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In the quest for Smart City (SC) development, numerous examples of ‘good practices’ have circulated in national and international policy arenas. Learning from good practices elsewhere is a common approach for cities to initiate and develop SC policies of their own. Nevertheless, because of political, legal and cultural differences across countries and cities, policies will always be context dependent, and prosper under specific conditions. There is a vast literature on policy transfer and policy mobility, but much of it utilizes different concepts (i.e., policy translation, policy learning, and policy diffusion). Nonetheless, a critical omission they all share is limited concern for context-dependence and lack of prescriptive clues. Addressing both omissions would lead to a framework in which learning from good SC policy practices, formulating lessons, transferring them, and then adjusting them to fit the recipient's needs is taken up systematically in a stepwise manner. To develop a theoretical framework for Smart City adoption, this study brings together variegated existing literature under the heading ‘policy transplantation’ and synthesizes existing insights into a prescriptive procedure policymakers can follow. A systematic literature review is conducted to identify all key elements and sub-elements associated with SC policy transplantation, leading to a theoretical framework. This (prescriptive) theoretical framework is subsequently validated using an expert group and illustrated through a real-life case. The framework can be used as an analytical lens by researchers, but also constitutes a practical tool to guide policymakers aiming to use insights from good practices and implement them in line with their own contextual setting. Conducting contextual assessment before transplanting a SC policy is essential.

Artificial Intelligence and algorithms are increasingly able to replace human workers in cognitively sophisticated tasks, including ones related to justice. Many governments and international organizations are discussing policies related to the application of algorithmic judges in courts. In this paper, we investigate the public perceptions of algorithmic judges. Across two experiments (N = 1,822), and an internal meta-analysis (N = 3,039), our results show that even though court users acknowledge several advantages of algorithms (i.e., cost and speed), they trust human judges more and have greater intentions to go to the court when a human (vs. an algorithmic) judge adjudicates. Additionally, we demonstrate that the extent that individuals trust algorithmic and human judges depends on the nature of the case: trust for algorithmic judges is especially low when legal cases involve emotional complexities (vs. technically complex or uncomplicated cases).

Many countries adhere to the Organisation for Economic Co-operation and Development creed that innovation is good for the economy. Experiments are often used to intentionally create space for innovation. Decisions allowing experiments result in temporary legal enclaves for a few, excluding many others. Therefore, they come with risks. The aim of this article is to provide a set of guidelines that help improve the legal resilience of experimentation policies, so they are better able to withstand legal attacks when they occur. To do so, we first arranged the existing diversity of legal experiments in a theoretical model. Special attention was paid to two archetypes of legal experiments: statutory experiments and regulatory sandboxes. Second, we analyzed the impact of both types of experiments on four core legal principles: legality, certainty, equality, and public accountability. From this assessment, we eventually formulated a set of guidelines to secure or improve legal resilience.