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’.

Big Data and artificial intelligence will have a profound transformational impact on governments around the world. Thus, it is important for scholars to provide a useful analysis on the topic to public managers and policymakers. This study offers an in-depth review of the Policy and Administration literature on the role of Big Data and advanced analytics in the public sector. It provides an overview of the key themes in the research field, namely the application and benefits of Big Data throughout the policy process, and challenges to its adoption and the resulting implications for the public sector. It is argued that research on the subject is still nascent and more should be done to ensure that the theory adds real value to practitioners. A critical assessment of the strengths and limitations of the existing literature is developed, and a future research agenda to address these gaps and enrich our understanding of the topic is proposed.

Public Service Motivation (PSM) is a topic that has generated considerable interest among Public Administration scholars. Research on PSM has mainly focused on either defining what PSM is and how this construct can be measured or on testing how PSM affects individual and organizational variables. However, very little is known about how the motivation to serve the public interest is influenced by personality. We evaluate the psychological antecedents of PSM by distinguishing two classes of motives behind PSM: affective versus nonaffective motives. Our analysis of data from responses to two independent questionnaires by 320 undergraduate students reveals that PSM is strongly influenced by core personality traits. Our results suggest that affective motives of PSM—Compassion (COM) and Self-Sacrifice (SS)—are positively influenced by the personality traits of Honesty-Humility, Emotionality, and Agreeableness, and negatively by Conscientiousness. In contrast, nonaffective PSM motives—Attraction to Policy-Making (APM) and Commitment to the Public Interest (CPI)—are positively associated with the Openness to Experience trait.