Literature DB >> 30082303

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

Slava Jankin Mikhaylov1, Marc Esteve2,3, Averill Campion3.   

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'.
© 2018 The Author(s).

Keywords:  artificial intelligence; cross-sector collaboration; data science; public policy

Year:  2018        PMID: 30082303      PMCID: PMC6107541          DOI: 10.1098/rsta.2017.0357

Source DB:  PubMed          Journal:  Philos Trans A Math Phys Eng Sci        ISSN: 1364-503X            Impact factor:   4.226


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Authors:  F K Sonnenberg
Journal:  J Bus Strategy       Date:  1992 May-Jun

2.  DETERMINANTS OF NETWORK OUTCOMES: THE IMPACT OF MANAGEMENT STRATEGIES.

Authors:  Tamyko Ysa; Vicenta Sierra; Marc Esteve
Journal:  Public Adm       Date:  2014-04-03
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1.  Application of Artificial Intelligence in COVID-19 Pandemic: Bibliometric Analysis.

Authors:  Md Mohaimenul Islam; Tahmina Nasrin Poly; Belal Alsinglawi; Li-Fong Lin; Shuo-Chen Chien; Ju-Chi Liu; Wen-Shan Jian
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Review 2.  Governing Ethical AI Transformation: A Case Study of AuroraAI.

Authors:  Jaana Leikas; Aditya Johri; Marko Latvanen; Nina Wessberg; Antti Hahto
Journal:  Front Artif Intell       Date:  2022-02-10

3.  The growing ubiquity of algorithms in society: implications, impacts and innovations.

Authors:  S C Olhede; P J Wolfe
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2018-09-13       Impact factor: 4.226

  3 in total

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