Simulating religion through computer modelling can demonstrate how fragmentary theories relate, untangle individual lines of causal influence, identify the relative importance of causal factors and enable experimentation that would never be possible (or ethical) in the real world. This chapter reviews the application of computational modelling and simulation to religion, presents findings from specific simulation studies and discusses some of the philosophical issues raised by this type of research. Social simulations are artificial complex systems that we can use to study real-world complex systems. The best of these simulation models are carefully validated in relation to real-world data. Multilevel validation justifies confidence that the causal architecture of the simulation reflects real-world causal processes, thereby delivering an invaluable proxy system into the hands of researchers who study religion.

This chapter explores design guidelines and potential regulatory issues that could be associated with future baby robot interaction. We coin the term “robot natives,” which we define as the first generation of human’s regularly interacting with robots in domestic environments. This term includes babies (0–1 year old) and toddlers (1–3 years old) born in the 2020s. Drawing from the experience of other interactive technologies becoming widely available in the home and the positive and negative impact they have on humans; we propose some insights into the design of future scenarios for baby–robot interaction, aiming to influence future legislation regulating service robots and social robots used with robot natives. Similarly, we aim to inform designers and developers to inhibit robot designs which can negatively affect the long-term interactions for the robot natives. We conclude that a qualitative, multidisciplinary, ethical, human-centered design approach should be beneficial to guide the design and use of robots in the home and around families as this is currently not a common approach in the design of studies in child robot interaction.

Romance between a human and robot will pose many questions for the laws that apply to human–robot interaction and, in particular, family law. Such questions include whether humans and robots can marry and what a subsequent divorce might look like. This chapter considers these issues, organized to track the seasons of romantic relationships, such as cohabitation, engagement, and marriage. Given that marriage is no longer devoid of the possibility of divorce, this chapter also considers issues of property division, alimony, child custody, and child support when a marriage between a human and robot dissolves. Even for skeptics of such a future, given rapid advances in robotics, the applicability of family law to relationships between a human and robot is nonetheless an increasingly relevant thought experiment and intersects with other emerging areas of law, technology, and robotics.

Chapter 8 introduces evaluation procedures for paradigms other than classification. In particular, it discusses evaluation for classical problems such as regression analysis, time-series analysis, outlier detection, and reinforcement learning, along with evaluation approaches for newer tasks such as positive-unlabelled classification, ordinal classification, multi-labeled classification, image segmentation, text generation, data stream mining, and lifelong learning.

The purpose of this chapter is to contribute to the current discussion on what robot laws might look like once artificial intelligence (AI)-enabled robots become more widespread and integrated within society. The starting point of the discussion is Asimov’s three laws of robotics, and the realization that while Asimov’s laws are norms formulated in human language, the behavior of robots is fundamentally controlled by algorithms, that is, by code. Three conclusions can be drawn from this starting point in the discussion of what laws for robots might look like. One is that laws enacted for humans will be translated into laws for robots, which as discussed here, will be a difficult challenge for legal scholars. The second conclusion is that due to the norms which exist within society, the same rules will be simultaneously present in the natural language version of laws for humans and the code version of laws for robots. And the third conclusion is that the translation of the robots’ actions and outputs back into human language will also be a challenging process. In addition, the chapter also argues that the regulation of a robot’s behavior largely overlaps with the current discourse on providing explainable AI but with the added difficulty of understanding how explaining legal decisions differ from explaining the outputs of AI and AI-enabled robots in general.

In previous research, we considered several novel problems posed by robot accidents and assessed related legal and economic approaches to the creation of optimal incentives for robot manufacturers, operators, and prospective victims. In this chapter, we synthesize our previous work in a unified analysis. We begin with a discussion about the problems and legal challenges posed by robot torts. Next, we describe the novel liability regime we proposed, that is, “manufacturer residual liability,” which blends negligence-based rules and strict manufacturer liability rules to create optimal incentives for robot torts. This regime makes operator and victim liability contingent upon their negligence (incentivizing them to act diligently) and makes manufacturers residually liable for nonnegligent accidents (incentivizing them to make optimal investments in researching safer technologies). This rule has the potential to drive unsafe technology out of the market and also to incentivize operators to adopt optimal activity levels in their use of automated technologies.

In Chapter 7, the history of statistical analysis is reviewed and its legacy discussed. Four situations of interest to machine learning evaluation are subsequently discussed within different statistical paradigms: the comparison of two classifiers on a single domain; the comparison of multiple classifiers on a single domain; the comparison of two classifiers on multiple domains; and the comparison of multiple classifiers on multiple domains. The three statistical paradigms considered for each of these situations are the null hypothesis statistical testing (NHST) setting; an enhanced Fisher-flavored methodology that adds the notions of confidence intervals, effect size, and power analysis to NHST; and a newer approach based on Bayesian reasoning.

Humans categorize themselves and others on the basis of many attributes forging a range of social groups. Such group identities can influence our perceptions, attitudes, beliefs, and behaviors toward others and ourselves. While decades of psychological research has examined how dividing the world into “us” and “them” impacts our attitudes, beliefs, and behaviors toward others, a new and emerging area of research considers how humans can ascribe social group memberships to humanoid robots. Specifically, our social perceptions and evaluations of humanoids can be shaped by subtle characteristics about the robot’s appearance or other features, particularly if these characteristics are interpreted through the lens of important human group identities. The current chapter reviews research on the psychology of intergroup relations to consider its manifestations and expressions in the context of human–robot interaction. We first consider how robots despite being nonliving can be ascribed certain identities (e.g., race, gender, and national origin). We then consider how this can in turn impact attitudes, beliefs, and behaviors toward such technology. Given the nascency of this field of study, we highlight existing gaps in our knowledge and highlight important directions for future research. The chapter concludes by considering the societal, market, and legal implications of bias in the context of human–robot interaction.

Over the last years there has been growing research interest in religion within the robotics community. Along these lines, this chapter will provide a case study of the ‘religious robot’ SanTO, which is the world’s first robot designed to be ‘Catholic’. This robot was created with the aim of exploring the theoretical basis for the application of robot technology in the religious space. While the application of this technology has many potential benefits for users, the use and design of religious or other social robots raises a number of ethical, legal, and social issues (ELSI). This paper, which is concerned with such issues will start with a general introduction, offer an ELSI analysis, and finally develop conclusions from an ethical design perspective.

The First Amendment of the U.S. Constitution protects “the freedom of speech.” Courts have also said it protects “freedom of thought.” But does that mean we have a right to speak with or listen to the speech of robots? Does it mean we have a First Amendment right to recruit robots to help us think or change the way we think? By “robots” here, I mean the robots that are the primary focus of this book: Humanoid robots that might not only emulate the way we solve intellectual challenges or express ourselves, but also emulate us physically – by taking on a physical form similar to that of human beings and moving and acting in the physical space of our homes, workplaces, or other spaces, and not simply the virtual space on our computers.