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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.

Our scoping review aims to assess what legal, ethical, and socio-technical factors contribute or inhibit the success of national eHealth system implementations. In addition, our review seeks to describe the characteristics and benefits of eHealth systems. We conducted a scoping review of literature published in English between January 2000 and 2020 using a keyword search on five databases; PubMed, Scopus, Web of Science, IEEEXplore, and ProQuest. After removal of duplicates, abstract screening and full-text filtering, 86 articles were included from 8276 search results. We identified 17 stakeholder groups, 6 eHealth Systems areas, and 15 types of legal regimes and standards. In-depth textual analysis revealed challenges mainly in implementation, followed by ethico-legal and data related aspects. Key factors influencing success include promoting trust of the system, ensuring wider acceptance amongst users, reconciling the system with legal requirements and ensuring an adaptable technical platform. Results revealed support for decentralised implementations because they carry less implementation and engagement challenges than centralised ones. Simultaneously, due to decentralised systems interoperability issues, federated implementations (with a set of national standards) might be preferable. This study identifies the primary socio-technical, legal and ethical factors that challenge and contribute to the success of eHealth system implementations. This study also describes the complexities and characteristics of existing eHealth implementation programs, and surmises suggested guidance for resolving the identified challenges., Comment: 28 pages, 4 figures, 2 tables

Multisite medical data sharing is critical in modern clinical practice and medical research. The challenge is to conduct data sharing that preserves individual privacy and data usability. The shortcomings of traditional privacy-enhancing technologies mean that institutions rely on bespoke data sharing contracts. These contracts increase the inefficiency of data sharing and may disincentivize important clinical treatment and medical research. This paper provides a synthesis between two novel advanced privacy enhancing technologies (PETs): Homomorphic Encryption and Secure Multiparty Computation (defined together as Multiparty Homomorphic Encryption or MHE). These PETs provide a mathematical guarantee of privacy, with MHE providing a performance advantage over separately using HE or SMC. We argue MHE fulfills legal requirements for medical data sharing under the General Data Protection Regulation (GDPR) which has set a global benchmark for data protection. Specifically, the data processed and shared using MHE can be considered anonymized data. We explain how MHE can reduce the reliance on customized contractual measures between institutions. The proposed approach can accelerate the pace of medical research whilst offering additional incentives for healthcare and research institutes to employ common data interoperability standards., Comment: 19 pages, 2 figures, 1 table

Our chapter presents a scoping review of published scientific studies or case studies of scientific studies that utilise both citizen scientists and Internet of Things devices. Specifically, we selected studies where the authors had included at least a short discussion of the ethical issues encountered during the research process. Having conducted a search of five databases (IEEE Xplore, Scopus, Web of Science, ProQuest, and PubMed), we identified 631 potential results. Following abstract and title screening, and then full text eligibility assessment, we identified 34 published articles that matched our criteria. We then analysed the full text for these articles inductively and deductively, coding ethical issues into three main categories. These categories were autonomy and data privacy, data quality, and intellectual property. We also analysed the full text of these articles to see what strategies researchers took to resolve these ethical issues, as well as any legal implications raised. Following this analysis, our discussion provides recommendations for researchers who wish to integrate citizen scientists and Internet of Things devices into their research. First, all citizen science projects should integrate a data privacy protocol to protect the confidentiality of participants. Secondly, scientific researchers should consider any potential issues of data quality, including whether compromises might be required, before establishing a project. Finally, all intellectual property issues should be clarified both at the start of the project and during its lifecycle. Researchers should also consider any ethical issues that might flow from the use of commercially available Internet of Things devices for research., Comment: Authors' version; 17 pages, 4 tables

Data collection and processing via digital public health technologies are being promoted worldwide by governments and private companies as strategic remedies for mitigating the COVID-19 pandemic and loosening lockdown measures. However, the ethical and legal boundaries of deploying digital tools for disease surveillance and control purposes are unclear, and a rapidly evolving debate has emerged globally around the promises and risks of mobilizing digital tools for public health. To help scientists and policymakers navigate technological and ethical uncertainty, we present a typology of the primary digital public health applications currently in use. Namely: proximity and contact tracing, symptom monitoring, quarantine control, and flow modeling. For each, we discuss context-specific risks, cross-sectional issues, and ethical concerns. Finally, in recognition of the need for practical guidance, we propose a navigation aid for policymakers made up of ten steps for the ethical use of digital public health tools., Comment: 14 pages, 3 figures

In the last five years, private companies, research institutions as well as public sector organisations have issued principles and guidelines for ethical AI, yet there is debate about both what constitutes "ethical AI" and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analyzed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted; why they are deemed important; what issue, domain or actors they pertain to; and how they should be implemented. Our findings highlight the importance of integrating guideline-development efforts with substantive ethical analysis and adequate implementation strategies., Comment: 42 pages (incl. figures and supplementary information)