Purpose: This paper formalizes long-term trajectories of human civilization as a scientific and ethical field of study. The long-term trajectory of human civilization can be defined as the path that human civilization takes during the entire future time period in which human civilization could continue to exist. Approach: We focus on four types of trajectories: status quo trajectories, in which human civilization persists in a state broadly similar to its current state into the distant future; catastrophe trajectories, in which one or more events cause significant harm to human civilization; technological transformation trajectories, in which radical technological breakthroughs put human civilization on a fundamentally different course; and astronomical trajectories, in which human civilization expands beyond its home planet and into the accessible portions of the cosmos. Findings: Status quo trajectories appear unlikely to persist into the distant future, especially in light of long-term astronomical processes. Several catastrophe, technological transformation, and astronomical trajectories appear possible. Value: Some current actions may be able to affect the long-term trajectory. Whether these actions should be pursued depends on a mix of empirical and ethical factors. For some ethical frameworks, these actions may be especially important to pursue.

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Can effective international governance for artificial intelligence remain fragmented, or is there a need for a centralised international organisation for AI? We draw on the history of other international regimes to identify advantages and disadvantages in centralising AI governance. Some considerations, such as efficiency and political power, speak in favour of centralisation. Conversely, the risk of creating a slow and brittle institution speaks against it, as does the difficulty in securing participation while creating stringent rules. Other considerations depend on the specific design of a centralised institution. A well-designed body may be able to deter forum shopping and ensure policy coordination. However, forum shopping can be beneficial and a fragmented landscape of institutions can be self-organising. Centralisation entails trade-offs and the details matter. We conclude with two core recommendations. First, the outcome will depend on the exact design of a central institution. A well-designed centralised regime covering a set of coherent issues could be beneficial. But locking-in an inadequate structure may pose a fate worse than fragmentation. Second, for now fragmentation will likely persist. This should be closely monitored to see if it is self-organising or simply inadequate.

This article introduces the concept of Artificial Intelligence (AI) to a criminological audience. After a general review of the phenomenon (including brief explanations of important cognate fields such as ‘machine learning’, ‘deep learning’, and ‘reinforcement learning’), the paper then turns to the potential application of AI by criminals, including what we term here ‘crimes with AI’, ‘crimes against AI’, and ‘crimes by AI’. In these sections, our aim is to highlight AI’s potential as a criminogenic phenomenon, both in terms of scaling up existing crimes and facilitating new digital transgressions. In the third part of the article, we turn our attention to the main ways the AI paradigm is transforming policing, surveillance, and criminal justice practices via diffuse monitoring modalities based on prediction and prevention. Throughout the paper, we deploy an array of programmatic examples which, collectively, we hope will serve as a useful AI primer for criminologists interested in the ‘tech-crime nexus’.

The international governance of artificial intelligence (AI) is at a crossroads: should it remain fragmented or be centralised? We draw on the history of environment, trade, and security regimes to identify advantages and disadvantages in centralising AI governance. Some considerations, such as efficiency and political power, speak for centralisation. The risk of creating a slow and brittle institution, and the difficulty of pairing deep rules with adequate participation, speak against it. Other considerations depend on the specific design. A centralised body may be able to deter forum shopping and ensure policy coordination. However, forum shopping can be beneficial, and fragmented institutions could self-organise. In sum, these trade-offs should inform development of the AI governance architecture, which is only now emerging. We apply the tradeoffs to the case of the potential development of high-level machine intelligence. We conclude with two recommendations. First, the outcome will depend on the exact design of a central institution. A well-designed centralised regime covering a set of coherent issues could be beneficial. But locking-in an inadequate structure may pose a fate worse than fragmentation. Second, fragmentation will likely persist for now. The developing landscape should be monitored to see if it is self-organising or simply inadequate. Policy Implications • Secretariats of emerging AI initiatives, for example, the OECD AI Policy Observatory, Global Partnership on AI, the UN High-level Panel on Digital Cooperation, and the UN System Chief Executives Board (CEB) should coordinate to halt and reduce further regime fragmentation. • There is an important role for academia to play in providing objective monitoring and assessment of the emerging AI regime complex to assess its conflict, coordination, and catalysts to address governance gaps without vested interests. Secretariats of emerging AI initiatives should be similarly empowered to monitor the emerging regime. The CEB appears particularly well placed and mandated to address this challenge, but other options exist. • What AI issues and applications need to be tackled in tandem is an open question on which the centralisation debate sensitively turns. We encourage scholars across AI issues from privacy to military applications to organise venues to more closely consider this vital question. • Non-state actors, especially those with technical expertise, will have a potent influence in either a fragmented or centralised regime. These contributions need to be used, but there also need to be safeguards in place against regulatory capture. • The AI regime complex is at an embryonic stage, where informed interventions may be expected to have an outsized impact. The effect of academics as norm entrepreneurs should not be underestimated at this point.