How Learning About Harms Impacts the Optimal Rate of Artificial Intelligence Adoption / Joshua S. Gans.

Format:

Book

Author/Creator:

Gans, Joshua S.

Contributor:

National Bureau of Economic Research.

Series:

Working Paper Series (National Bureau of Economic Research) no. w32105.

NBER working paper series no. w32105

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2024.

Summary:

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.

Notes:

Print version record

February 2024.