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Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration

Mikhaylov, SJ; Esteve, M; Campion, A; (2018) Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration. Philosophical Transactions of The Royal Society A - Mathematical, Physical and Engineering Sciences , 376 (2128) 10.1098/rsta.2017.0357. Green open access

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Abstract

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’.

Type: Article
Title: Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration
Open access status: An open access version is available from UCL Discovery
DOI: 10.1098/rsta.2017.0357
Publisher version: https://doi.org/10.1098/rsta.2017.0357
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Science & Technology, Multidisciplinary Sciences, Science & Technology - Other Topics, cross-sector collaboration, data science, artificial intelligence, public policy, PRIVATE JOINT VENTURES, MANAGEMENT, PERFORMANCE, LEADERSHIP, NETWORKS, SERVICE, IMPLEMENTATION, PARTNERSHIPS, GOVERNANCE, DIVERSITY
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Political Science
URI: https://discovery.ucl.ac.uk/id/eprint/10060001
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