Artificial intelligence (AI) is reshaping economies, promising to generate productivity gains, improve efficiency and lower costs. It contributes to better lives and helps people make better predictions and more informed decisions. These technologies, however, are still in their infancy, and there remains much promise for AI to address global challenges and promote innovation and growth. As AI’s impacts permeate our societies, its transformational power must be put at the service of people and the planet.

This book aims to help build a shared understanding of artificial intelligence (AI) in the present and near term. The book maps the economic and social impacts of AI technologies and applications and their policy implications, presenting evidence and policy options. It is also intended to help co‑ordination and consistency with discussions in other international fora, notably the G7, the G20, the European Union and the United Nations.

The artificial intelligence (AI) technical landscapehas evolvedsignificantly from 1950 when Alan Turing first posed the question of whether machines can think. Coined as a term in 1956, AI has evolved from symbolic AI where humans built logic-based systems, through the AI “winter” of the 1970s to the chess-playing computer Deep Blue in the 1990s. Since 2011, breakthroughs in “machine learning” (ML), an AI subset that uses a statistical approach, have been improving machines ability to make predictions from historical data. The maturity of a ML modelling technique called “neural networks”, along with large datasets and computing power, is behind the expansion in AI development.

This chapter characterises the “artificial intelligence (AI) technical landscape”, which has evolved significantly from 1950 when Alan Turing first posed the question of whether machines can think. Since 2011, breakthroughs have taken place in the subset of AI called “machine learning”, in which machines leverage statistical approaches to learn from historical data and make predictions in new situations. The maturity of machine-learning techniques, along with large datasets and increasing computational power are behind the current expansion of AI. This chapter also provides a high-level understanding of an AI system, which predicts, recommends or decides an outcome to influence the environment. In addition, it details a typical AI system lifecycle from i) design, data and models, including planning and design, data collection and processing and model building and interpretation; ii) verification and validation; iii) deployment; to iv) operation and monitoring. Lastly, this chapter proposes a research taxonomy to support policy makers.

This chapter describes the economic characteristics of artificial intelligence (AI) as an emerging general-purpose technology with the potential to lower the cost of prediction and enable better decisions. Through less expensive and more accurate predictions, recommendations or decisions, AI promises to generate productivity gains, improve well-being and help address complex challenges. Speed of adoption varies across companies and industries, since leveraging AI requires complementary investments in data, skills, digitalisation of workflows and the capacity to adapt organisational processes. Additionally, AI has been a growing target area for investment and business development. Private equity investment in AI start-ups has accelerated since 2016, doubling from 2016 to 2017 to reach USD 16 billion. AI start-ups attracted 12% of worldwide private equity investments in the first half of 2018, a significant increase from just 3% in 2011. Investment in AI technologies is expected to continue its upward trend as these technologies mature.

This chapter illustrates opportunities in several sectors where artificial intelligence (AI) technologies are seeing rapid uptake, including transport, agriculture, finance, marketing and advertising, science, healthcare, criminal justice, security the public sector, as well as in augmented and virtual reality applications. In these sectors, AI systems can detect patterns in enormous volumes of data and model complex, interdependent systems to generate outcomes that improve the efficiency of decision making, save costs and enable better resource allocation. The section on AI in transportation was developed by the Massachusetts Institute of Technology’s Internet Policy Research Institute. Several sections build on work being undertaken across the OECD, including the Committee on Digital Economy Policy and its Working Party on Privacy and Security, the Committee for Scientific and Technological Policy, the e-leaders initiative of the Public Governance Committee, as well as the Committee on Consumer Policy and its Working Party on Consumer Product Safety.

This chapter explores public policy considerations to ensure that artificial intelligence (AI) systems are trustworthy and human-centred. It covers concerns related to ethics and fairness; the respect of human democratic values, including privacy; and the dangers of transferring existing biases from the analogue world into the digital world, including those related to gender and race. The need to progress towards more robust, safe, secure and transparent AI systems with clear accountability mechanisms for their outcomes is underlined. Policies that promote trustworthy AI systems include those that encourage investment in responsible AI research and development; enable a digital ecosystem where privacy is not compromised by a broader access to data; enable small and medium-sized enterprises to thrive; support competition, while safeguarding intellectual property; and facilitate transitions as jobs evolve and workers move from one job to the next.

Artificial intelligence (AI) policies and initiatives are gaining momentum in governments, companies, technical organisations, civil society and trade unions. Intergovernmental initiatives on AI are also emerging. This chapter collects AI policies, initiatives and strategies from different stakeholders at both national and international levels around the world. It finds that, in general, national government initiatives focus on using AI to improve productivity and competitiveness with actions plans to strengthen: i) factor conditions such as AI research capability; ii) demand conditions; iii) related and supporting industries; iv) firm strategy, structure and competition; as well as v) domestic governance and co-ordination. International initiatives include the OECD Recommendation of the Council on Artificial Intelligence, which represents the first intergovernmental policy guidelines for AI and identifies principles and policy priorities for responsible stewardship of trustworthy AI.