TY - JOUR
T1 - Comparing the Emergence of Technical and Social Sciences Research in Artificial Intelligence
AU - Linkov, Igor
AU - Rand, Krista
AU - Bassett, Jason
AU - Galaitsi, Stephanie
AU - Trump, Benjamin
AU - Jayabalasingham, Bamini
AU - Collins, Tom
N1 - Funding Information:
The authors would like to thank Philipp Lorenz-Spreen, Andreas Koher for fruitful discussions and their analytical comments on methodological approaches, as well as Miriam Pollock for their diligent proof-reading of the manuscript and constructive remarks. Further, Maksim Kitsak, Jeff Keisler for their constructive commenting of the manuscript and structural suggestions, as well as Jeff Cegan. The views and opinions expressed in this article are those of the individual authors and not those of the U.S. Army or other sponsor organizations. Funding. This study was funded in parts by the US Army Corps of Engineers.
Funding Information:
This study was funded in parts by the US Army Corps of Engineers.
Publisher Copyright:
© Copyright © 2021 Ligo, Rand, Bassett, Galaitsi, Trump, Jayabalasingham, Collins and Linkov.
PY - 2021/4/26
Y1 - 2021/4/26
N2 - Applications of Artificial Intelligence (AI) can be examined from perspectives of different disciplines and research areas ranging from computer science and security, engineering, policymaking, and sociology. The technical scholarship of emerging technologies usually precedes the discussion of their societal implications but can benefit from social science insight in scientific development. Therefore, there is an urgent need for scientists and engineers developing AI algorithms and applications to actively engage with scholars in the social sciences. Without collaborative engagement, developers may encounter resistance to the approval and adoption of their technological advancements. This paper reviews a dataset, collected by Elsevier from the Scopus database, of papers on AI application published between 1997 and 2018, and examines how the co-development of technical and social science communities has grown throughout AI's earliest to latest stages of development. Thus far, more AI research exists that combines social science and technical explorations than AI scholarship of social sciences alone, and both categories are dwarfed by technical research. Moreover, we identify a relative absence of AI research related to its societal implications such as governance, ethics, or moral implications of the technology. The future of AI scholarship will benefit from both technical and social science examinations of the discipline's risk assessment, governance, and public engagement needs, to foster advances in AI that are sustainable, risk-informed, and societally beneficial.
AB - Applications of Artificial Intelligence (AI) can be examined from perspectives of different disciplines and research areas ranging from computer science and security, engineering, policymaking, and sociology. The technical scholarship of emerging technologies usually precedes the discussion of their societal implications but can benefit from social science insight in scientific development. Therefore, there is an urgent need for scientists and engineers developing AI algorithms and applications to actively engage with scholars in the social sciences. Without collaborative engagement, developers may encounter resistance to the approval and adoption of their technological advancements. This paper reviews a dataset, collected by Elsevier from the Scopus database, of papers on AI application published between 1997 and 2018, and examines how the co-development of technical and social science communities has grown throughout AI's earliest to latest stages of development. Thus far, more AI research exists that combines social science and technical explorations than AI scholarship of social sciences alone, and both categories are dwarfed by technical research. Moreover, we identify a relative absence of AI research related to its societal implications such as governance, ethics, or moral implications of the technology. The future of AI scholarship will benefit from both technical and social science examinations of the discipline's risk assessment, governance, and public engagement needs, to foster advances in AI that are sustainable, risk-informed, and societally beneficial.
KW - AI
KW - machine learning
KW - review – systematic
KW - risk
KW - taxonomy
UR - http://www.scopus.com/inward/record.url?scp=85116056049&partnerID=8YFLogxK
U2 - 10.3389/fcomp.2021.653235
DO - 10.3389/fcomp.2021.653235
M3 - Review article
VL - 3
JO - Frontiers of Computer Science
JF - Frontiers of Computer Science
SN - 2095-2228
M1 - 653235
ER -