The future is likely to see an increase in the use of automated decision-making systems in the public sector, which employ Artificial Intelligence and, in particular, machine learning techniques, to enable more proactive and personalised delivery of public services. Proactive delivery can reduce the administrative burdens on citizens and government staff. While there is a small but growing body of literature that highlights the benefits of proactive public services, the implementation of such services is data intensive and can harm citizens beyond privacy concerns. Proactive service delivery requires high degrees of automated data processing using various data sources and algorithms that reduce the level of human control that both citizens and public officials have in verifying or correcting system errors. The purpose of this workshop is to initiate a discussion about proactive and personalised public services, discussing them and learning from the EGOV community about the practice of applying proactive and personalised services in different countries. This includes presenting an initial version of the developed framework for proactive and personalised public services, which is expected to provide further research directions.@en

The emergence of super-applications is a complete game changer in how future governments will deliver e-services and interact with their citizens. With respect to this, the scope of currently established e-government stage models is exhausted. Therefore, this article proposes a “provident stage” as an extension of the Layne and Lee stage model, that adequately addresses the rapid technological development and evolvement of mobile- and smart-government solutions. We argue that super-applications can drive the transformation of e-government towards a yet unforeseen quality level: smart government. This article discusses that transition process, the influence of mobile government solutions in this as well as emerging citizens’ expectations for modern government service delivery.@en