This article argues that legal discourses about robots are framed within a limiting ‘human paradigm.’ While this is not a specific failure of lawyers, it has significant consequences for law in a digital future. This visualising of robots has its origins in mainstream twentieth-century science fictional tropes of artificial beings. This article begins by identifying the predominant science fiction tropes regarding artificial beings as a source of anxiety for human futures, as located in discrete bodies and as separate from humans. (...) The article then traces this ‘human paradigm’ in robot law scholarship. It is shown how a focus on embodiment and separation disrupts appreciation of the emerging partial disembodiment and hybridity of digital autonomy. There is a continual sense of needing to keep robots and humans distinct and separate, which is not how digital futures are manifesting. (shrink)

Law and the legal system through which law is effected are very powerful, yet the power of the law has always been limited by the laws of nature, upon which the law has now direct grip. Human law now faces an unprecedented challenge, the emergence of a second limit on its grip, a new “species” of intelligent agents (AI machines) that can perform cognitive tasks that until recently only humans could. What happens, as a matter of law, when another species (...) interacts with us, can be integrated into human minds and bodies, makes “real-world” decisions—not through human proxies, but directly—and does all this “intelligently”, with what one could call autonomous agency or even a “mind” of its own? The article starts from the clear premise that control cannot be exercised directly on AI machines through human law. That control can only be effected through laws that apply to humans. This has several regulatory implications. The article’s first discusses what, in any attempt to regulate AI machines, the law can achieve. Having identified what the law can do, the article then canvases what the law should aim to achieve overall. The article encapsulate its analysis in a list of both doctrinal and normative principles that should underpin any regulation aimed at AI machines. Finally, the article compares three transnational options to implement the proposed regulatory approach. (shrink)

As robots and Artificial Intelligences become more enmeshed in rich social contexts, it seems inevitable that we will have to make them into moral machines equipped with moral skills. Apart from the technical difficulties of how we could achieve this goal, we can also ask the ethical question of whether we should seek to create such Artificial Moral Agents (AMAs). Recently, several papers have argued that we have strong reasons not to develop AMAs. In response, we develop a comprehensive analysis (...) of the relevant arguments for and against creating AMAs, and we argue that all things considered we have strong reasons to continue to responsibly develop AMAs. The key contributions of this paper are threefold. First, to provide the first comprehensive response to the important arguments made against AMAs by Wynsberghe and Robbins (in “Critiquing the Reasons for Making Artificial Moral Agents”, Science and Engineering Ethics 25, 2019) and to introduce several novel lines of argument in the process. Second, to collate and thematise for the first time the key arguments for and against AMAs in a single paper. Third, to recast the debate away from blanket arguments for or against AMAs in general, to a more nuanced discussion about the use of what sort of AMAs, in what sort of contexts, and for what sort of purposes is morally appropriate. (shrink)

The transfer of tasks with sometimes far-reaching implications to autonomous systems raises a number of ethical questions. In addition to fundamental questions about the moral agency of these systems, behavioral issues arise. We investigate the empirically accessible question of whether the imposition of harm by an agent is systematically judged differently when the agent is artificial and not human. The results of a laboratory experiment suggest that decision-makers can actually avoid punishment more easily by delegating to machines than by delegating (...) to other people. Our results imply that the availability of artificial agents could provide stronger incentives for decision-makers to delegate sensitive decisions. (shrink)

Through modern driver assistant systems, algorithmic decisions already have a significant impact on the behavior of vehicles in everyday traffic. This will become even more prominent in the near future considering the development of autonomous driving functionality. The need to consider ethical principles in the design of such systems is generally acknowledged. However, scope, principles and strategies for their implementations are not yet clear. Most of the current discussions concentrate on situations of unavoidable crashes in which the life of human (...) beings is existentially affected. In this paper, we argue that ethical considerations should be mandatory for any algorithmic decision of autonomous vehicles, instead of a limitation to hazard situations. Such an ethically aligned behavior is relevant because autonomous vehicles, like any other traffic participants, operate in a shared public space, where every behavioral decision impacts the operational possibilities of others. These possibilities concern the fulfillment of a road-user’s safety, utility and comfort needs. We propose that, to operate ethically in such space, an autonomous vehicle will have to take its behavior decisions according to a just distribution of operational possibilities among all traffic participants. Using an application on a partially-autonomous prototype vehicle, we describe how to apply and implement concepts of distributive justice to the driving environment and demonstrate the impact on its behavior in comparison to an advanced but egoistic decision maker. (shrink)

I argue for responsibility internalism. That is, moral responsibility (i.e., accountability, or being apt for praise or blame) depends only on factors internal to agents. Employing this view, I also argue that no one is responsible for what AI does but this isn’t morally problematic in a way that counts against developing or using AI. Responsibility is grounded in three potential conditions: the control (or freedom) condition, the epistemic (or awareness) condition, and the causal responsibility condition (or consequences). I argue (...) that causal responsibility is irrelevant for moral responsibility, and that the control condition and the epistemic condition depend only on factors internal to agents. Moreover, since what AI does is at best a consequence of our actions, and the consequences of our actions are irrelevant to our responsibility, no one is responsible for what AI does. That is, the so-called responsibility gap exits. However, this isn’t morally worrisome for developing or using AI. Firstly, I argue, current AI doesn’t generate a new kind of concern about responsibility that the older technologies don’t. Then, I argue that responsibility gap is not worrisome because neither responsibility gap, nor my argument for its existence, entails that no one can be justly punished, held accountable, or incurs duties in reparations when AI causes a harm. (shrink)

The ethics of emerging forms of artificial intelligence has become a prolific subject in both academic and public spheres. A great deal of these concerns flow from the need to ensure that these technologies do not cause harm—physical, emotional or otherwise—to the human agents with which they will interact. In the literature, this challenge has been met with the creation of artificial moral agents: embodied or virtual forms of artificial intelligence whose decision procedures are constrained by explicit normative principles, requiring (...) the implementation of what is commonly called artificial morality into these agents. To date, the types of reasoning structures and principles which inform artificial morality have been of two kinds: first, an ethically maximal vision of artificial morality which relies on the strict implementation of traditional moral theories such as Kantian deontology or Utilitarianism, and second, a more minimalist vision which applies stochastic AI techniques to large data sets of human moral preferences so as to illicit or intuit general principles and preferences for the design of artificial morality. Taken individually, each approach is unable to fully answer the challenge of producing inoffensive behavior in artificial moral agents, most especially since both forms are unable to strike a balance between the ideal set of constraints which morality imposes on one hand, and the types of constraints public acceptability imposes, on the other. We provide an alternative approach to the design of artificial morality, the Ethical Valence Theory, whose purpose is to accommodate this balance, and apply this approach to the case of autonomous vehicles. (shrink)