Emerging technologies are both a cause of many grand societal challenges (GSCs) facing twenty-first-century societies and an integral part of some of their most promising solutions. As an element of the GSCs, technology becomes intertwined with several interrelated issues that constitute the GSCs. This calls for approaches to Technology Assessment (TA) that account for the paradoxical role of technology in the GSCs, and the imperative and complexity of pointing technological innovation toward addressing the GSCs. In this introduction to the special issue, we identify three major streams in TA research and practice, namely TA as a policy instrument, a deliberation process, and an issue field. These streams highlight tensions between relying on experts and on the inclusion of various stakeholders in TA processes, and between a TA framing around the intersection of technology and critical issues around critical issues, such as those constituting the GSCs. We discuss the advantages and challenges of each stream. We also outline and discuss key principles for conducting TA in the context of GSCs. We end by introducing the four papers that constitute this special issue.@en

Computational artificial intelligence (AI) algorithms are increasingly used to support decision making by governments. Yet algorithms often remain opaque to the decision makers and devoid of clear explanations for the decisions made. In this study, we used an experimental approach to compare decision making in three situations: humans making decisions (1) without any support of algorithms, (2) supported by business rules (BR), and (3) supported by machine learning (ML). Participants were asked to make the correct decisions given various scenarios, while BR and ML algorithms could provide correct or incorrect suggestions to the decision maker. This enabled us to evaluate whether the participants were able to understand the limitations of BR and ML. The experiment shows that algorithms help decision makers to make more correct decisions. The findings suggest that explainable AI combined with experience helps them detect incorrect suggestions made by algorithms. However, even experienced persons were not able to identify all mistakes. Ensuring the ability to understand and traceback decisions are not sufficient for avoiding making incorrect decisions. The findings imply that algorithms should be adopted with care and that selecting the appropriate algorithms for supporting decisions and training of decision makers are key factors in increasing accountability and transparency.

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Data seldom create value by themselves. They need to be linked and combined from multiple sources, which can often come with variable data quality. The task of improving data quality is a recurring challenge. In this paper, we use a case study of a large telecom company to develop a generic process pattern model for improving data quality. The process pattern model is defined as a proven series of activities, aimed at improving the data quality given a certain context, a particular objective, and a specific set of initial conditions. Four different patterns are derived to deal with the variations in data quality of datasets. Instead of having to find the way to improve the quality of big data for each situation, the process model provides data users with generic patterns, which can be used as a reference model to improve big data quality.

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The right to know under the Freedom of Information (FOI) Act in the UK has made public authorities as the duty-bearer, often making them to selectively decouple practices from policies. This has resulted in disclosing data that may derail from the intended goals of open government. By analyzing the top fifty requesters who made 34,314 requests, we examine how the same requests can result in varying responses. Our preliminary findings suggest four implementation blind spots. The first entails data disclosure that contravene privacy and the second disclosure can potentially jeopardize the long-standing stakeholder relationship. Whereas the last two types withhold information despite it is in the public interests. The findings offer a counterintuitive insight that public authorities are willing to disclose information in support of transparency and accountability and to withhold information that is not in the public interests. We find the opposite with private pursuits superseding public interests.@en

Big data is driving the use of algorithm in governing mundane but mission-critical tasks. Algorithms seldom operate on their own and their (dis)utilities are dependent on the everyday aspects of data capture, processing and utilization. However, as algorithms become increasingly autonomous and invisible, they become harder for the public to detect and scrutinize their impartiality status. Algorithms can systematically introduce inadvertent bias, reinforce historical discrimination, favor a political orientation or reinforce undesired practices. Yet it is difficult to hold algorithms accountable as they continuously evolve with technologies, systems, data and people, the ebb and flow of policy priorities, and the clashes between new and old institutional logics. Greater openness and transparency do not necessarily improve understanding. In this editorial we argue that through unravelling the imperceptibility, materiality and governmentality of how algorithms work, we can better tackle the inherent challenges in the curatorial practice of data and algorithm. Fruitful avenues for further research on using algorithm to harness the merits and utilities of a computational form of technocratic governance are presented.@en

The value of data as a new economic asset class is seldom realized on its own. With less reliance on self-administered survey, it offers new insights into behaviors and patterns. Yet, it involves a huge undertaking of bringing together multiple actors from different disciplines and diverse practices to examine the underexplored relationships between types of data. There are different inquiry systems and research cycles to make sense out of big and open data (BOLD). We argue that deploying theories from diverse disciplines, and considering using different inquiry systems and research cycles, offers a more disciplined and robust methodological approach. This allows us to break through the limits of backward induction from the evidence by moving back and forward in exploring the unknown through BOLD. As such, we call for developing a variety of rigorous approaches to counterbalance the current theory-free practice in the analysis and use of BOLD.@en