Mikhaylov, Slava VJ and Esteve, Marc and Campion, Averill (2018) AI for the Public Sector: Opportunities and challenges of cross-sector collaboration. Philosophical Transactions A: Mathematical, Physical and Engineering Sciences, 376 (2128). p. 20170357. DOI https://doi.org/10.1098/rsta.2017.0357
Mikhaylov, Slava VJ and Esteve, Marc and Campion, Averill (2018) AI for the Public Sector: Opportunities and challenges of cross-sector collaboration. Philosophical Transactions A: Mathematical, Physical and Engineering Sciences, 376 (2128). p. 20170357. DOI https://doi.org/10.1098/rsta.2017.0357
Mikhaylov, Slava VJ and Esteve, Marc and Campion, Averill (2018) AI for the Public Sector: Opportunities and challenges of cross-sector collaboration. Philosophical Transactions A: Mathematical, Physical and Engineering Sciences, 376 (2128). p. 20170357. DOI https://doi.org/10.1098/rsta.2017.0357
Abstract
Public sector organisations 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 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 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 and challenges from AI for public sector. Finally, we propose a series of strategies to successfully manage these cross-sectoral collaborations.
Item Type: | Article |
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Uncontrolled Keywords: | cross - sector collaboration, data science, artificial intelligence, public policy |
Subjects: | J Political Science > JA Political science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources |
Divisions: | Faculty of Social Sciences Faculty of Social Sciences > Government, Department of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 05 Sep 2018 12:29 |
Last Modified: | 30 Oct 2024 17:07 |
URI: | http://repository.essex.ac.uk/id/eprint/22466 |
Available files
Filename: AI for the Public Sector.pdf