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.

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