Many ethical questions about our future with intelligent machines rest upon assumptions concerning the origins, development and ideal future of humanity and of the universe, and hence overlap considerably with many religious questions. First, could computers themselves become moral in any sense, and could different components of morality – whatever they are – be instantiated in a computer? Second, could computers enhance the moral functioning of humans? Do computers potentially have a role in narrowing the gap between moral aspiration and how morality is actually lived out? Third, if we develop machines comparable in intelligence to humans, how should we treat them? This question is especially acute for embodied robots and human-like androids. Fourthly, numerous moral issues arise as society changes such that artificial intelligence plays an increasingly significant role in making decisions, with implications for how human beings function socially and as individuals, treat each other and access resources.

Data is one of the most valuable resources in the twenty-first century. Property rights are a tried-and-tested legal response to regulating valuable assets. With non-personal, machine-generated data within an EU context, mainstream IP options are not available, although certain types of machine generated data may be protected as trade secrets or within sui generis database protection. However, a new IP right is not needed. The formerly proposed EU data producer’s right is a cautionary tale for jurisdictions considering a similar model. A new property right would both strengthen the position of de facto data holders and drive up costs. However, with data, there are valuable lessons to be learned from constructed commons models.

The EU Artificial Intelligence Act proposal is based on a risk-oriented approach. While AI systems that pose an unacceptable risk will be banned, high-risk AI systems will be subject to strict obligations before they can be put on the market. Most of the provisions deal with high-risk systems, setting out obligations on providers, users and other participants across the AI value chain. At the heart of the proposal is the notion of co-regulation through standardization based on the New Legislative Framework. Accordingly, this chapter provides a critical analysis of the proposal, with particular focus on how the co-regulation, standardization and certification system envisaged contributes to European governance of AI and addresses the manifold ethical and legal concerns of (high-risk) AI systems.

The hope is that legal rules relating to AI technologies can frame their progress and limit the risks of abuse. This hope is tentative as technology seriously challenges the theory and practice of the law across legal traditions. The use of interdisciplinary and comparative methodologies makes clear that AI is currently impacting our understanding of the law. AI can be understood as a regulatory technology and confirms that AI can produce normative effects some of which may be contrary to public laws and regulations.

This paper presents the main topics, arguments, and positions in the philosophy of AI at present (excluding ethics). Apart from the basic concepts of intelligence and computation, the main topics of artificial cognition are perception, action, meaning, rational choice, free will, consciousness, and normativity. Through a better understanding of these topics, the philosophy of AI contributes to our understanding of the nature, prospects, and value of AI. Furthermore, these topics can be understood more deeply through the discussion of AI; so we suggest that “AI philosophy” provides a new method for philosophy.

In this chapter, Fruzsina Molnár-Gábor and Johanne Giesecke consider specific aspects of how the application of AI-based systems in medical contexts may be guided under international standards. They sketch the relevant international frameworks for the governance of medical AI. Among the frameworks that exist, the World Medical Association’s activity appears particularly promising as a guide for standardisation processes. The organisation has already unified the application of medical expertise to a certain extent worldwide, and its guidance is anchored in the rules of various legal systems. It might provide the basis for a certain level of conformity of acceptance and implementation of new guidelines within national rules and regulations, such as those on new technology applications within the AI field. In order to develop a draft declaration, the authors then sketch out the potential applications of AI and its effects on the doctor–patient relationship in terms of information, consent, diagnosis, treatment, aftercare, and education. Finally, they spell out an assessment of how further activities of the WMA in this field might affect national rules, using the example of Germany.

It is only recently that the EPO’s Boards of Appeal have had to deal with appeals relating to the surge of AI based inventions. In doing so the Boards of Appeal have adopted a gradualist approach, adapting the extensive EPO case law relating to the patentability of computer programs ‘as such’ and applying it to AI inventions. The most recent change to the Guidelines indicates the EPO’s willingness to adapt to technological developments and to refine its approach to patentability of inventions involving AI, while at the same time taking a firm line against patenting non-technical inventions.

AI is a complex, multifaceted concept and is therefore hard to define because AI can refer to technological artifacts, certain methods or a scientific field that is split into many subfields and that is continuously changing and evolving AI systems can therefore be seen as digital artifacts that require hardware and software components and that contain at least one learning or learned component, i.e., a component that is able to change the system’s behavior based on presented data and the processing of this data.

Appendix H briefly lists and discusses the available NVIDIA and third-party tools to support AI and Python.

This chapter turns to the possibility that the AI systems challenging the legal order may also offer at least part of the solution. Here China, which has among the least developed rules to regulate conduct by AI systems, is at the forefront of using that same technology in the courtroom. This is a double-edged sword, however, as its use implies a view of law that is instrumental, with parties to proceedings treated as means rather than ends. That, in turn, raises fundamental questions about the nature of law and authority: at base, whether law is reducible to code that can optimize the human condition, or if it must remain a site of contestation, of politics, and inextricably linked to institutions that are themselves accountable to a public. For many of the questions raised, the rational answer will be sufficient; but for others, what the answer is may be less important than how and why it was reached, and whom an affected population can hold to account for its consequences.