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

作者：Mikhaylov Slava Jankin^*; Esteve Marc; Campion Averill

来源：Philosophical Transactions of the Royal Society A-Mathematical Physical and Engineering Sciences, 2018, 376(2128): 20170357.

DOI：10.1098/rsta.2017.0357

• cross-sector collaboration
• data science
• artificial intelligence
• public policy

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'.

• 出版日期2018-9-13