Philipp Kellmeyer

Definition of the problem In this article, a review of neuroethics is provided. Building on an earlier paper by Müller et al. (2018), we identify continuities and shed light on more recent important developments. Arguments In particular, we show that neuroethics is partly confronted with new substantive challenges. In addition, the question of how to deal with diversity plays a growing role: neuroethics is increasingly reflecting on the extent to which certain social groups are under- or overrepresented in research and ethical discourse, as well as the resulting blind spots. In this context, adopting a global perspective that takes cultural plurality into account is becoming increasingly important for neuroethics. Finally, we point out existing and emerging forms of institutionalization and networking with which the scientific community is responding to these challenges. Conclusion We argue that neuroethics should focus more on inclusive and participatory research concepts.

The development of non-medical consumer neurotechnology is gaining momentum. As companies chart the course for future implanted and invasive brain-computer interfaces (BCIs) in non-medical populations, the time has come for concrete steps toward their regulation. We propose three measures: First, a mandatory Mental Impact Assessment that comprehensively screens for adverse mental effects of neurotechnologies under realistic use conditions needs to be developed and implemented. Second, until such an assessment is developed and further ethical concerns are effectively resolved, a moratorium on placing implantable non-medical devices on markets should be established. Third, implantable consumer neurotech for children should be banned. These measures are initial steps in a process seeking to define the necessary requirements for placing these devices on markets. They are grounded in a human rights-based approach to technology regulation that seeks to promote the interests protected by human rights while minimizing the risks posed to them. Neurotechnologies have the potential to profoundly alter cognitive, emotional, and other mental processes, with implications for the rights to mental health and integrity, and possibly for societal dynamics.

Background: The integration of artificial intelligence (AI) into health care has become a crucial element in the digital transformation of health systems worldwide. Despite the potential benefits across diverse medical domains, a significant barrier to the successful adoption of AI systems in health care applications remains the prevailing low user trust in these technologies. Crucially, this challenge is exacerbated by the lack of consensus among experts from different disciplines on the definition of trust in AI within the health care sector. Objective: We aimed to provide the first consensus-based analysis of trust in AI in health care based on an interdisciplinary panel of experts from different domains. Our findings can be used to address the problem of defining trust in AI in health care applications, fostering the discussion of concrete real-world health care scenarios in which humans interact with AI systems explicitly. Methods: We used a combination of framework analysis and a 3-step consensus process involving 18 international experts from the fields of computer science, medicine, philosophy of technology, ethics, and social sciences. Our process consisted of a synchronous phase during an expert workshop where we discussed the notion of trust in AI in health care applications, defined an initial framework of important elements of trust to guide our analysis, and agreed on 5 case studies. This was followed by a 2-step iterative, asynchronous process in which the authors further developed, discussed, and refined notions of trust with respect to these specific cases. Results: Our consensus process identified key contextual factors of trust, namely, an AI system’s environment, the actors involved, and framing factors, and analyzed causes and effects of trust in AI in health care. Our findings revealed that certain factors were applicable across all discussed cases yet also pointed to the need for a fine-grained, multidisciplinary analysis bridging human-centered and technology-centered approaches. While regulatory boundaries and technological design features are critical to successful AI implementation in health care, ultimately, communication and positive lived experiences with AI systems will be at the forefront of user trust. Our expert consensus allowed us to formulate concrete recommendations for future research on trust in AI in health care applications. Conclusions: This paper advocates for a more refined and nuanced conceptual understanding of trust in the context of AI in health care. By synthesizing insights into commonalities and differences among specific case studies, this paper establishes a foundational basis for future debates and discussions on trusting AI in health care.

Phenomenological interview methods (PIMs) have become important tools for investigating subjective, first-person accounts of the novel experiences of people using neurotechnologies. Through the deep exploration of personal experience, PIMs help reveal both the structures shared between and notable differences across experiences. However, phenomenological methods vary on what aspects of experience they aim to capture and what they may overlook. Much discussion of phenomenological methods has remained within the philosophical and broader bioethical literature. Here, we begin with a conceptual primer and preliminary guide for using phenomenological methods to investigate the experiences of neural device users.To improve and expand the methodology of phenomenological interviewing, especially in the context of the experience of neural device users, we first briefly survey three different PIMs, to demonstrate their features and shortcomings. Then we argue for a critical phenomenology—rejecting the ‘neutral’ phenomenological subject—that encompasses temporal and ecological aspects of the subjects involved, including interviewee and interviewer (e.g. age, gender, social situation, bodily constitution, language skills, potential cognitive disease-related impairments, traumatic memories) as well as their relationality to ensure embedded and situated interviewing. In our view, PIMs need to be based on a conception of experience that includes and emphasizes the relational and situated, as well as the anthropological, political and normative dimensions of embodied cognition.We draw from critical phenomenology and trauma-informed qualitative work to argue for an ethically sensitive interviewing process from an applied phenomenological perspective. Drawing on these approaches to refine PIMs, researchers will be able to proceed more sensitively in exploring the interviewee’s relationship with their neuroprosthetic and will consider the relationship between interviewer and interviewee on both interpersonal and social levels.

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.

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.

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.

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.

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.

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.

Abstract:Highly immersive virtual reality (VR) systems have been introduced into the consumer market in recent years. The improved technological capabilities of these systems as well as the combination with biometric sensors, for example electroencephalography (EEG), in a closed-loop hybrid VR-EEG, opens up a range of new potential medical applications. This article first provides an overview of the past and current clinical applications of VR systems in neurology and psychiatry and introduces core concepts in neurophilosophy and VR research (such as agency, trust, presence, and others). Then, important adverse effects of highly immersive VR simulations and the ethical implications of standalone and hybrid VR systems for therapy in neurology and psychiatry are highlighted. These new forms of VR-based therapy may strengthen patients in exercising their autonomy. At the same time, however, these emerging systems present ethical challenges, for example in terms of moral and legal accountability in interactions involving “intelligent” hybrid VR systems. A user-centered approach that is informed by the target patients’ needs and capabilities could help to build beneficial systems for VR therapy.

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.