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.

The EU definitions of AI moved from a narrow one to a broad one because of the EU policy which is to govern the phenomenon of AI in the broadest way that includes a wide range of situations. The key contents of the main EU AI documents including the European Parliament Resolution with recommendations to the Commission on Civil Law Rules on Robotics, the Ethics Guidelines for Trustworthy AI, the proposed AI Act, and the recent Proposal for an AI Liability Directive, are examined.

This chapter introduces basic concepts of AI to lawyers, deals with key concepts, the capabilities and limitations of AI, and identifies technological challenges which might require legal responses.

Yet to Recieve

Technological tools currently being developed are capable of substantially assisting judges in their daily work. In particular, data analysis of widely available court decisions will evaluate, in an unprecedented way, the activity of the courts and the quality of justice. In doing so, it will allow for more efficient and faster dispute resolution, as well as cost reductions for litigants and society. Technological evolution will probably not cause the disappearance of humans from judicial adjudication but a new, progressive and subtle redistribution of tasks between men and machines.

INTRODUCTION

• 1. Business entities currently employ AI and other algorithmic techniques in essentially all sectors of the economy in order to influence potential customers. The concept of AI is discussed elsewhere in this book. This contribution is more concerned with what is happening on the market under the label ‘AI’ and how this may affect those who are generally labelled as consumers. After all, the focus of legal research is not so much on ‘new’ technology itself, but rather on the aspects of social life that this technology makes newly salient.

To that end, I will first identify and categorise some of the ways in which business entities employ what is commonly referred to as AI as well as the risks and benefits of such uses (part 2). For this, I will rely on the findings of the ARTificial intelligence SYstems and consumer law & policy project (ARTSY Project) conducted by the European University Institute in Florence under the supervision of professor Hans Micklitz. Subsequently, I will examine how the legislator intends to adapt consumer policy to the changing circumstances created by these previously mentioned developments (part 3). I will limit this study to European Union consumer policy as the Belgian legislator is likely to adopt this approach. I will then examine some of the hurdles (AI-driven) autonomous agents present to consumer autonomy as well as the question to what extent and how this can be dealt with within the current consumer law framework (part 4). In particular, I will discuss a number of market practices which are closely related to the advent of autonomous agents. In this regard, I will rely on the key issues in the consumer domain as defined in a briefing document to the European Parliament prepared by one of the researchers of the ARTSY Project. I will not elaborate on consumer privacy as privacy considerations are discussed elsewhere in this book. Finally, I will recapitulate my findings and contemplate on the nature of consumer rights in the era of AI (part 5).

BENEFITS AND RISKS OF AI AS A MARKET TOOL

• 2. A sectoral analysis prepared within the framework of the ARTSY Project shows that the use of AI is booming in several domains.

Generative artificial intelligence has a long history but surged into global prominence with the introduction in 2017 of the transformer architecture for large language models. Based on deep learning with artificial neural networks, transformers revolutionised the field of generative AI for production of natural language outputs. Today’s large language models, and other forms of generative artificial intelligence, now have unprecedented capability and versatility. This emergence of these forms of highly capable generative AI poses many legal issues and questions, including consequences for intellectual property, contracts and licences, liability, data protection, use in specific sectors, potential harms, and of course ethics, policy, and regulation of the technology. To support the discussion of these topics in this Handbook, this chapter gives a relatively non-technical introduction to the technology of modern artificial intelligence and generative AI.