Research-driven technology development in the fields of the neurosciences presents interesting and potentially complicated issues around data in general and brain data specifically. The data produced from brain recordings are unlike names and addresses in that it may result from the processing of largely involuntarily brain activity, it can be processed and reprocessed for different aims, and it is highly sensitive. Consenting for brain recordings of a specific type, or for a specific purpose, is complicated by these factors. Brain data collection, retention, processing, storage, and destruction are each of high ethical importance. This leads us to ask: Is the present European Data Protection Regulation sufficient to deal with emerging data concerns relating to neurotechnology? This is pressing especially in a context of rapid advancement in the fields of brain computer interfaces (BCIs), where devices that can function via recorded brain signals are expanding from research labs, through medical treatments, and beyond into consumer markets for recreational uses. One notion we develop herein is that there may be no trivial data collection when it comes to brain recording, especially where algorithmic processing is involved. This article provides analysis and discussion of some specific data protection questions related to neurotechnology, especially BCIs. In particular, whether and how brain data used in BCI-driven applications might count as personal data in a way relevant to data protection regulations. It also investigates how the nature of BCI data, as it appears in various applications, may require different interpretations of data protection concepts. Importantly, we consider brain recordings to raise questions about data sensitivity, regardless of the purpose for which they were recorded. This has data protection implications. @en

This paper addresses the question of managing the existential risk potential of general Artificial Intelligence (AI), as well as the more near-term yet hazardous and disruptive implications of specialised AI, from the perspective of a particular research project that could make a significant contribution to the development of Artificial Intelligence (AI): the Human Brain Project (HBP), a ten-year Future and Emerging Technologies Flagship of the European Commission. The HBP aims to create a digital research infrastructure for brain science, cognitive neuroscience, and brain-inspired computing. This paper builds on work undertaken in the HBP's Ethics and Society subproject (SP12). Collaborators from two activities in SP12, Foresight and Researcher Awareness on the one hand, and Ethics Management on the other, use the case of machine intelligence to illustrate key aspects of the dynamic processes through which questions of ethics and society, including existential risks, are approached in the organisational context of the HBP. The overall aim of the paper is to provide practice-based evidence, enriched by self-reflexive assessment of the approach used and its limitations, for guiding policy makers and communities who are, and will be, engaging with such questions.@en

Objective: We describe current practices of ethics-related data governance in large neuro-ICT projects, identify gaps in current practice, and put forward recommendations on how to collaborate ethically in complex regulatory and normative contexts. Methods: We undertake a survey of published principles of data governance of large neuro-ICT projects. This grounds an approach to a normative analysis of current data governance approaches. Results: Several ethical issues are well covered in the data governance policies of neuro-ICT projects, notably data protection and attribution of work. Projects use a set of similar policies to ensure users behave appropriately. However, many ethical issues are not covered at all. Implementation and enforcement of policies remain vague. Conclusions: The data governance policies we investigated indicate that the neuro-ICT research community is currently close-knit and that shared assumptions are reflected in infrastructural aspects. This explains why many ethical issues are not explicitly included in data governance policies at present. With neuro-ICT research growing in scale, scope, and international involvement, these shared assumptions should be made explicit and reflected in data governance.@en