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In recent years, philosophical-legal studies on neuroscience (mainly in the fields of neuroethics and neurolaw) have given increasing prominence to a normative analysis of the ethical-legal challenges in the mind and brain sciences in terms of rights, freedoms, entitlements and associated obligations. This way of analyzing the ethical and legal implications of neuroscience has come to be known as "neurorights." Neurorights can be defined as the ethical, legal, social, or natural principles of freedom or entitlement related to a person's cerebral and mental domain; that is, the fundamental normative rules for the protection and preservation of the human brain and mind. Although reflections on neurorights have received ample coverage in the mainstream media and have rapidly become a mainstream topic in the public neuroethics discourse, the frequency of such reflections in the academic literature is still relatively scarce. While the prominence of the neurorights debate in public opinion is crucial to ensure public engagement and democratic participation in deliberative processes on this issue, its relatively sporadic presence in the academic literature poses a risk of semantic-normative ambiguity and conceptual confusion. This risk is exacerbated by the presence of multiple and not always reconcilable terminologies. Several meta-ethical, normative ethical, and legal-philosophical questions need to be solved in order to ensure that neurorights can be used as effective instruments of global neurotechnology governance and be adequately imported into international human rights law. To overcome the shortcomings above, this paper attempts to provide a comprehensive normative-ethical, historical and conceptual analysis of neurorights. In particular, it attempts to (i) reconstruct a history of neurorights and locate these rights in the broader history of idea, (ii) outline a systematic conceptual taxonomy of neurorights, (iii) summarize ongoing policy initiatives related to neurorights, (iv) proactively address some unresolved ethico-legal challenges, and (v) identify priority areas for further academic reflection and policy work in this domain. ; ISSN:1662-5161

The Routledge Handbook of the Ethics of Human Enhancement provides readers with a philosophically rich and scientifically grounded analysis of human enhancement and its ethical implications. A landmark in the academic literature, the volume covers human enhancement in genetic engineering, neuroscience, synthetic biology, regenerative medicine, bioengineering, and many other fields. The Handbook includes a diverse and multifaceted collection of 30 chapters--all appearing here in print for the first time-- that reveal the fundamental ethical challenges related to human enhancement. The chapters have been written by internationally recognized leaders in the field and are organized into seven parts: I. Historical Background and Key ConceptsII. Human Enhancement and Human NatureIII. Physical EnhancementIV. Cognitive EnhancementV. Mood Enhancement and Moral EnhancementVI. Human Enhancement and MedicineVII. Legal, Social, and Political Implications The depth and topical range of the Handbook makes it an essential resource for upper-level undergraduates, graduate students, and postdoctoral fellows in a broad variety of disciplinary areas. Furthermore, it is an authoritative reference for basic scientists, philosophers, engineers, physicians, lawyers, and other professionals who work on the topic of human enhancement.

Intro -- Acknowledgments -- Contents -- About the Authors -- 1: Introduction -- References -- Part I: Big Data and Automated Learning: Scientific and Ethical Considerations -- 2: Big Data in Medical AI: How Larger Data Sets Lead to Robust, Automated Learning for Medicine -- 2.1 Why the Big Data Revolution? -- 2.1.1 More Samples, More Features -- 2.1.2 Hardware Improvements -- 2.2 Precision Medicine -- 2.2.1 Challenges and Opportunities Unique to Mental Health and Wellness -- 2.3 Tools for Big Data in Medicine -- 2.3.1 Standardization Tools -- 2.3.2 Analytics to Leverage Big Data -- 2.3.2.1 Unsupervised and Semi-Supervised Learning Methods -- 2.3.2.2 High-Throughput Model Selection and Testing -- 2.3.2.3 Ensemble Methods -- 2.3.2.4 Deep Learning -- 2.4 Big Data Concerns -- 2.4.1 The Need for Proper Validation -- 2.4.1.1 Test Models with Separating Training, Validation, and Test Sets -- 2.4.1.2 Assure Samples Adequately Represent the Reported Populations and Contexts -- 2.4.1.3 Avoid Contamination Between Training and Test Sets -- 2.4.1.4 Select the Right Performance Metrics -- 2.4.2 Security -- 2.4.3 Ethical Challenges -- 2.5 Conclusion -- References -- 3: Automatic Diagnosis and Screening of Personality Dimensions and Mental Health Problems -- 3.1 Introduction: Automatic Analysis of Personality -- 3.1.1 Personality and Diagnosis -- 3.1.2 Diagnosis Versus Screening -- 3.1.3 The Clinical Versus the Nonclinical Context -- 3.2 Computational Personality Analysis -- 3.2.1 The Relevance of Computational Personality Analysis in the Current Culture -- 3.2.2 Computational Personality Analysis in a Nutshell -- 3.3 Computational Personality Analysis Further Detailed -- 3.4 A Critical Perspective -- 3.5 Summary and Conclusions -- References.

Trust is a ubiquitous term used in emerging technology (e.g., Big Data, precision medicine), innovation policy, and governance literatures in particular. But what exactly is trust? Even though trust is considered a critical requirement for the successful deployment of precision medicine initiatives, nonetheless, there is a need for further conceptualization with regard to what qualifies as trust, and what factors might establish and sustain trust in precision medicine, predictive analytics, and large-scale biology. These new fields of 21st century medicine and health often deal with the "futures" and hence, trust gains a temporal and ever-present quality for both the present and the futures anticipated by new technologies and predictive analytics. We address these conceptual gaps that have important practical implications in the way we govern risk and unknowns associated with emerging technologies in biology, medicine, and health broadly. We provide an in-depth conceptual analysis and an operative definition of trust dynamics in precision medicine. In addition, we identify three main types of "trust facilitators": (1) technical, (2) ethical, and (3) institutional. This three-dimensional framework on trust is necessary to building and maintaining trust in 21st century knowledge-based innovations that governments and publics invest for progressive societal change, development, and sustainable prosperity. Importantly, we analyze, identify, and deliberate on the dimensions of precision medicine and large-scale biology that have carved out trust as a pertinent tool to its success. Moving forward, we propose a "points to consider" on how best to enhance trust in precision medicine and predictive analytics. ; ISSN:1536-2310 ; ISSN:1557-8100

Trust is a ubiquitous term used in emerging technology (e.g., Big Data, precision medicine), innovation policy, and governance literatures in particular. But what exactly is trust? Even though trust is considered a critical requirement for the successful deployment of precision medicine initiatives, nonetheless, there is a need for further conceptualization with regard to what qualifies as trust, and what factors might establish and sustain trust in precision medicine, predictive analytics, and large-scale biology. These new fields of 21st century medicine and health often deal with the "futures" and hence, trust gains a temporal and ever-present quality for both the present and the futures anticipated by new technologies and predictive analytics. We address these conceptual gaps that have important practical implications in the way we govern risk and unknowns associated with emerging technologies in biology, medicine, and health broadly. We provide an in-depth conceptual analysis and an operative definition of trust dynamics in precision medicine. In addition, we identify three main types of "trust facilitators": (1) technical, (2) ethical, and (3) institutional. This three-dimensional framework on trust is necessary to building and maintaining trust in 21st century knowledge-based innovations that governments and publics invest for progressive societal change, development, and sustainable prosperity. Importantly, we analyze, identify, and deliberate on the dimensions of precision medicine and large-scale biology that have carved out trust as a pertinent tool to its success. Moving forward, we propose a "points to consider" on how best to enhance trust in precision medicine and predictive analytics.

Recent advances in military-funded neurotechnology and novel opportunities for misusing neurodevices show that the problem of dual use is inherent to neuroscience. This paper discusses how the neuroscience community should respond to these dilemmas and delineates a neuroscience-specific biosecurity framework. This neurosecurity framework involves calibrated regulation, (neuro)ethical guidelines, and awareness-raising activities within the scientific community.