Recently there has been more attention to the cultural aspects of social robots. This chapter contributes to this effort by offering a philosophical, in particular Wittgensteinian framework for conceptualizing in what sense and how robots are related to culture and by exploring what it would mean to create an “Ubuntu Robot.” In addition, the chapter gestures toward a more culturally diverse and more relational approach to social robotics and emphasizes the role technology can play in addressing the challenges of modernity and in assisting cultural change: It argues that robots can help us to engage in cultural dialogue, reflect on our own culture, and change how we do things. In this way, the chapter contributes to the growing literature on cross-cultural approaches to social robotics.

This chapter examines from a theoretical perspective the role of interface in the formation of trust in HR interaction, notably between humans and humanoid robotic agents. Interfaces are fundamental means of communication that do not only allow interaction between different agents but they also enable them to exchange information on the nature of the context in which the interaction takes place: This can facilitate the formation of a trust relationship between agents, whether they are human or robotic, within a more or less structured context. The chapter concludes with a discussion of the pivotal role of human–robot interfaces as essential means of communication from the analysis displayed in my contribution.

After researching for years the law and ethics of robotic systems in the security sector, that is, defense, law enforcement, and disaster relief, we present a set of eight recommendations bearing on human–robot interaction. These recommendations should guide developers, lawyers, ethicists, and policymakers who navigate a highly technical and dynamic field. Our eight recommendations boil down to the following: In circumstances where technology develops rapidly while normative discussions fail to keep up and uncertainty prevails, we suggest a pragmatic, practical, and dynamic form of applied ethics to those involved with robotics – an ethics that eschews high politics at first, but ultimately produces experience that can be fed back into higher-level normative discussions on robots and technology more generally and thus move them forward.

Chapter 8 analyzes the transformative impact of non-fungible tokens (NFTs) in redefining digital ownership, authenticity, and value. Grounded in blockchain technology, NFTs have initiated a paradigm shift closely aligned with the tenets of the New Economy and the Self-Sovereign Internet. The New Economy is characterized by decentralized, digital, and disintermediated transactions, while the Self-Sovereign Internet aims to return data control to individual users. We examine the underlying technical architecture that makes NFTs unique, including blockchain protocols and metadata, illustrating how they foster transparency and individual control. The chapter also delves into the expansive applicability of NFTs across sectors such as art, sports, gaming, and real estate, elucidating their role in catalyzing new forms of economic activity. Moreover, we consider future trends in NFT innovation and responsible governance practices that align with the ideals of individual data ownership and decentralization. By dissecting these aspects, the chapter offers a nuanced perspective on the role of NFTs in shaping future digital interactions and economic models.

In Chapter 10, the book turns to practical considerations. In particular, it surveys the software engineering discipline with its rigorous software testing methods, and asks how these techniques can be adapted to the subfield of machine learning. The adaptation is not straightforward, as machine learning algorithms behave in non-deterministic ways aggravated by data, algorithm, and platform imperfections. These issues are discussed and some of the steps taken to handle them are reviewed. The chapter then turns to the practice of online testing and addresses the ethics of machine learning deployment. The chapter concludes with a discussion of current industry practice along with suggestions on how to improve the safety of industrial deployment in the future.

This chapter examines the various trajectories of Christian reflection on current and potential developments in artificial intelligence (AI). It reviews the theological questions generated by the emergence of intelligent machines and explores some of the most interesting solutions proposed in response to these quandaries. The first part is dedicated to inquiries about hypothetical AI developments and their potential implications for Christian theology. Could intelligent machines become authentic selves? If so, could they also partake in the image of God? Could the Christian imaginary envisage a future where robots develop their own religiosity and robotheologies? Could robots also aspire to be saved? The second section adopts a theological anthropological angle of inquiry, considering how insights gained from AI may contribute to refining this approach. What do our fascination with AI and our deep desire to create an intelligent other reveal about human nature? How would our theological self-understanding change if intelligent machines became ubiquitous?

The use of advanced robots interacting with humans, especially in the health and care sector, will interfere with the personal autonomy and privacy of the users. A person can give permission to such interference by giving their consent. In law, the term “consent” has different connotations dependent on the field of law and jurisdictions. A recurrent topic in robotics research is how consent can be implemented in human–robot interaction (HRI). However, it is not always clear what consent actually means. The terms “informed consent” and “consent” are used interchangeably with an emphasis on informational privacy, whereas the embodiment of robots also intervenes with “physical” privacy. This lack of nuance leads to misconceptions about the role consent can play in HRI. In this chapter, I critically examine the perception and operationalization of consent in HRI and whether consent is an appropriate concept at all. It is crucial for the adaptation of consent to understand the requirements for valid consent, and the implications consent have for deployment of robots, in particular in a health and care setting. I argue that valid consent to the use of AI-driven robots may be hard, if not impossible, to obtain due to the complexity of understanding the intentions and capabilities of the robot. In many instances, consent would not be appropriate in robotics because it would not be possible to have truly informed and free consent beyond a simple “yes” or “no.” This chapter is a critical analysis that builds on the extensive scholarly critique of consent. As the chapter shows, robotics is yet another field where the shortcomings of consent are salient.

This chapter assesses EU Consumer law and Policy in the light of consumers’ increasing interaction with humanoid robots. Amidst the plethora of benefits that humanoid robots can bring to consumers, they can also challenge the application of consumer protection principles. The biggest problems lie in the areas of protection of the weaker party, consumer autonomy, nondiscrimination, and privacy. Consumers may face difficulties ranging from exposure to unfair commercial practices, difficulties in exercising their rights under the Consumer Rights Directive, in claiming damages for AI-related losses under the Product Liability Directive, in exercising the rights – for example, to consent, to be forgotten, to be informed, not to be profiled – under the General Data Protection Regulation, and in avoiding being discriminated by credit scoring systems under the Consumer Credit Directive. The chapter explores the sufficiency of EU legislation to tackle the wide range of challenges and proposes targeted regulatory action. The rationale behind addressing these issues is to strike a balance between consumer protection and innovation in robotics.

Chapter 6 provides a comprehensive overview of tokenomics, analyzing the economic models and incentive mechanisms underlying tokens and cryptocurrencies. It explores the classification, functions, supply and demand dynamics, and financial aspects of various token types, including utility, governance, platform, stablecoins, NFTs, and meme coins. The interplay between tokenomics and Decentralized Finance (DeFi) is examined, highlighting considerations such as staking rewards and yields. Innovations such as "play to earn" gaming are covered but also their risks such as sustainability and Ponzi schemes. The impact of community sentiment and conviction on valuation is analyzed through meme coins and viral hype. Overall, this chapter offers crucial insights into the foundational economics of the Web3 ecosystem, grounded in a nuanced understanding of the incentive structures and value drivers behind diverse crypto tokens and assets. Both the technological potential and limitations are critically appraised. The key takeaway is an ability to comprehensively evaluate tokenomics across dimensions such as utility, supply, demand, distribution, financials, sentiment, and regulation.

When the idea of a robot butler or maid started to spread, Rosie from the Jetsons became a symbol for a vision shared by many roboticists: One day, each household would have a robotic helper that takes care of our chores and keeps us company. However, when we examine the realities of robots produced for the consumer market today (from autonomous cars to social robots), we discover that realizing such a future with robots is not all that rosy for consumers. From a property law perspective, we find that most consumers will only partially own the robots they purchase. The software that gives life, functionality, and character to the robots will likely be owned by the manufacturer, who can take it all away from the purchasers just as easily as they can change subscription fees and user terms and conditions. In this chapter, we draw analogies from the automotive industry and current business trends in the consumer robotics market to underscore seven issues that stem from this precarious ownership dynamic. Reflecting on our collective journey toward “a robot in every home,” we highlight how the status quo is not only unsustainable from an environmental perspective (i.e., generation of electronic waste) but also ineffective in generating a flourishing consumer robotics market. We find that the existing roboethics and AI ethics frameworks do not sufficiently protect consumers from these issues and call on the community to critically examine the complex ethical and societal implications of deploying social robots widely.

Chapter 3 provides a comprehensive overview of the foundational blockchain technology stack. It begins by examining the consensus layer, which enables a decentralized network to agree on the state of a shared ledger. The consensus layer is highlighted as the core foundation, with a deep dive into the breakthrough Nakamoto consensus protocol. Nakamoto consensus leverages proof of work for leader election and the longest-chain rule for transaction confirmation to achieve decentralized consensus in permissionless settings. The chapter then explores the computer layer, which defines the blockchain’s computational model, and the application layer where decentralized apps are built. It uses Bitcoin and Ethereum as examples to demonstrate how these layers work together in real-world systems. Bitcoin implements a simple UTXO model and scripting language, while Ethereum provides a Turing complete virtual machine. Finally, the chapter discusses elements such as wallets and RPC services involved in the user-facing experience. Overall, the chapter provides a comprehensive technical grounding across all layers of blockchain systems.

Chapter 5 starts with an analysis of the classification metrics presented in Chapter 4, outlining their strengths and weaknesses. It then presents more advanced metrics such as Cohen’s kappa, Youden’s index, and likelihood ratios. This is followed by a discussion about data and classifier complexities such as the class imbalance problem and classifier uncertainty that require particular scrutiny to ensure that the results are trustworthy. The chapter concludes with a detailed discussion of ROC analysis to complement its introduction in Chapter 4, and a presentation of other visualization metrics.

Chapter 3 discusses the field of machine learning from a theoretical perspective. The review will advance the discussion of advanced metrics in Chapter 5 and error estimation methods in Chapter 6. The specific concepts surveyed in this chapter include loss functions, empirical risk, generalization error, empirical and structural risk minimization, regularization, and learning bias. The unsupervised learning paradigm is also reviewed and the chapter concludes with a discussion of the bias/variance tradeoff.

In this concluding chapter, we discuss future directions in law, policy, and regulations for robots that are expressive, humanoid in appearance, becoming smarter, and that are anthropomorphized by users. Given the wide range of skills shown by this emerging class of robots, legal scholars, legislators, and roboticists are beginning to discuss how law, policy, and regulations should be applied to robots that are becoming more like us in form and behavior. For such robots, we propose that human–robot interaction should be the focus of efforts to regulate increasingly smart, expressive, humanoid, and social robots. Therefore, in the context of human–robot interaction, this chapter summarizes our views on future directions of law and policy for robots that are becoming highly social and intelligent, displaying the ability to detect and express emotions, and controversially, in the view of some commentators, beginning to display a rudimentary level of self-awareness.