This chapter seeks to clarify the criminal responsibility that may be imputable to: (i) programmers of autonomous vehicles for related crimes under national criminal law such as manslaughter and negligent homicide and (ii) programmers of autonomous weapons for related crimes under international criminal law, such as war crimes. The key question is whether programmers could satisfy the actus reus element required for establishing criminal responsibility. The core challenge in answering this question is establishing a causal link between programmers’ conduct and crimes related to autonomous vehicles and autonomous weapons. The chapter proposes responsibility for inherent foreseeable risks associated with the use of AVs and AWs on the basis of programmers’ alleged control of the behavior and/or effects of the autonomous vehicles and autonomous weapons. Establishing the exercise of meaningful human control by programmers over autonomous vehicles and autonomous weapons is crucial to the process of imputing criminal responsibility and bridging a responsibility gap.

In human–robot interactions in legal proceedings, human responses to robot-generated evidence will present unique challenges to the accuracy of litigation as well as ancillary goals such as fairness and transparency, though it may also enhance accuracy in other respects. The most important feature of human–robot interactions is the human tendency to anthropomorphize robots, which can generate misleading impressions and be manipulated by designing robots to make them appear more trustworthy and believable. Although robot-generated evidence may also offer unique advantages, there are concerns about the degree to which the traditional methods of testing the accuracy of evidence, particularly cross-examination, will be effective. We explore these phenomena in the autonomous vehicles context, comparing the forums of litigation, alternative dispute resolution, and the National Transportation Safety Board. We suggest that the presence of expert decision-makers might help mitigate some of the problems with human–robot interactions, though other aspects of the procedures in each of the forums still raise concerns.

In Singapore, residents have expressed concerns about the safety of autonomous vehicles. This chapter considers the case of Singapore, which has supported the development of autonomous vehicles and tested their use. Using research studies and newspaper reports, the chapter examines the rhetorical devices used to frame relevant discussion and identifies the narrative arguments used to reduce fears and justify the presence of vehicles on public streets. The narratives of government and commercial entities complement each other and are frequently upbeat, but they differ in that commercial entities asserted the narrative that autonomous vehicles were inevitable, while government entities did not. The government’s rejection of inevitability supports a different view of law and government, in which government officials decide the degree and pace of AV development. However, Singapore has not adopted a strict regulatory approach, and opted instead for light touch regulation. As a narrative argument, rejection of inevitability does not dictate regulatory approach.

The UK Parliament has already pre-emptively legislated for a compensation solution for autonomous vehicle accidents through the Automated and Electric Vehicles Act 2018. The Act is a response to the fact that the ordinary approach to motor vehicle accidents cannot apply in an AV context since there is no human driver. Tort law has previously been subjected to major shifts in response to motor vehicles, and we are again on the cusp of another motor-vehicle-inspired revolution in tort law. However, in legislating for AV accidents in the UK, there was inadequate consideration of alternative approaches.

This chapter discusses three philosophical disputes concerning the comparison between the ethics of crashing autonomous vehicles and the Trolley Problem. The first dispute concerns whether there is something ethically problematic – or perhaps even flippant – about comparing the real-world issue of what autonomous vehicles should do in accident scenarios with the philosophy of the Trolley Problem. The second dispute concerns whether or not there is a close enough analogy between real-world accidents involving autonomous vehicles and the so-called trolley cases discussed in relation to the Trolley Problem. The third dispute concerns whether or not the large literature on the Trolley Problem discusses topics directly relevant to the real-world ethics of crashes with autonomous vehicles. The chapter considers key arguments on each side of the dispute. It also offers a diagnosis regarding whether the Trolley Problem is relevant for the ethics of autonomous vehicles. The conclusion is that it is either directly or indirectly relevant: It may be directly relevant because the Trolley Problem can teach us important lessons or indirectly relevant because identifying key differences between the ethics of autonomous vehicles and the Trolley Problem allows us to get clear on what matters most in the real-world ethics of autonomous vehicles.

Open-source platforms are an increasingly popular business model for AI development for global technology companies. This chapter examines why a restrictive (non-fuzzy) interpretation of the data localisation provisions within the Cyber Security Law would harm the growth of China’s entrepreneurial ecosystem, focusing on recent Chinese government plans to grow its own domestic open-source AI ecosystem. Accordingly, this chapter reinforces the reasons why fuzzy logic lawmaking in China is so effective. It also queries whether the increased popularity of open-source platforms in China during 2017–2019 may have been another reason why data localisation was not comprehensively enforced.

In analyzing the existing and future transportation system in general, and the testing and deployment of automated and self-driving vehicles in particular, this chapter demonstrates that the application of our framework provides a good understanding of the interdependencies between the technological and institutional dimensions at stake. An analysis of both the vertical coordination between the layers along these two dimensions, respectively, and the horizontal alignment between them offers in-depth insights about the complexity of the transportation network and the conditions to be met if the expected services are to be delivered. The changes in the technological architecture, with the introduction of technological designs and operation of automated vehicles, and their interdependence with macro-institutional values, in particular safety but also security, privacy, and efficiency, offer a rich opportunity to analyze the structural complexities at stake. In this chapter, we focus on the layer of transactions: transactions between car manufacturers and their suppliers, between car manufacturers and the providers of the transportation services, and between these providers and their customers. The importance of the alignment between technical operations and micro-institutions is illustrated by the fatal accident involving an automated test car on March 19, 2018 in a street in Tempe, Arizona.

Can we trust the judgement of machines that see? Computer vision is being entrusted with ever more critical tasks: from access control by face recognition, to diagnosis of disease from medical scans and hand-eye coordination for surgical and nuclear decommissioning robots, and now to taking control of motor vehicles.

The increasing autonomy of AI systems is exposing gaps in regulatory regimes that assume the centrality of human actors. Yet surprisingly little attention is given to what is meant by ‘autonomy’ and its relationship to those gaps. Driverless vehicles and autonomous weapon systems are the most widely studied examples, but related issues arise in algorithms that allocate resources or determine eligibility for programmes in the private or public sector. This chapter develops a novel typology that distinguishes three lenses through which to view the regulatory issues raised by autonomy: the practical difficulties of managing risk associated with new technologies, the morality of certain functions being undertaken by machines at all, and the legitimacy gap that is created when public authorities delegate their powers to algorithms.