Machine learning in governments: Benefits, challenges and future directions

Abstract

The unprecedented increase in computing power and data availability has significantly altered the way and the scope that organizations make decisions relying on technologies. There is a conspicuous trend that organizations are seeking the use of frontier technologies with the purpose of helping the delivery of services and making day-to-day operational decisions. Machine learning (ML) is the fastest growing and at the same time, the most debated and controversial of these technologies. Although there is a great deal of research in the literature related to machine learning applications, most of them focus on the technical aspects or private sector use. The governmental machine learning applications suffer the lack of theoretical and empirical studies and unclear governance framework. This paper reviews the literature on the use of machine learning by government, aiming to identify the benefits and challenges of wider adoption of machine learning applications in the public sector and to propose the directions for future research.