The use of care robots can reduce the demands for manpower in long-term care facilities. Further, care robots serve the needs of both the elders residing in care facilities and the staff of the facilities. This chapter considers the following issues for care robots. Whether long-term care robots should be required to meet the high standards for the use of medical devices found in current regulations. How should standards of use be developed for care robots based on the characteristics of the robots? For this question, I note that in Japan, a public–private partnership has shown success in the regulation of care robots. In addition, with care robots, how should we protect the privacy of elders and their relatives or friends in contact with care robots given that the elderly may have reduced cognitive ability. And lastly, what legal and ethical concerns apply to the design of the interfaces between care robots and elders?

The Perceptual Assimilation Model (PAM) accounts for how native-language (L1) experience shapes speech perception. According to PAM, infants develop phonological categories by attuning to the critical phonetic features that set phonological categories apart (phonological distinctiveness) and to the phonetic variability that defines each category (phonological constancy).The effects of L1 attunement on perception can also be seen in adults. PAM generates predictions about discrimination accuracy for non-native contrasts by comparing how the non-native phones are perceived in terms of L1 phonological categories. The extent to which perception might be altered further by experience with a second language (L2) is outlined by PAM-L2. While PAM has focused on L1 attunement in monolinguals, and PAM-L2 on L2 acquisition in adulthood, their principles also apply to early bilingual language acquisition. In this chapter, we will consider the various contexts of acquisition and language use in early bilinguals to sketch out how experience with more than one native language shapes perception and how childhood L2 acquisition might modify the emerging phonological system.

When a robot harms humans, are there any grounds for holding it criminally liable for its misconduct? Yes, provided that the robot has the ability to form, act on, and explain its moral decisions. If such a robot falls short of the basic moral standards expected by society, labeling it as a criminal can serve criminal law’s function of censuring wrongful conduct and ease the emotional harm suffered by human victims. Moreover, imposing criminal liability on robots could have significant instrumental value in certain cases, such as in identifying culpable humans. However, this does not exempt the manufacturers, trainers, or owners of the robots from any potential criminal liability.

Even the most market-oriented approaches to regulating AI-enabled robots presume some governmental regulator to collaborate in setting outcome goals. And the more advanced an AI-enabled robot becomes, the greater the need for oversight. For the past several decades, regulatory oversight boards have grown in use to promote the quality, transparency, and accountability of regulatory rules and policy. And recently, leading administrative law voices in the United States and the European Union have called for the creation of an oversight board to monitor regulator use of AI entities. How do we determine if these boards are worth the risks they create? To answer this question, this chapter uses the context of AI-enabled robots, which are increasingly prominent in homes, business, and education, to explain both when regulatory oversight boards are valuable as well as when they can frustrate society’s efforts to reduce the ill effects of emerging smart robots. Regulatory oversight boards create value in this context by conducting impact assessments of regulatory policies with an eye to the technological advancements and social context relevant to AI technologies such as robots, and oversight boards can promote regulatory foresight. However, oversight boards themselves pose risks. By influencing the methodological approach used by regulators, errors made by oversight boards can have outsized impacts. To determine whether any given oversight board is worth the risk it creates, this chapter sets out a simple cost-based approach for comparing the risks and benefits of regulatory oversight boards. This approach then is applied to emerging regulatory oversight boards looking at robots entering society.

The chapter examines a classic subject of HRI, social robotics, and the law, such as the design, manufacture, and use of humanoid AI robots for healthcare. The aim is to illustrate a new set of legal challenges that are unique to these systems when displayed in outer space. Such challenges may require the adoption of new legal standards, in particular, either sui generis standards of space law, or stricter or more flexible standards for HRI in space missions, down to a new “principle of equality” between human standards and robotic standards in outer space. The chapter complements current discussions and initiatives on the development of standards for the use of humanoid AI robots in health law, consumer law, and cybersecurity by considering the realignment of terrestrial standards that we may thus expect with the increasing use of humanoid AI systems in space journeys. The assumption is that breathtaking advancements of AI and robotics, current trends on the privatization of the space, and the evolution of current regulatory frameworks, also but not only in space law, will put the development of these new legal standards in the spotlight.

We have witnessed a growing number of investigations into the acquisition process in a multilingual context, which has become recognized as an independent field, quantitatively and qualitatively different from second language acquisition. Scholars have started to differentiate between learners/speakers on the basis of the complexity of their linguistic background, with the numer of known languages being an additional variable. A growing body of studies into the acquisition of third language phonology demonstrates an inherent complexity of the field reflected, among others, in multidirectional dynamic cross-linguistic influence. As shown, multilingual learners have at their disposal a broadened phonetic repertoire, a raised level of metalinguistic awareness and enhanced perceptual sensitivity, which may facilitate the learning of subsequent phonological systems. Thus, this chapter aims to compare bilingual and trilingual phonetics and phonology by providing an overview of recent research into both subdomains, identifying their common features and, importantly, their points of departure for L3 phonology, with a view to providing new insights into the acquisition of speech.

This chapter introduces the construct of anthropomorphism and highlights its relevance for human–robot interaction (HRI) research. This chapter reviews existing definitions of anthropomorphism and distinguishes it from anthropomorphization. It further discusses established theoretical models of anthropomorphism and their respective empirical support (or lack thereof). Moreover, we address consequences of anthropomorphism, especially for HRI. We shed light on different ways to measure anthropomorphism in HRI, discussing advantages and disadvantages of such measurement approaches, respectively. Finally, the present overview offers reflections on the added value of taking into account anthropomorphism and anthropomorphization in HRI research.

In recent years, the embodiment of AI in the form of a robot brings forth new challenges in privacy and transparency. A cognitive robot must be able to integrate multiple tasks in its performance that requires collecting a great amount of data and the use of various AI techniques. As robotics technology continues to advance, socially assistive humanoid robots will play a more central role in interacting with humans. However, can we trust robots in social contexts? How can we design embedded AI robots such that they are more transparent and trustworthy? and what technical, legal, and ethical frameworks might we adopt to build a trust relationship with robots? This chapter discusses these fundamental questions concerning privacy and transparency in human–robot interaction. It will then propose possible ethical and regulatory responses to address them.

Study abroad is typically viewed as a catalyst for pronunciation learning because it affords learners both massive amounts of L2 input and abundant opportunities for meaningful L2 use. Yet, even in such an environment, there is substantial variability in learning trajectories and outcomes. The nature of the target structure is also a powerful determinant of learning; some structures seem to develop effortlessly, whereas others do not improve much at all. Additionally, study abroad research brings to light the important issue of speaker identity, as learners often make decisions about how they want to sound and what pronunciation features they will adopt. This chapter examines developmental time frames, trajectories, and turning points in the phonetics and phonology of L2 learners in a study abroad context. We also describe how learners acquire the regional pronunciation variants of their host communities considering the phonetics of the target feature and learners’ attitudes and beliefs. We argue that study abroad should be situated within a dynamic, longitudinal, and context-dependent view of phonetic and phonological learning.

Researchers in bilingualism seek to identify factors that are associated with specific features of bilingual speech. One such predictive factor is language dominance, typically understood as the degree to which one of the languages of a bilingual is more often and more proficiently used. In this chapter we review landmark studies that demonstrate the power of language dominance in predicting fine-grained phonetic and phonological characteristics of speech production and on the perceptual and processing abilities in one or both languages of bilinguals. We then critically examine the construct of dominance and identify ways that dominance can be and has been measured, as well as challenges inherent in the measurement of dominance. We follow demonstrating the dynamic character of dominance by reviewing research on dominance switches and shifts. This is followed by a review of extant studies on language dominance in bilingual speech production, perception, and processing in both languages. We conclude with four areas where research can be fruitfully directed.

With technological advancements occurring at a rapid pace in the field of computers, robotics, and artificial intelligence, major changes have taken place in the robotics industry. These changes have led to what some have termed the “robotics revolution” that has had a major impact on social organizations, the economy, and as discussed within this chapter, human rights for industry and service workers. The emergence of AI-enabled robotics has begun to change the world in major ways challenging the law within nation states and also internationally among nations. In that context, the use of intelligent service and industrial robots has broad applications for the large and small industries that use robots as a source of labor. For example, intelligent service robots are used in the fields of healthcare and medicine, transportation, and for care of the elderly and children. Further, police and security services also use robots for crowd control and for surveillance purposes. However, while developments in robotics have provided numerous benefits to society, they have also brought forth many issues that challenge social, moral, and professional norms within society. As a result, the ever-increasing growth and development of robotic technology in various industries is challenging current legal schemes in fundamental ways, one of which is human rights law. As an example, the use of industrial and service robots can lead to employment insecurity, threats to the health and safety of workers, and privacy concerns. Further, the use of robots in industry and for the delivery of services can be inconsistent with other human rights such as the right to health and safety, the right to equality of opportunity, the right to employment and fair working conditions, the right to life, the right to association, prohibition against discrimination, and equality, which are all supported in international and regional human rights documents.

The usage of robots and artificial intelligence is expanding and changing every day. These exciting developments, especially in areas such as engineering, industry, education, and health, have begun to influence the legal world and have become the grounds for many important discussions on the future of law and technology. One of these debates is the question of whether robot judges can take part in a trial, which is the subject of this chapter. Although this problem was previously described as a “distant dream,” there are important examples of this issue on the way to becoming a reality today. Considering developments of AI-enabled and humanoid robots, the following question is posed: “Can robot judges replace human judges?” As a current example, in the “Internet Courts” in China, a robot judge looks like a humanoid robot in the image of a woman using a 3D image inspired by human judges. For this reason, it is important to consider the positive and negative aspects of the possible consequences of the development in the legal world of robot judges that is likely to be widespread in the future, to ensure that the law does not fall behind technological developments.

The Automatic Selective Perception (ASP) model posits that listeners make use of selective perceptual routines (SPRs) that are fast and efficient for recovering lexical meaning. These SPRs serve as filters to accentuate relevant cues and minimize irrelevant information. Years of experience with the first language (L1) lead to fairly automatic L1 SPRs; consequently, few attentional resources are needed in processing L1 speech. In contrast, L2 SPRs are less automatic. Under difficult task or stimulus conditions, listeners fall back on more automatic processes, specifically L1 SPRs. And L2 speech perception suffers where there is a mismatch between the L1 and the L2 phonetics because L1 SPRs may not extract the important cues needed for identifying L2 phonemes. This chapter will present behavioral and neurophysiology evidence that supports the ASP model, but which also indicates the need for some modification. We offer suggestions for future directions in extending this model.

This chapter discusses the Ontogeny Phylogeny Model (OPM), which focuses on the formation and development of second language phonological systems. It proposes an interrelationship between L2 native-like productions, L1 transfer, and universal factors. The model argues that chronologically, and as style becomes increasingly formal, L2 native-like processes increase, L1 transfer processes decrease, and universal processes increase and then decrease. It further claims that the roles of universals and L1 transfer are mediated by markedness and similarity, both of which slow L2 acquisition. Specifically, in similar phenomena L1 transfer processes persist, while in marked phenomena universal processes persist. The OPM also argues that these same principles obtain for learners acquiring more than one L2, monolingual and bilingual acquisition, and L1 attrition. In addition to the chronological stages and variation of the individual learner, the model claims that these relationships hold true for language variation and change, including pidgins and creoles.

Pronunciation teaching is often based on assumptions that learners are monolingual speakers, with the sound system of their native language determining the segmental and suprasegmental difficulties that mark learners’ foreign accents. However, many, if not a majority of speakers of other languages come to pronunciation with more than one language under their command. These bilingual/multilingual speakers are the norm in a globalized world, but how we teach pronunciation rarely accounts for the knowledge and skills these speakers bring to the learning of pronunciation. This chapter describes how the characteristics of bilingual speakers suggest how pronunciation teaching can be reimagined to take into account the range and flexibility of bilingual speakers in using multiple languages. Specifically, we argue that taking a nativeness viewpoint is inconsistent with taking a bilingual viewpoint and calls for pedagogical techniques that build on the kinds of needs bilingual speakers have in pronouncing additional languages.

This chapter reviews research on the phonetic and phonological production patterns of children exposed to two or more languages in early childhood. Much of the research has examined whether bilingual children differ from monolingual children in their production patterns and, when differences have been found, whether such patterns can be explained by language-internal (e.g., the influence of the phonological properties of one language on the other), language-external (e.g., language input or dominance), or other factors (e.g., the developing lexicon). We focus on language-internal influences, also referred to as cross-linguistic interaction, and review models that have been used to account for interaction. Our findings reveal that, while systematic differences between monolingual and bilingual speech exist, the differences are not large and may be explained by multiple factors. The chapter concludes with a discussion of research limitations which include small participant numbers and a predominance of certain language pairs, factors that have hindered attempts to model cross-linguistic interaction.