How Learning About Harms Impacts the Optimal Rate of Artificial Intelligence Adoption

22 Pages Posted: 5 Feb 2024

See all articles by Joshua Gans

Joshua Gans

University of Toronto - Rotman School of Management

Date Written: February 2024

Abstract

This paper examines recent proposals and research suggesting that AI adoption should be delayed until its potential harms are properly understood. It is shown that conclusions regarding the social optimality of delayed AI adoption are sensitive to assumptions regarding the process by which regulators learn about the salience of particular harms. When such learning is by doing -- based on the real-world adoption of AI -- this generally favours acceleration of AI adoption to surface and react to potential harms more quickly. This case is strengthened when AI adoption is potentially reversible. The paper examines how different conclusions regarding the optimality of accelerated or delayed AI adoption influence and are influenced by other policies that may moderate AI harm.

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Suggested Citation

Gans, Joshua, How Learning About Harms Impacts the Optimal Rate of Artificial Intelligence Adoption (February 2024). NBER Working Paper No. w32105, Available at SSRN: https://ssrn.com/abstract=4716668

Joshua Gans (Contact Author)

University of Toronto - Rotman School of Management ( email )

105 St. George Street
Toronto, Ontario M5S 3E6 M5S1S4
Canada

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