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.

This chapter explores AI’s potential consciousness, distinguishing it from human consciousness and addressing concerns about unintentionally creating conscious AI. The "Hard Problem of Consciousness" examines challenges in understanding how systems generate consciousness. "Strong AI" and "weak AI" concepts are introduced, envisioning AI replicating human functions, including consciousness. The chapter explores artificial consciousness’s significance in human–AI interactions, attachment, and ethical considerations, addressing potential risks and implications. Later sections cover consciousness aspects such as self-awareness, subjectivity, memory, anticipation, learning, perception, time awareness, cognition, reflection, intentionality, emotion, empathy, dreaming, and imagination. It navigates the intersection of AI, consciousness, and ethical and legal implications, discussing challenges and testing approaches like the Turing test, the Argonov test, the ConsScale test, the emotional response test, the ethical decision-making test, the mirror test, the global workspace test, and the know thyself test. The chapter suggests that AI consciousness may not be binary but could exist in varying degrees.

Artificial intelligence (AI) is increasingly adopted in society, creating numerous opportunities but at the same time posing ethical challenges. Many of these are familiar, such as issues of fairness, responsibility, and privacy, but are presented in a new and challenging guise due to our limited ability to steer and predict the outputs of AI systems. This chapter first introduces these ethical challenges, stressing that overviews of values are a good starting point but often fail to suffice due to the context-sensitivity of ethical challenges. Second, this chapter discusses methods to tackle these challenges. Main ethical theories (such as virtue ethics, consequentialism, and deontology) are shown to provide a starting point, but often lack the details needed for actionable AI ethics. Instead, we argue that mid-level philosophical theories coupled to design-approaches such as “design for values”, together with interdisciplinary working methods, offer the best way forward. The chapter aims to show how these approaches can lead to an ethics of AI that is actionable and that can be proactively integrated in the design of AI systems.

Despite the benefits of the convergence of AI in ecommerce, it is necessary to address some concerns. The presence of AI-powered platforms raises significant challenges to consumer autonomy. This chapter discusses the overlap and interplay among three main legal regimes – EU AI Act Proposal, Digital Services Act (DSA), and EU Consumer Law.These laws will need to be amended with new articles to adequately address AI-specific concerns

Society needs to influence and mould our expectations so AI is used for the collective good. we should be reluctant to throw away hard (and recently) won consumer rights and values on the altar of technological developments.

In the late third or early second century BC the off-glide of the diphthong /ai/ was lowered to /ae̯/, leading to a change in spelling from <ai> to <ae> (see p. 000). The use of <ai> for <ae> in inscriptions of the first–fourth centuries AD, especially in genitive and dative singulars of the first declension, is actually not particularly difficult to find, even in quite large numbers (although given the thousands of examples of <ae>, the frequency is probably still very low). Some, but not all, of these will be due to Greek influence, misreadings, or mistakes by the stonemason. Use of <ai> seems to have been one of the spellings favoured by Claudius (Biddau 2008: 130–1), but examples can still be found long afterwards.

As the use of AI grows ever more prevalent and sophisticated, the issuesof the patentability of AI will need be addressed by the US Congress, USPTO, and the courts. While the questions raised with respect to patenting AI have been debated and are now being considered more broadly, few have been definitively answered. Early address and resolution of these issues will allow patent law to keep pace with the new tide of AI-related technologies and inventions.

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.

Metacognition is the concept of reasoning about an agent’s own internal processes and was originally introduced in the field of developmental psychology. In this position chapter, we examine the concept of applying metacognition to artificial intelligence (AI). We introduce a framework for understanding metacognitive AI that we call TRAP: transparency, reasoning, adaptation, and perception.