Philipp Kellmeyer

Advancements in novel neurotechnologies, such as brain computer interfaces and neuromodulatory devices such as deep brain stimulators, will have profound implications for society and human rights. While these technologies are improving the diagnosis and treatment of mental and neurological diseases, they can also alter individual agency and estrange those using neurotechnologies from their sense of self, challenging basic notions of what it means to be human. As an international coalition of interdisciplinary scholars and practitioners, we examine these challenges and make recommendations to mitigate negative consequences that could arise from the unregulated development or application of novel neurotechnologies. We explore potential ethical challenges in four key areas: identity and agency, privacy, bias, and enhancement. To address them, we propose democratic and inclusive summits to establish globally-coordinated ethical and societal guidelines for neurotechnology development and application, new measures, including “Neurorights,” for data privacy, security, and consent to empower neurotechnology users’ control over their data, new methods of identifying and preventing bias, and the adoption of public guidelines for safe and equitable distribution of neurotechnological devices.

The rise of neurotechnologies, especially in combination with artificial intelligence (AI)-based methods for brain data analytics, has given rise to concerns around the protection of mental privacy, mental integrity and cognitive liberty – often framed as “neurorights” in ethical, legal, and policy discussions. Several states are now looking at including neurorights into their constitutional legal frameworks, and international institutions and organizations, such as UNESCO and the Council of Europe, are taking an active interest in developing international policy and governance guidelines on this issue. However, in many discussions of neurorights the philosophical assumptions, ethical frames of reference and legal interpretation are either not made explicit or conflict with each other. The aim of this multidisciplinary work is to provide conceptual, ethical, and legal foundations that allow for facilitating a common minimalist conceptual understanding of mental privacy, mental integrity, and cognitive liberty to facilitate scholarly, legal, and policy discussions.

Definition of the problem This article critically addresses the conceptualization of trust in the ethical discussion on artificial intelligence (AI) in the specific context of social robots in care. First, we attempt to define in which respect we can speak of ‘social’ robots and how their ‘social affordances’ affect the human propensity to trust in human–robot interaction. Against this background, we examine the use of the concept of ‘trust’ and ‘trustworthiness’ with respect to the guidelines and recommendations of the High-Level Expert Group on AI of the European Union. Arguments Trust is analyzed as a multidimensional concept and phenomenon that must be primarily understood as departing from trusting as a human functioning and capability. To trust is an essential part of the human basic capability to form relations with others. We further want to discuss the concept of _responsivity _which has been established in phenomenological research as a foundational structure of the relation between the self and the other. We argue that trust and trusting as a capability is fundamentally _responsive_ and needs responsive others to be realized. An understanding of _responsivity_ is thus crucial to conceptualize trusting in the ethical framework of human flourishing. We apply a phenomenological–anthropological analysis to explore the link between certain qualities of social robots that construct responsiveness and thereby simulate responsivity and the human propensity to trust. Conclusion Against this background, we want to critically ask whether the concept of trustworthiness in social human–robot interaction could be misguided because of the limited ethical demands that the constructed responsiveness of social robots is able to answer to.

Auf den Menschen bezogen sind unter Neurotechnologien im weitesten Sinn alle jene Geräte und Forschungsmethoden zu verstehen, bei denen das menschliche Nervensystem, insbesondere das Gehirn, im Zentrum der Beobachtung steht oder Objekt der technischen Interaktion ist. Unter diese weit gefasste Definition fallen, neben den verbreiteten Geräten zur Bildgebung des Gehirns, den Methoden zur Messung der elektrischen Gehirnaktivität mittels Elektroenzephalographie oder Geräten zur elektrischen Stimulation des Gehirns, letztlich aber auch Methoden zur feingeweblichen Untersuchung von Gehirnzellen und zellulären Funktionen.

The increasing availability of brain data within and outside the biomedical field, combined with the application of artificial intelligence (AI) to brain data analysis, poses a challenge for ethics and governance. We identify distinctive ethical implications of brain data acquisition and processing, and outline a multi-level governance framework. This framework is aimed at maximizing the benefits of facilitated brain data collection and further processing for science and medicine whilst minimizing risks and preventing harmful use. The framework consists of four primary areas of regulatory intervention: binding regulation, ethics and soft law, responsible innovation, and human rights.

In the past decade, artificial intelligence has become a disruptive force around the world, offering enormous potential for innovation but also creating hazards and risks for individuals and the societies in which they live. This volume addresses the most pressing philosophical, ethical, legal, and societal challenges posed by AI. Contributors from different disciplines and sectors explore the foundational and normative aspects of responsible AI and provide a basis for a transdisciplinary approach to responsible AI. This work, which is designed to foster future discussions to develop proportional approaches to AI governance, will enable scholars, scientists, and other actors to identify normative frameworks for AI to allow societies, states, and the international community to unlock the potential for responsible innovation in this critical field. This book is also available as Open Access on Cambridge Core.

Extended Reality (XR) systems, such as Virtual Reality (VR) and Augmented Reality (AR), provide a digital simulation either of a complete environment, or of particular objects within the real world. Today, XR is used in a wide variety of settings, including gaming, design, engineering, and the military. In addition, XR has been introduced into psychology, cognitive sciences and biomedicine for both basic research as well as diagnosing or treating neurological and psychiatric disorders. In the context of XR, the simulated ‘reality’ can be controlled and people may safely learn to cope with their feelings and behavior. XR also enables to simulate environments that cannot easily be accessed or created otherwise. Therefore, Extended Reality systems are thought to be a promising tool in the resocialization of criminal offenders, more specifically for purposes of risk assessment and treatment of forensic patients. Employing XR in forensic settings raises ethical and legal intricacies which are not raised in case of most other healthcare applications. Whereas a variety of normative issues of XR have been discussed in the context of medicine and consumer usage, the debate on XR in forensic settings is, as yet, straggling. By discussing two general arguments in favor of employing XR in criminal justice, and two arguments calling for caution in this regard, the present paper aims to broaden the current ethical and legal debate on XR applications to their use in the resocialization of criminal offenders, mainly focusing on forensic patients.

The focus of this paper are the ethical, legal and social challenges for ensuring the responsible use of “big brain data”—the recording, collection and analysis of individuals’ brain data on a large scale with clinical and consumer-directed neurotechnological devices. First, I highlight the benefits of big data and machine learning analytics in neuroscience for basic and translational research. Then, I describe some of the technological, social and psychological barriers for securing brain data from unwarranted access. In this context, I then examine ways in which safeguards at the hardware and software level, as well as increasing “data literacy” in society, may enhance the security of neurotechnological devices and protect the privacy of personal brain data. Regarding ethical and legal ramifications of big brain data, I first discuss effects on the autonomy, the sense of agency and authenticity, as well as the self that may result from the interaction between users and intelligent, particularly closed-loop, neurotechnological devices. I then discuss the impact of the “datafication” in basic and clinical neuroscience research on the just distribution of resources and access to these transformative technologies. In the legal realm, I examine possible legal consequences that arises from the increasing abilities to decode brain states and their corresponding subjective phenomenological experiences on the hitherto inaccessible privacy of these information. Finally, I discuss the implications of big brain data for national and international regulatory policies and models of good data governance.

Currently, many scientific fields such as psychology or biomedicine face a methodological crisis concerning the reproducibility, replicability, and validity of their research. In neuroimaging, similar methodological concerns have taken hold of the field, and researchers are working frantically toward finding solutions for the methodological problems specific to neuroimaging. This article examines some ethical and legal implications of this methodological crisis in neuroimaging. With respect to ethical challenges, the article discusses the impact of flawed methods in neuroimaging research in cognitive and clinical neuroscience, particularly with respect to faulty brain-based models of human cognition, behavior, and personality. Specifically examined is whether such faulty models, when they are applied to neurological or psychiatric diseases, could put patients at risk, and whether this places special obligations on researchers using neuroimaging. In the legal domain, the actual use of neuroimaging as evidence in United States courtrooms is surveyed, followed by an examination of ways that the methodological problems may create challenges for the criminal justice system. Finally, the article reviews and promotes some promising ideas and initiatives from within the neuroimaging community for addressing the methodological problems.