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While there is growing consensus that the analytical and cognitive tools of artificial intelligence (AI) have the potential to transform government in positive ways, it is also clear that AI challenges traditional government decision-making processes and threatens the democratic values within which they are framed. These conditions argue for conservative approaches to AI that focus on cultivating and sustaining public trust. We use the extended Brunswik lens model as a framework to illustrate the distinctions between policy analysis and decision making as we have traditionally understood and practiced them and how they are evolving in the current AI context along with the challenges this poses for the use of trustworthy AI. We offer a set of recommendations for practices, processes, and governance structures in government to provide for trust in AI and suggest lines of research that support them.

AbstractSystem dynamics depends heavily upon quantitative data to generate feedback models. Qualitative data and their analysis also have a central role to play at all levels of the modeling process. Although the classic literature on system dynamics strongly supports this argument, the protocols to incorporate this information during the modeling process are not detailed by the most influential authors. Data‐gathering techniques such as interviews and focus groups, and qualitative data analysis techniques such as grounded theory methodology and ethnographic decision models could have a strong, critical role in rigorous system dynamics efforts. This article describes some of the main qualitative, social science techniques and explores their suitability in the different stages of the modeling process. Additionally, the authors argue that the techniques described in the paper could contribute to the understanding of the modeling process, facilitate communication among modelers and clients, and set up a methodological framework to promote constructive discussion around the merits of qualitative versus quantitative modeling. Copyright © 2003 John Wiley & Sons, Ltd.

The field of performance management is premised on the centrality of measurement and performance information use in everyday decision making and practice. Information is managed through the use of information systems, but research shows that implementing these technological systems is not enough. This research responds to recent calls for a better understanding of performance information use and the role of dialogue among stakeholders in promoting learning and system change. Through case analysis and qualitative modeling, it proposes the concept of performance information artifacts, and the need for effective boundary spanners to promote effective learning and knowledge sharing in performance dialogue.

Purpose
In this paper, the authors compare the open government data (OGD) ecosystems of Mexico, Russia and the USA in an effort to extract some of the major points of similarity and differentiation between these countries and to trace how variations in these ecosystems may be related to context-specific historical problems and politics, particularly with regard to the possibility of sustained and institutionalized practice.


Design/methodology/approach
The authors take a comparative approach, using a set of concepts commonly applied to the three countries to analyze similarities and differences within this group. The authors gathered textual data and information, the searches for relevant documents guided by a set of concepts or criteria that are frequently used in studies of government's open data readiness assessment.


Findings
The authors conclude by focusing on the very different national exigencies that have given rise to open data ecosystems in the three countries, the variations in policy vehicles and implementation schemes that have instantiated open data practices within the three ecosystems and the common challenges that each country faces in institutionalizing OGD programs beyond the tenures of their current executives.


Originality/value
OGD is an information policy with near global relevance and increasing application. Practitioners and scholars alike have used the concept of an "ecosystem" to guide their approach to implementing this policy and to theorizing its scope and benefits. The international comparison is original and adds to the current understanding of an ecosystem approach to OGD.

This book provides a comprehensive approach to the study of policy analytics, modelling and informatics. It includes theories and concepts for understanding tools and techniques used by governments seeking to improve decision making through the use of technology, data, modelling, and other analytics, and provides relevant case studies and practical recommendations. Governments around the world face policy issues that require strategies and solutions using new technologies, new access to data and new analytical tools and techniques such as computer simulation, geographic information systems, and social network analysis for the successful implementation of public policy and government programs. Chapters include cases, concepts, methodologies, theories, experiences, and practical recommendations on data analytics and modelling for public policy and practice, and addresses a diversity of data tools, applied to different policy stages in several contexts, and levels and branches of government. This book will be of interest of researchers, students, and practitioners in e-government, public policy, public administration, policy analytics and policy informatics.