• The paper assesses the public policy-cycle model in the age of artificial intelligence
• Practical cases are analyzed since the policy-making cycle to understand implementations of artificial intelligence toward a dynamic framework
• This work contributes to laying the foundations of the theoretical and practical relationship between artificial intelligence and public policy

Introduction

Nowadays, Artificial Intelligence (AI) has become a top priority on the agenda of different nations around the world. AI may lead to transformative applications within a wide range of industrial, intellectual and social applications, far beyond those generated by previous industrial revolutions (Dwivedi et al., 2019). Countries such as China, the United States of America, and most of the European Union have already implemented AI techniques to improve internal government processes, the provision of services and interaction with citizens, or have developed a national strategy for the implementation of AI (Craglia et al., 2018). AI has the potential to change different aspects of government, including processes, interaction with citizens, service delivery, decision-making and public policy design and evaluation (Sun & Medaglia, 2019; Valle-Cruz, 2019). In fact, AI could help humans in decision-making, in understanding and deriving meaningful results from complex big data nexus (Höchtl, Parycek, & Schöllhammer, 2016). However, selection, design, implementation, and use of AI in government requires the collaboration of different specialists in the field, implies massive automation of processes within government, and entails involving new professionals to promote AI implementation.

The scientific study of AI began in the 1940s, in the field of computer science. However, its application and consequences in the public sector have not been studied in depth. AI in government involves the design, building, use, and evaluation of intelligent algorithms, robotics, and computational techniques to improve the management of public agencies (Desouza, 2018). At present, few studies report that governments and institutions are responding to the demands of a rapidly-changing society. Some facts that governments need to consider are a product of the growing need and demand for public services, costs and time implementations, personalization, or quality of services for improving public value: AI has the potential to improve governments. AI-based technologies are promoting faster and more specific answers to increasingly complex societal problems.

The challenge of AI in the public policy-cycle, regarding this highly technological, changing and complex context, is to make public administrations faster, more efficient, precise, transparent, and responsive to the needs of citizens. However, we do not know what the implications of AI in the policy process are. Given how recent the trend is, many governments are waiting to understand AI applications and find important applications to their field of research (de Sousa, de Melo, Bermejo, Farias, & Gomes, 2019). Based on the current literature on AI in public policy, public administration, political science and departments for international relations, a selection of practical cases are studied through the lens of the policy-making cycle (Bridgman & Davis, 2003; Howlett, McConnell, & Perl, 2017; Howlett, Ramesh, & Perl, 2009). The objective is to understand the implementation of AI and work toward a dynamic framework. Therefore, the main objective of this investigation is to assess the public policy-cycle framework in the age of artificial intelligence (AI), regarding the actual and expected changes that these emerging technologies will bring about at different moments in the policy-making process.

The structure of the article is as follows: The second section shows concepts, techniques and theory related to AI. The aim here is to further our understanding of the relationship between the public policy framework and AI techniques. The third section describes AI in the context of public policy, considering potential opportunities, challenges, and negative implications. Furthermore, it analyses cases related to the public policy-cycle. The fourth section introduces the features of AI which seek to make the public policy-cycle (DPPC) more dynamic. The fifth section creates a basis for discussion. Finally, the last section summarizes the conclusion of the article.