RePEc:oec:comaaa:15-EN

Template-type: ReDIF-Paper 1.0
Author-Name: OECD
Title: Defining AI incidents and related terms
Abstract: As AI use grows, so do its benefits and risks. These risks can lead to actual harms ("AI incidents") or potential dangers ("AI hazards"). Clear definitions are essential for managing and preventing these risks. This report proposes definitions for AI incidents and related terms. These definitions aim to foster international interoperability while providing flexibility for jurisdictions to determine the scope of AI incidents and hazards they wish to address.
Creation-Date: 2024-05-06
Number: 16
Handle: RePEc:oec:comaaa:16-EN

Template-type: ReDIF-Paper 1.0
Author-Name: Andrew Green
Title: Artificial intelligence and the changing demand for skills in Canada: The increasing importance of social skills
Abstract: 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 Canada on 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, communication and digital skills. These include skills in budgeting, accounting, written communication, as well as competencies in basic word processing and spreadsheet software. The results also show that, in Canada, demand for social and language skills have increased the most over the past decade in occupations highly exposed to AI. Using a panel of establishments confirms the increasing demand for social and language skills, as well as rising demand for production and physical skills, which may be complementary to AI. However, the establishment panel also finds evidence of decreasing demand for business, management and digital skills in establishments more exposed to AI.
Classification-JEL: J23; J24; J63
Keywords: Artificial Intelligence, Canada, Skills
Creation-Date: 2024-05-30
Number: 17
Handle: RePEc:oec:comaaa:17-EN

Template-type: ReDIF-Paper 1.0
Author-Name: OECD
Title: Artificial intelligence, data and competition
Abstract: This paper discusses recent developments in Artificial Intelligence (AI), particularly generative AI, which could positively impact many markets. While it is important that markets remain competitive to ensure their benefits are widely felt, the lifecycle for generative AI is still developing. This paper focuses on three stages: training foundation models, fine-tuning and deployment. It is too early to say how competition will develop in generative AI, but there appear to be some risks to competition that warrant attention, such as linkages across the generative AI value chain, including from existing markets, and potential barriers to accessing key inputs such as quality data and computing power. Several competition authorities and policy makers are taking actions to monitor market developments and may need to use the various advocacy and enforcement tools at their disposal. Furthermore, co-operation could play an important role in allowing authorities to efficiently maintain their knowledge and expertise.
Creation-Date: 2024-05-24
Number: 18
Handle: RePEc:oec:comaaa:18-EN

Template-type: ReDIF-Paper 1.0
Author-Name: Ailbhe Brioscú
Author-Name: Anne Lauringson
Author-Name: Anne Saint-Martin
Author-Name: Theodora Xenogiani
Title: A new dawn for public employment services: Service delivery in the age of artificial intelligence
Abstract: As part of broader digitalisation efforts, half of public employment services (PES) in OECD countries are employing Artificial Intelligence (AI) to enhance their services. AI is being adopted across all key tasks of PES, including most commonly to match jobseekers with vacancies. While several PES have been using such tools for a decade, adoption of AI has been increasing in recent years as these become more accessible. New AI use cases have emerged to assist employers in designing vacancy postings and jobseekers in their career management and job-search strategies. AI initiatives have significant impact on PES clients, changing how they interact with the PES and receive support, and PES staff, altering their day-to-day work. As PES seek to maximise the opportunities brought by AI, proactive steps should be taken to mitigate associated risks. Key considerations for PES include prioritising transparency of AI algorithms and explainability of results, establishing governance frameworks, ensuring end-users (staff and clients) are included and supported in the development and adoption process, and committing to rigorous monitoring and evaluation to increase the positive and manage any negative impact of AI solutions.
Classification-JEL: J24; J63; J64; J68; O33
Keywords: activation, artificial intelligence, digitalisation, job matching, profiling, public employment services, unemployment
Creation-Date: 2024-06-13
Number: 19
Handle: RePEc:oec:comaaa:19-EN

Template-type: ReDIF-Paper 1.0
Author-Name: OECD
Title: Governing with Artificial Intelligence: Are governments ready?
Abstract: OECD countries are increasingly investing in better understanding the potential value of using Artificial Intelligence (AI) to improve public governance. The use of AI by the public sector can increase productivity, responsiveness of public services, and strengthen the accountability of governments. However, governments must also mitigate potential risks, building an enabling environment for trustworthy AI. This policy paper outlines the key trends and policy challenges in the development, use, and deployment of AI in and by the public sector. First, it discusses the potential benefits and specific risks associated with AI use in the public sector. Second, it looks at how AI in the public sector can be used to improve productivity, responsiveness, and accountability. Third, it provides an overview of the key policy issues and presents examples of how countries are addressing them across the OECD.
Keywords: AI in the public sector, government use of AI
Creation-Date: 2024-06-13
Number: 20
Handle: RePEc:oec:comaaa:20-EN

Template-type: ReDIF-Paper 1.0
Author-Name: Annelore Verhagen
Title: Using AI to manage minimum income benefits and unemployment assistance: Opportunities, risks and possible policy directions
Abstract: While means-tested benefits such as minimum income benefits (MIB) and unemployment assistance (UA) are an essential safety net for low-income people and the unemployed, incomplete take-up is the rule rather than the exception. Building on desk research, open-ended surveys and semi-structured interviews, this paper investigates the opportunities and risks of using artificial intelligence (AI) for managing these means-tested benefits. This ranges from providing information to individuals, through determining eligibility based on pre-determined statutory criteria and identifying undue payments, to notifying individuals about their eligibility status. One of the key opportunities of using AI for these purposes is that this may improve the timeliness and take-up of MIB and UA. However, it may also lead to systematically biased eligibility assessments or increase inequalities, amongst others. Finally, the paper explores potential policy directions to help countries seize AI’s opportunities while addressing its risks, when using it for MIB or UA management.
Classification-JEL: C8; H53; I3; J68; O3
Keywords: Artificial Intelligence, Means-Tested Benefits, Minimum Income Benefits, Social Protection, Unemployment Assistance
Creation-Date: 2024-06-24
Number: 21
Handle: RePEc:oec:comaaa:21-EN

Template-type: ReDIF-Paper 1.0
Author-Name: OECD
Title: AI, data governance and privacy: Synergies and areas of international co-operation
Abstract: Recent AI technological advances, particularly the rise of generative AI, have raised many data governance and privacy questions. 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 national frameworks. This report focuses on the privacy risks and opportunities stemming from recent AI developments. It maps the principles set in the OECD Privacy Guidelines to the OECD AI Principles, takes stock of national and regional initiatives, and suggests potential areas for collaboration. The report supports the implementation of the OECD Privacy Guidelines alongside the OECD AI Principles. By advocating for international co-operation, the report aims to guide the development of AI systems that respect and support privacy.
Keywords: Artificial intelligence, privacy
Creation-Date: 2024-06-26
Number: 22
Handle: RePEc:oec:comaaa:22-EN

Template-type: ReDIF-Paper 1.0
Author-Name: Samo Varsik
Author-Name: Lydia Vosberg
Title: The potential impact of Artificial Intelligence on equity and inclusion in education
Abstract: This working paper reviews the impact of Artificial Intelligence (AI) on equity and inclusion in education, focusing on learner-centred, teacher-led and other institutional AI tools. It highlights the potential of AI in adapting learning while also addressing challenges such as access issues, inherent biases and the need for comprehensive teacher training. The paper emphasises the importance of balancing the potential benefits of AI with ethical considerations and the risk of exacerbating existing disparities. It highlights the need to address privacy and ethical concerns, enhance cultural responsiveness, manage techno ableism and provide continuing professional learning in AI. Additionally, the paper stresses the importance of maintaining educational integrity amidst growing commercial influence. It encourages research on AI tools’ implications for equity and inclusion to ensure that AI adoption in education supports a more equitable and inclusive learning environment.
Creation-Date: 2024-08-14
Number: 23
Handle: RePEc:oec:comaaa:23-EN

Template-type: ReDIF-Paper 1.0
Author-Name: OECD
Title: Regulatory approaches to Artificial Intelligence in finance
Abstract: The use of Artificial Intelligence (AI) in finance has increased rapidly in recent years, with the potential to deliver important benefits to market participants and to improve customer welfare. At the same time, AI in finance could also amplify existing risks in financial markets and create new ones. This report analyses different regulatory approaches to the use of AI in finance in 49 OECD and non-OECD jurisdictions based on the Survey on Regulatory Approaches to AI in Finance.
Creation-Date: 2024-09-05
Number: 24
Handle: RePEc:oec:comaaa:24-EN

Template-type: ReDIF-Paper 1.0
Author-Name: Julia Schmidt
Author-Name: Graham Pilgrim
Author-Name: Annabelle Mourougane
Title: Measuring the demand for AI skills in the United Kingdom
Abstract: This paper estimates the artificial intelligence-hiring intensity of occupations/industries (i.e. the share of job postings related to AI skills) in the United Kingdom during 2012-22. The analysis deploys a natural language processing algorithm (NLP) on online job postings, collected by Lightcast, which provides timely and detailed insights into labour demand for different professions. The key contribution of the study lies in the design of the classification rule identifying jobs as AI-related which, contrary to the existing literature, goes beyond the simple use of keywords. Moreover, the methodology allows for comparisons between data-hiring intensive jobs, defined as the share of jobs related to data production tasks, and AI-hiring intensive jobs. Estimates point to a rise in the economy-wide AI-hiring intensity in the United Kingdom over the past decade but to fairly small levels (reaching 0.6% on average over the 2017-22 period). Over time, the demand for AI-related jobs has spread outside the traditional Information, Communication and Telecommunications industries, with the Finance and Insurance industry increasingly demanding AI skills. At a regional level, the higher demand for AI-related jobs is found in London and research hubs. At the occupation level, marked changes in the demand for AI skills are also visible. Professions such as data scientist, computer scientist, hardware engineer and robotics engineer are estimated to be the most AI-hiring intense occupations in the United Kingdom. The data and methodology used allow for the exploration of cross-country estimates in the future.
Classification-JEL: C80; C88; E01; J21
Keywords: AI-hiring intensity, artificial intelligence, job advertisements, natural language processing, united kingdom
Creation-Date: 2024-09-05
Number: 25
Handle: RePEc:oec:comaaa:25-EN

Template-type: ReDIF-Paper 1.0
Author-Name: Marguerita Lane
Title: Who will be the workers most affected by AI?: A closer look at the impact of AI on women, low-skilled workers and other groups
Abstract: This paper examines how different socio-demographic groups experience AI at work. As AI can automate non-routine, cognitive tasks, tertiary-educated workers in “white-collar” occupations will likely face disruption, even if empirical analysis does not suggest that overall employment levels have fallen due to AI, even in “white-collar” occupations. The main risk for those without tertiary education, female and older workers is that they lose out due to lower access to AI-related employment opportunities and to productivity-enhancing AI tools in the workplace. By identifying the main risks and opportunities associated with different socio-demographic groups, the ultimate aim is to allow policy makers to target supports and to capture the benefits of AI (increased productivity and economic growth) without increasing inequalities and societal resistance to technological progress.
Classification-JEL: J16; J21; J23; J24; O33
Keywords: Artificial Intelligence, Education, Employment, Gender, Inequality
Creation-Date: 2024-10-31
Number: 26
Handle: RePEc:oec:comaaa:26-EN

Template-type: ReDIF-Paper 1.0
Author-Name: OECD
Title: Assessing potential future artificial intelligence risks, benefits and policy imperatives
Abstract: The swift evolution of AI technologies calls for policymakers to consider and proactively manage AI-driven change. The OECD’s Expert Group on AI Futures was established to help meet this need and anticipate AI developments and their potential impacts. Informed by insights from the Expert Group, this report distils research and expert insights on prospective AI benefits, risks and policy imperatives. It identifies ten priority benefits, such as accelerated scientific progress, productivity gains and better sense-making and forecasting. It discusses ten priority risks, such as facilitation of increasingly sophisticated cyberattacks; manipulation, disinformation, fraud and resulting harms to democracy; concentration of power; incidents in critical systems and exacerbated inequality and poverty. Finally, it points to ten policy priorities, including establishing clearer liability rules, drawing AI “red lines”, investing in AI safety and ensuring adequate risk management procedures. The report reviews existing public policy and governance efforts and remaining gaps.
Keywords: AI, AI futures, AI safety, artificial intelligence
Creation-Date: 2024-11-14
Number: 27
Handle: RePEc:oec:comaaa:27-EN

Template-type: ReDIF-Paper 1.0
Author-Name: Margarita Almyranti
Author-Name: Eric Sutherland
Author-Name: Nachman Ash, Dr.
Author-Name: Samuel Eiszele
Title: Artificial Intelligence and the health workforce: Perspectives from medical associations on AI in health
Abstract: Healthcare has progressed through advancements in medicine, leading to improved global life expectancy. Nevertheless, the sector grapples with increasing challenges such as heightened demand, soaring costs, and an overburdened workforce. Factors contributing to health workforce strain include ageing populations, increasing burden from non-communicable and chronic diseases, healthcare providers’ burnout, and evolving patient expectations. Artificial Intelligence (AI) could potentially transform healthcare by alleviating some of these pressures. But AI in health poses risks to health providers through potential workforce disruption – with changing roles requiring adapted skills with some functions subject to automation. Striking a balance between innovation and safeguards is imperative.
Classification-JEL: I1; I10; I15; J21; J24; O33
Keywords: Artificial Intelligence, Doctors, Health, Health Data, Innovation, Medical Care, Productivity, Research, Skill Building, Technology, Training, Workforce
Creation-Date: 2024-11-20
Number: 28
Handle: RePEc:oec:comaaa:28-EN

Template-type: ReDIF-Paper 1.0
Author-Name: Francesco Filippucci
Author-Name: Peter Gal
Author-Name: Matthias Schief
Title: Miracle or Myth? Assessing the macroeconomic productivity gains from Artificial Intelligence
Abstract: The paper studies the expected macroeconomic productivity gains from Artificial Intelligence (AI) over a 10-year horizon. It builds a novel micro-to-macro framework by combining existing estimates of micro-level performance gains with evidence on the exposure of activities to AI and likely future adoption rates, relying on a multi-sector general equilibrium model with input-output linkages to aggregate the effects. Its main estimates for annual aggregate total-factor productivity growth due to AI range between 0.25-0.6 percentage points (0.4-0.9 pp. for labour productivity). The paper discusses the role of various channels in shaping these macro-level gains and highlights several policy levers to support AI's growth-enhancing effects.
Classification-JEL: E1; O3; O4; O5
Keywords: Artificial Intelligence, Productivity, Technology adoption
Creation-Date: 2024-11-22
Number: 29
Handle: RePEc:oec:comaaa:29-EN

Template-type: ReDIF-Paper 1.0
Author-Name: Flavio Calvino
Author-Name: Hélène Dernis
Author-Name: Lea Samek
Author-Name: Antonio Ughi
Title: A sectoral taxonomy of AI intensity
Abstract: This work proposes a sectoral taxonomy of AI intensity, outlining different dimensions that characterise the extent to which AI relates to the activity of economic sectors. Focusing on AI human capital, AI innovation, exposure to and use of AI, the taxonomy provides a novel multifaceted perspective and reveals significant heterogeneity across sectors and indicators. While some sectors, such as IT services, score high along all the dimensions considered, others, such as Pharmaceuticals, exhibit more considerable heterogeneity (high AI human capital but low AI innovation). The taxonomy can be a useful tool for future policy-relevant analyses aimed at exploring empirically the role of AI and the implications of its diffusion.
Classification-JEL: C81; O33; O34; J24
Keywords: Artificial Intelligence, Human Capital, Innovation
Creation-Date: 2024-12-12
Number: 30
Handle: RePEc:oec:comaaa:30-EN