Internet of Thing (IoT)s. It focuses in particular on the question of liability in circumstances where an IoT system has not performed as expected and where this has resulted in loss or damage. The authors argue that the combination of AI and the IoT raises several novel aspects concerning the basis for assessing responsibility and of allocating liability for loss or damage, and that this will necessitate the development of a more creative approach to liability than generally followed in many legal systems. Linear liability based on contractual relationships and fault-based or strict liability of a wrongdoer in tort law are no longer sufficient to deal with the complex issues associated with the interaction of AI and the IoT. According to the authors, the values underpinning established liability systems, particularly in the field of consumer protection law, should be maintained in the context of new digital technology applications. The adoption of new digital technology applications cannot be a basis for imposing a lower threshold of liability than the level of liability established in other contexts.

Stigma serves as the mechanism through which an individual is disqualified from full social acceptance because they exhibit an attitude, a behavior, or a characteristic that is regarded as socially unacceptable. For men, this is most often experienced when they violate the socialized male gender role, often in the form of appearing feminine, weak, suffering from psychological distress, or asking for psychological help. Men subsequently adjust their behaviors to conform to the socialized traditional male gender role regardless of the interpersonal and/or psychological consequences. The purpose of this chapter is to summarize the predominant theories of masculinity and articulate the linkages between those theories and the current understanding of mental health stigma. An overview of the small (but growing) body of literature linking mental health stigma to men, masculinity, and a variety of outcomes is then provided. Finally, the chapter concludes with a discussion of the broader implications these concepts have for mental health stigma research as well as the psychology of men and areas needing further research.

Since the second AI revolution started around 2009, society has witnessed new and steadily more impressive applications of AI. The growth of practical applications has in turn led to a growing body of literature devoted to the liability issues of AI. The present text is an attempt to assess the current state of the law and to advance the discussion.

Advanced artificial intelligence (AI) or superintelligence promises great disruption in the law, economy, and society. The world is close to reaching an inflection point; the so-called existential threat of superintelligence with the potential of replacing human control and decision-making with its creation. The focus should be on mitigating the negative effects of disruption and using smart design to prevent AI from ever becoming an existential threat to humankind.

Albeit the UN Convention of the Rights of Persons with Disabilities advocates for the right to full and active participation is society, it is well known that individuals with intellectual disabilities face great disadvantages in most domains of life. Stigma and discrimination are at the roots of the limited life opportunities available to this group of individuals. This chapter explores the state of the evidence in the field of intellectual disability stigma published since 2016. Specifically, we review 29 studies that dealt with public stigma, professional stigma, self-stigma, and family or affiliate stigma. Further, we review studies that have focused on stigma change interventions during these years. In our last section we provide a summary of the findings as well as suggestions for future research.

The rapid development of robotics and intelligent systems raises the issue of how to adapt the legal framework to accidents arising from devices based on artificial intelligence (AI) and machine learning. In the light of the numerous legal studies published in recent years, it is clear that ‘tort law and AI’ has become one of the hot topics of legal scholarship both in national and comparative contexts.

Secreting a diffuse liability, potentially involving a large chain of actors (the designers and managers of the system, the authority having authorized it, the vehicle manufacturer, the intelligent road network operator and the driver), autonomous circulation defies Liability Law with regard to the requirement to establish a fault or at least accountability. Accordingly, the complex system of algorithms allowing autonomous circulation disrupts these classical mechanisms of liability, which do not appear to be able to meet the contemporary concern of guaranteeing compensation to victims of accidents caused by these vehicles.

This chapter reviews the theoretical and research literature on self-, public, and structural stigma and stigma’s impact on mental health for the largest ethnic minority groups in the United States: African Americans, Latinx, Asian Americans, and Native Americans. None of these ethnic minority groups receives mental health treatment commensurate with treatment need. Research documents that stigma deters minority mental health help seeking, especially for Asian and African Americans. Limited research suggests that pubic and structural stigma may interfere more with access to high-quality care and success in community functioning, although suitably formulated hypotheses remain to be tested. As researchers move beyond ethnic categorization for studying stigma disparity’s role, they must better specify cultural differences explaining minority-White disparities in stigma. They must also further explain stigma disparities in comprehensive models that explain how stigma disparities explain disparities in minority help seeking. Findings can inform culturally attuned anti-stigma interventions and public health messages to reach ethnic minority communities and guide outreach by trusted actors and institutions seeking to break down stigma’s barriers, recruit more minority persons into care, and provide a welcoming environment for successful community living.

The chapter analyses the impact of AI and IP Law. In August 2019, news reports carried stories about the first patent applications naming an AI algorithm, called DABUS, as an inventor on patent applications. Almost immediately, the United States Patent Office published a request for comments, asking questions about how it should approach AI and patent law. Less than a year later, the questions were seemingly definitively resolved.

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.

AI has profound ethical implications and poses grave threats to humanity, whereas a theoretical framework for determining what it can and cannot do or for what it should and should not do, has yet to be developed. That gap is understandable as applying ethics and law to AI is not easy and doing so raises deep philosophical problems. The result is a formidable strategic challenge. On one hand, we may make fundamental errors if we try to sidestep foundational issues to arrive at pragmatic solutions. On the other hand, we may get bogged down trying to solve those foundational issues. This chapter attempts to steer a middle course - addressing the deep problems while avoiding gratuitous philosophical commitments. In other words, the chapter attempts to confront philosophical issues to the extent - but only to the extent - necessary to chart a way forward. Human beings may have an ongoing role to play in supervising AI from ethical and legal perspectives, despite impressive technological advances. The chapter identifies key challenges for regulating AI and for AI as a legal regulator, sketching a general framework for understanding the relationship between AI, consciousness, ethics, and law.

Members of the Lesbian, Gay, Bisexual, Transgender, and Queer (LGBTQ+) population have a unique relationship with stigma, as this community experiences both stigma associated with identifying within the LGBTQ+ spectrum and may also navigate the unique stigmas associated with mental illness and seeking help. It is important for researchers and practitioners to understand the historical roots of these stigmata as a means of understanding the present landscape of LGBTQ+ stigma. This chapter reviews the literature of LGBTQ+ mental health stigma (i.e., mental illness, help seeking, and structural stigmas). Additionally, this chapter presents an introduction to using intersectionality as means of understanding LGBTQ+ stigmata. Research and clinical applications are discussed.

In the context of the legal industry, examples of AI applications inlcude automation in case flow management, contract review, and legal research. This chapter reviews three stages of technological appplication in the legal profession. The legal industry must look toward how lawyers can manage AI and reframe AI technologies as tools that emphasize the value of the human legal decision maker.

Research on the measurement of mental illness stigma and discrimination has grown rapidly in the past 15 years with a large number of measures developed. This chapter first defines mental illness stigma and discrimination and highlights the importance of using an appropriately targeted measurement strategy including consideration of key measurement principles such as content validity, context of use, and psychometric properties. Nine commonly used measures of perceived, experienced, and self -stigma and discrimination are then highlighted with measurement considerations summarized. We also discuss global and local measurement issues including translation and cross-cultural adaptation. Future directions for stigma and discrimination measurement research in mental illness stigma and discrimination are presented including the need to ensure that research includes consideration of complexity and variation in the experience of stigma and discrimination and that research is focused proportionately on communities that experience the most mental illness stigma and discrimination.