HOW TO MEASURE AI: TRENDS, CHALLENGES AND IMPLICATIONS

Yulia Turovets (), Konstantin Vishnevskiy () and Artem Altynov
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Yulia Turovets: National Research University Higher School of Economics
Konstantin Vishnevskiy: National Research University Higher School of Economics

HSE Working papers from National Research University Higher School of Economics

Abstract: How do comparable and similar indicators to measure artificial intelligence (AI) look across countries? In answering this question, our study addresses two main aims. Firstly, the paper introduces a holistic approach as operational tool to measure AI development (supply side) and adoption (demand side), which covers AI definition, AI technologies taxonomy, and a set of indicators. Secondly, the suggested methodology combines several sources of information like survey, bibliometric, and patent analysis. Next, by analyzing the results of a pilot survey and calculations, the reliability of indicators and a tentative assessment the state of the art of AI development and adoption in Russia is provided. Taking into consideration the complex nature of AI, the study represents a number of baseline parameters that give an overview of AI progress on a country level. The next step will be an elaboration of detailed indicators that at capture AI characteristics to a greater extent in different economic sectors

Keywords: artificial intelligence; AI definition; digital technologies; indicators; measurement. (search for similar items in EconPapers)
JEL-codes: O33 O38 (search for similar items in EconPapers)
Pages: 47 pages
Date: 2020
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Published in WP BRP Series: Science, Technology and Innovation / STI, November 2020, pages 1-47

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