The Dangers of AI Nationalism and Beggar-Thy-Neighbour Policies

As they attempt to nurture and govern AI, some nations are acting in ways that – with or without direct intent – discriminate among foreign market actors. For example, some governments are excluding foreign firms from access to incentives for high-speed computing, or requiring local content in the AI supply chain, or adopting export controls for the advanced chips that power many types of AI. If policy makers in country X can limit access to the building blocks of AI – whether funds, data or high-speed computing power – it might slow down or limit the AI prowess of its competitors in country Y and/or Z. At the same time, however, such policies could violate international trade norms of non-discrimination. Moreover, if policy makers can shape regulations in ways that benefit local AI competitors, they may also impede the competitiveness of other nations’ AI developers. Such regulatory policies could be discriminatory and breach international trade rules as well as long-standing rules about how nations and firms compete – which, over time, could reduce trust among nations. In this article, the author attempts to illuminate AI nationalism and its consequences by answering four questions:

– What are nations doing to nurture AI capacity within their borders?

– Are some of these actions trade distorting?

 – Are some nations adopting twenty-first century beggar thy neighbour policies?

– What are the implications of such trade-distorting actions?

The author finds that AI nationalist policies appear to help countries with the largest and most established technology firms across multiple levels of the AI value chain. Hence, policy makers’ efforts to dominate these sectors, as example through large investment sums or beggar thy neighbour policies are not a good way to build trust.

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G20 AI Strategies

Many nations have started to publish national AI strategies. Since Canada released its strategy in 2017, 11 other G20 members have followed suit, but each nation has approached its AI strategy differently. The Digital Trade and Data Governance Hub has examined these strategies and produced two documents. The first provides an overview summarizing the main goals of each national AI strategy, and the second provides an analysis of how each strategy deals with data governance with respect to AI. Our analysis shows that there is no one formula to achieve a national AI strategy. Thus, strategies range in focus and cover aspects such as funding, research, infrastructure, ethics, jobs, standards, and data governance. These documents are current as of August 2019 and will be updated every 6 months.

Below are the G20 countries with each country’s AI strategy.

A more in-depth look at G20 and their data governance strategies can be found in our G20 data governance overview.

Country AI Strategy
Argentina No dedicated plan
Australia 2018-2019 budget announced a four-year investment to support the development of AI in Australia
Brazil No dedicated plan
Canada Pan-Canadian Artificial Intelligence Strategy (2017)
China A Next Generation Artificial Intelligence Development Plan (2017)
France For a Meaningful Artificial Intelligence: Towards a French and European Strategy (2018)
Germany AI strategy (2018)
India National AI Strategy (2018)
Indonesia No dedicated plan
Italy White Paper on Artificial Intelligence at the service of citizens
Japan Artificial Intelligence Technology Strategy (2017)
Mexico Towards an AI Strategy in Mexico: Harnessing the AI Revolution (2018)
Republic of Korea Mid- to Long-Term Master Plan in Preparation for the Intelligent Information Society Managing the Fourth Industrial Revolution
Republic of South Africa No dedicated plan
Russia No dedicated plan
Saudi Arabia No dedicated plan
Turkey No dedicated plan
United Kingdom AI Sector Deal (2018)
United States American AI Initiative (2019)
European Union Communication on Artificial Intelligence (2018)

The Global Data Governance Mapping Project

Global Data Governance Mapping

Although data is the most collected, analyzed, shared, and traded good or service around the world, we know little about how various types of data are governed at the national and international level.

Continents

Regions

Countries plus the EU

Background and Project Rationale

Tim Berners-Lee, the architect of the World Wide Web, taught us that the Internet is a function of the choices we make about data. He argued that policies to govern the data that underpin the internet must be coherent, interoperable, accountable, and built on trust. Although data is the most collected, analyzed, shared, and traded good or service around the world, we know little about how various types of data are governed at the national and international level.  In this project, with help from a small team of researchers, we will map and assess laws and regulations focused on the governance of data. By so doing, we can gain a better understanding of innovative approaches to  data governance and what comprehensive data governance looks like.