AI-Driven Productivity Scenarios

Technological development and diffusion are rapidly transforming economic, financial and societal systems. Policy and legal frameworks may be unable to keep pace with approaches that maximize opportunities and manage risk, and advancing artificial intelligence (AI) technologies, combined with weak economic growth projections, create an urgent need to better understand AI-driven scenarios and the implications for productivity.

In this special report, Paul Samson, S. Yash Kalash and Nikolina Zivkovic identify three key conduits (drawing on established AI-related indices) that link actions and improved productivity outcomes: technological capabilities; applications and markets; and policy and regulation. They then provide a country-level assessment of G7 countries (plus China and India) to outline a current snapshot of AI-driven productivity in each and to inform plausible futures for AI’s impact on productivity. They also examine four scenarios for the potential impact of AI on global productivity: flat AI; US-led AI; multipolar AI; and artificial general intelligence. These scenarios broaden the scope for G7 members to consider key areas and actions that could boost productivity under different circumstances.

In this commentary, first published by the Italian Institute for International Political Studies, Alex He discusses China’s changing approach to digital trade and its strategy on global digital trade collaboration.

He writes that while China’s cross-border e-commerce continues to rapidly expand, Beijing has broadened its approach to focus more comprehensively on digitally deliverable trade, “acknowledging its importance in the context of rapid technological advancement.” As well, China’s applications to join the Comprehensive and Progressive Agreement for Trans-Pacific Partnership and the Digital Economy Partnership Agreement “highlight its ambition to play a larger role in shaping international digital trade agreements and to expand its digital trade with developed economies.”

The Digital Policy Hub at CIGI is a collaborative space for emerging scholars and innovative thinkers from the social, natural and applied sciences. Here are the most recent working papers published by Hub fellows.

Sophie Xiaoyi Liu: “The Digital Ecology of Hate: Technology, Policy and Online Fields”

Shirley Anne Scharf: “Digital Regulation and Innovation in Sweden, Korea and Canada”

Follow the links on the Hub webpage to learn more about the Hub scholars and their work.

“Precrime prediction” is no longer limited to the realm of science fiction. As Jennifer Tang and Kyle Hiebert describe in this commentary, “police departments worldwide are already embracing AI-powered tools to advance the field of predictive policing. And some authoritarian regimes have openly declared their intent to use these technologies to eliminate crime altogether.”

Tang and Hiebert write that “the promise of these technologies masks a series of profound ethical dilemmas” and that “without adequate protections or guardrails in place, even in the hands of the well-meaning, the technologies and practices involved could open the door to a dystopian future of ubiquitous state surveillance. And it is not hard to imagine how, in the hands of vengeful and paranoid actors in power, these same predictive tools could be used to quash even the most nascent opposition.”