There is a red thread that is of interest for antitrust experts, which links together the foundational elements of contracts, as comparatively detectable in modern systems of law, and the more specific notion of cartels or concerted practices. Both of these disciplines have as their basis some form of mutual understanding between parties aimed at coordinating the behaviour of two or more subjects according to a certain 'common meeting of the mind'.

Consumer data has become a driving force in the digital economy. As the number of data interactions increases, so too the insights into ever more intimate aspects of one’s daily life, behaviour and personality. Amongst the various products and services, one innovative advancement in the world of data-driven technology stands out as deserving particular attention: the capability to infer emotions from (personal) data and to use such information to respond to an individual’s needs on a highly intimate level. Whereas the technology has considerable potential, it is controversial not least due to the highly sensitive and private nature of emotions but also due to its questionable reliability as well as potential adverse effects. The authors indicate that the legal classification of emotions under EU data protection law is a grey area, before highlighting particular concerns in relation to the technology. With reference to the recent EU proposal for an ‘Artificial Intelligence Act’, the chapter focuses on how instruments in EU consumer law could alleviate certain asymmetries in power and information, and thereby allow for emotion AI to serve consumer needs

Help seeking for mental health problems is a multifaceted and dynamic process involving both formal and informal networks that has many associated barriers. One of the prominent barriers is help-seeking stigma, which is stigma associated with asking for or receiving help. This stigma can emerge even during very early stages of the development of a mental health problem, leading to delays in receiving any care or support. The aim of help-seeking interventions is to mitigate the barriers associated with help seeking. Much of the research surrounding help-seeking interventions focuses on increasing mental health literacy, and developing cognitive techniques surrounding help seeking for improving mental health and reducing stigma by applying strategies including: psychoeducation, contact, and resource sharing. Systematic reviews show that the majority of interventions target formal help seeking. Research that is more recent has highlighted the potential benefits of online help-seeking interventions due to its accessibility and cost-effectiveness. This chapter reviews the current challenges of help-seeking interventions and future direction of research.

It is only recently that the EPO’s Boards of Appeal have had to deal with appeals relating to the surge of AI based inventions. In doing so the Boards of Appeal have adopted a gradualist approach, adapting the extensive EPO case law relating to the patentability of computer programs ‘as such’ and applying it to AI inventions. The most recent change to the Guidelines indicates the EPO’s willingness to adapt to technological developments and to refine its approach to patentability of inventions involving AI, while at the same time taking a firm line against patenting non-technical inventions.

Stigma can maintain discrimination and oppression and reduce compassion and understanding. In the area of mental illness and psychological help seeking, stigma acts as a considerable barrier to recovery and adds additional burdens to be managed. This reality has led many different research groups to explore the workings of stigma and ways to intervene to help people who suffer from the stigma associated with mental health problems. We wanted to create a state-of-the-science source for the best research being done in this area and so we organized the Handbook of Stigma and Mental Health. This chapter provides an overview to the Handbook and the excellent research that is reviewed in it. In their chapters, the authors of the Handbook answer four important questions: “What are the forms of mental health stigma?”; “What are impacts of mental health stigma?”; “How can we develop interventions to reduce mental health stigma across contexts?”; “How can we understand the specific ways that mental health stigma impacts different groups (e.g., racial minorities, veterans)?” We hope that asking these questions will stimulate and drive more innovative research in the future.

Stigma is a powerful force that is not easily dismantled. The goal of the Handbook of Stigma and Mental Health is to assist with policy changes, interventions, and movement toward social justice by presenting the breadth and depth of the work on mental health stigma. The authors of the Handbook have provided a deep and more complete picture of what stigma is, how it might develop, and how it might be changed. The authors also have provided a clear picture that stigma cannot be understood in isolation, but rather intersectional and contextual approaches are best. Through the work reviewed by the authors of the Handbook, it is clear that research is still needed to expand on what situations and under what conditions stigmas could be minimized, reduced, buffered, or eliminated. Also, work needs to be done to create culturally affirming approaches to stigma reduction. We believe the work presented in the Handbook provides optimism about the changes that have been made and the progress in our knowledge and interventions. It also provides insights into developing unique perspectives on the field, challenging some of our well-worn ideas, and pushing the limits of our knowledge.

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.

The chapter addresses the question of how to continue developing artificial intelligence (AI) without challenging and infringing legal norms, principles and values, represented by the current legal frameworks of liberal democratic societies. To answer this question, the chapter first of all briefly deals with the concept of legality (what it means to be legal in the age of disruptive technologies) and then relates it to two specific private law challenges: The first challenge is related to intellectual property law and is represented by the clash between trade secret protection of algorithms and the increasing public need for algorithmic transparency and explicability; the second challenge is related to consumer protection where the questions of liability and the shifting roles of the main stakeholders build the space for discussing who is who in building, developing and using AI.

Older adults are the least likely age group to seek mental health services and stigma is frequently cited as a key explanation. Guided by the internalized stigma of help-seeking model, the first objective of this chapter is to review research examining age differences in public stigmas, self-stigmas, help-seeking attitudes, and intentions to seek help. With some exceptions, the bulk of this research suggests that stigmas, attitudes, and intentions are, in fact, more positive in later life. The second objective of the chapter is to examine the current state of research focusing on anti-stigma interventions among older adults. Unfortunately, a key conclusion from our review of this research is that older adults are vastly underrepresented in stigma intervention work. Most participants in meta-analyses and reviews of stigma interventions are teenagers and adults in their 20s and 30s. Only a handful of studies have targeted intervention work toward older adults, with promising results. We conclude by highlighting additional work that needs to be done to understand how age interacts with stigmas and related constructs, and how to improve them through intervention work. These efforts have the potential to improve the lives of a large and quickly growing segment of our population.

This chapter examines the intersection of stigma and mental health in certain sects of Abrahamic religious traditions (including sects of Christianity, Islam, and Judaism). Research has shown people in many religious communities underutilize mental health services. Although there are numerous reasons for this underutilization, the stigma against mental health professionals and treatment in religious communities – religious mental health stigma – and the historical antagonism of psychologists against religious communities are two major reasons. This chapter reviews these factors and discusses how religious communities and mental health professionals can bridge the schism between their groups. We argue that by understanding the needs of religious communities, working within their worldviews, and engaging in respectful ways, psychological researchers and clinicians can build bridges that surmount stigma and other barriers and promote the best care for people in need.

The present chapter provides a review and analysis of research on mental health stigma among military personnel. Given expectations of psychological resilience for military personnel, a large amount of research has been conducted to examine the antecedents and consequences of mental health stigma in this population. The chapter first provides an analysis of the different types of mental health stigma that have been examined, including their definitions and assessments. The chapter then addresses the antecedents of mental health stigma in the military, including demographics, mental health symptoms, unit factors, and personality/individual differences. The consequences of mental health stigma are then examined, including treatment- seeking intentions and treatment seeking, mental health symptoms, treatment dropout, and suicidal ideation. Training and interventions to reduce mental health stigma are then discussed. The chapter concludes with recommendations for future research, including a larger focus on unit/organizational factors that influence mental health stigma in the military.

AI is a complex, multifaceted concept and is therefore hard to define because AI can refer to technological artifacts, certain methods or a scientific field that is split into many subfields and that is continuously changing and evolving AI systems can therefore be seen as digital artifacts that require hardware and software components and that contain at least one learning or learned component, i.e., a component that is able to change the system’s behavior based on presented data and the processing of this data.