Artificial Intelligence, Surveillance, and Big Data

David Karpa, Torben Klarl and Michael Rochlitz

No 2108, Bremen Papers on Economics & Innovation from University of Bremen, Faculty of Business Studies and Economics

Abstract: The most important resource to improve technologies in the field of artificial intelligence is data. Two types of policies are crucial in this respect: privacy and data-sharing regulations, and the use of surveillance technologies for policing. Both types of policies vary substantially across countries and political regimes. In this paper, we examine how authoritarian and democratic political institutions can influence the quality of research in artificial intelligence, and the availability of large-scale datasets to improve and train deep learning algorithms. We focus mainly on the Chinese case, and find that - ceteris paribus - authoritarian political institutions continue to have a negative effect on innovation. They can, however, have a positive effect on research in deep learning, via the availability of large-scale datasets that have been obtained through government surveillance. We propose a research agenda to study which of the two effects might dominate in a race for leadership in artificial intelligence between countries with different political institutions, such as the United States and China.

Keywords: Artificial intelligence; political institutions; big data; surveillance; innovation; China (search for similar items in EconPapers)
JEL-codes: O25 O31 O38 P16 P51 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2021-11
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ino and nep-pay
References: View references in EconPapers View complete reference list from CitEc
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Published in Diginomics Research Perspectives - The Role of Digitalization in Business and Society, pp.145-172

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Related works:
Working Paper: Artificial Intelligence, Surveillance, and Big Data (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:atv:wpaper:2108

DOI: 10.26092/elib/1168

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