Centre for Digital Governance
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Many governments have problems with developing digital government services in an effective and efficient manner. One proposed solution to improve governmental digital service development is for governments to utilize agile development methods. However, there is currently a lack of understanding on two important and related topics. First, whether or not agile development might help to overcome common failure reasons of digital government projects. Second, what the challenges and success factors of agile digital government service development are. This paper addresses both of these gaps. By gathering insights from six cases where agile methods were used in the development of new digital services, it identifies five core categories of challenges and success factors encountered when utilizing agile development methods for governmental digital service development: organizational, methodological, end-user-related, technological, and regulatory. Furthermore, based on these findings, it makes initial recommendations on when and how to best use agile methods for digital government service development.
Open government data, as a phenomena, may be considered an important and influential innovation that has the potential to drive the creation of public value via enabling the prevention of corruption, increase in accountability and transparency, and driving the co-creation of new and innovative services. However, in order for open government data to be fully taken advantage of, it must be found, understood, and used. Though many countries maintain open government data portals, the usability of said portals can vary greatly; this is important to understand as the usability of a portal likely impacts the eventual reuse of the data made available there. Acknowledging the importance of portal usability to the data reuse process, this paper helps to elucidate some initial insights by asking two questions: “How can the usability of open government data portals be evaluated and compared across contexts?” and “What are the most commonly missing usability aspects from open government data portals?”. In order to answer these research questions, a subset of 41 national open government data portals were selected for an in-depth usability analysis drawing on the feedback from 40 individual users. As a result of this analysis, the paper is able to make three primary contributions: (1) the validation of a framework for open government data portal usability analysis, (2) develops an initial comparative international ranking of open government data portal usability, and (3) identifies commonly occurring portal usability strengths and weaknesses across contexts.
There is an increased interest amongst governments and public sector organisations about how to best integrate artificial intelligence into their day-to-day business processes. Yet, a large majority of technical know how is concentrated within the private sector, requiring most public sector organisations to rely on public procurement for AI systems. While many governments may have experience with traditional forms of public technological procurement, this paper argues that the public procurement of AI is different and new insight is needed to understand this differentiation and procure AI better. This paper offers an initial contribution to the public administration and management literature by describing this difference, and identifying the challenges associated with the public procurement of AI. In order to achieve this contribution, the research studied guidelines in four European countries (Estonia, Netherlands, Serbia, and the United Kingdom) to generate insight into the challenges faced, and potential solutions to these challenges, during the public procurement of AI process.
The rapid pace of technological advancement and innovation has put governance and regulatory mechanisms to the test. There is a clear need for new and innovative regulatory mechanisms that enable governments to successfully manage the integration of such technologies into our societies and ensure that such integration occurs in a sustainable, beneficial, and just manner. Artificial Intelligence stands out as one of the most debated such innovations. What exactly is it, how should it be built, how can it be used, and how and should it be regulated? Yet, in this debate, AI is becoming widely utilized within both existing, evolving, and bespoke regulatory contexts. The present chapter explores in particular what is arguably the most successful AI regulatory approach to date, that of the European Union. We explore core definitional concepts, shared understandings, values, and approaches currently in play. We argue that due to the so-called ‘Brussels effect’, regulatory initiatives within the European Union have a much broader global impact and, therefore, warrant close inspection.
In today's world, disasters, both natural and manmade, are becoming increasingly frequent, and new solutions are of a compelling need to provide and disseminate information about these disasters to the public and concerned authorities in an effective and efficient manner. One of the most frequently used ways for information dissemination today is through social media, and when it comes to real-time information, Twitter is often the channel of choice. Thus, this paper discusses how Big Data Analytics (BDA) can take advantage of information streaming from Twitter to generate alerts and provide information in real-time on ongoing disasters. The paper proposes TAGS (Twitter Alert Generation System), a novel solution for collecting and analyzing social media streaming data in realtime and subsequently issue warnings related to ongoing disasters using a combination of Hadoop and Spark frameworks. The paper tests and evaluates the proposed solution using Twitter data from the 2018 earthquake in Palu City, Sulawesi, Indonesia. The proposed architecture was able to issue alert messages on various disaster scenarios and identify critical information that can be utilized for further analysis. Moreover, the performance of the proposed solution is assessed with respect to processing time and throughput that shows reliable system efficiency.