Artificial Intelligence for the Public Sector: Opportunities and challenges of cross-sector collaboration
September 12, 2018 Β· Declared Dead Β· π Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
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Authors
Slava Jankin Mikhaylov, Marc Esteve, Averill Campion
arXiv ID
1809.04399
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.CY
Citations
205
Venue
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Last Checked
3 months ago
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.
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