TY - JOUR
T1 - Artificial intelligence for the public sector
T2 - Opportunities and challenges of cross-sector collaboration
AU - Mikhaylov, Slava Jankin
AU - Esteve Laporta, M.
AU - Campion, Averill
N1 - Funding Information:
Data accessibility. The article has no supporting data. Authors’ contributions. All authors contributed equally to all stages of study design and drafting the manuscript. All authors gave final approval for publication. Competing interests. There are no competing interests. Funding. The study is funded by HEFCE Catalyst Fund #E10, and the MINECO CSO2016-80823-P fund.
Publisher Copyright:
© 2018 The Author(s) Published by the Royal Society. All rights reserved.
PY - 2018
Y1 - 2018
N2 - Public sector organizations are increasingly interested in using data science and artificial intelligence capabilities to deliver policy and generate efficiencies in high-uncertainty environments. The long-term success of data science and artificial intelligence (AI) in the public sector relies on effectively embedding it into delivery solutions for policy implementation. However, governments cannot do this integration of AI into public service delivery on their own. The UK Government Industrial Strategy is clear that delivering on the AI grand challenge requires collaboration between universities and the public and private sectors. This cross-sectoral collaborative approach is the norm in applied AI centres of excellence around the world. Despite their popularity, cross-sector collaborations entail serious management challenges that hinder their success. In this article we discuss the opportunities for and challenges of AI for the public sector. Finally, we propose a series of strategies to successfully manage these cross-sectoral collaborations. This article is part of a discussion meeting issue 'The growing ubiquity of algorithms in society: implications, impacts and innovations'.
AB - Public sector organizations are increasingly interested in using data science and artificial intelligence capabilities to deliver policy and generate efficiencies in high-uncertainty environments. The long-term success of data science and artificial intelligence (AI) in the public sector relies on effectively embedding it into delivery solutions for policy implementation. However, governments cannot do this integration of AI into public service delivery on their own. The UK Government Industrial Strategy is clear that delivering on the AI grand challenge requires collaboration between universities and the public and private sectors. This cross-sectoral collaborative approach is the norm in applied AI centres of excellence around the world. Despite their popularity, cross-sector collaborations entail serious management challenges that hinder their success. In this article we discuss the opportunities for and challenges of AI for the public sector. Finally, we propose a series of strategies to successfully manage these cross-sectoral collaborations. This article is part of a discussion meeting issue 'The growing ubiquity of algorithms in society: implications, impacts and innovations'.
KW - Artificial intelligence
KW - Cross-sector collaboration
KW - Data science
KW - Public policy
UR - http://www.scopus.com/inward/record.url?scp=85060237501&partnerID=8YFLogxK
U2 - 10.1098/rsta.2017.0357
DO - 10.1098/rsta.2017.0357
M3 - Article
C2 - 30082303
AN - SCOPUS:85060237501
SN - 1364-503X
VL - 376
JO - Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
JF - Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
IS - 2128
M1 - 20170357
ER -