Towards an understanding of global brain data governance: ethical positions that underpin global brain data governance discourse.

Introduction: The study of the brain continues to generate substantial volumes of data, commonly referred to as "big brain data," which serves various purposes such as the treatment of brain-related diseases, the development of neurotechnological devices, and the training of algorithms. This big brain data, generated in different jurisdictions, is subject to distinct ethical and legal principles, giving rise to various ethical and legal concerns during collaborative efforts. Understanding these ethical and legal principles and concerns is crucial, as it catalyzes the development of a global governance framework, currently lacking in this field. While prior research has advocated for a contextual examination of brain data governance, such studies have been limited. Additionally, numerous challenges, issues, and concerns surround the development of a contextually informed brain data governance framework. Therefore, this study aims to bridge these gaps by exploring the ethical foundations that underlie contextual stakeholder discussions on brain data governance. Method: In this study we conducted a secondary analysis of interviews with 21 neuroscientists drafted from the International Brain Initiative (IBI), LATBrain Initiative and the Society of Neuroscientists of Africa (SONA) who are involved in various brain projects globally and employing ethical theories. Ethical theories provide the philosophical frameworks and principles that inform the development and implementation of data governance policies and practices. Results: The results of the study revealed various contextual ethical positions that underscore the ethical perspectives of neuroscientists engaged in brain data research globally. Discussion: This research highlights the multitude of challenges and deliberations inherent in the pursuit of a globally informed framework for governing brain data. Furthermore, it sheds light on several critical considerations that require thorough examination in advancing global brain data governance.

Addressing Global Environmental Challenges to Mental Health Using Population Neuroscience: A Review.

Importance: Climate change, pollution, urbanization, socioeconomic inequality, and psychosocial effects of the COVID-19 pandemic have caused massive changes in environmental conditions that affect brain health during the life span, both on a population level as well as on the level of the individual. How these environmental factors influence the brain, behavior, and mental illness is not well known. Observations: A research strategy enabling population neuroscience to contribute to identify brain mechanisms underlying environment-related mental illness by leveraging innovative enrichment tools for data federation, geospatial observation, climate and pollution measures, digital health, and novel data integration techniques is described. This strategy can inform innovative treatments that target causal cognitive and molecular mechanisms of mental illness related to the environment. An example is presented of the environMENTAL Project that is leveraging federated cohort data of over 1.5 million European citizens and patients enriched with deep phenotyping data from large-scale behavioral neuroimaging cohorts to identify brain mechanisms related to environmental adversity underlying symptoms of depression, anxiety, stress, and substance misuse. Conclusions and Relevance: This research will lead to the development of objective biomarkers and evidence-based interventions that will significantly improve outcomes of environment-related mental illness.

A systematic review of artificial intelligence impact assessments.

Artificial intelligence (AI) is producing highly beneficial impacts in many domains, from transport to healthcare, from energy distribution to marketing, but it also raises concerns about undesirable ethical and social consequences. AI impact assessments (AI-IAs) are a way of identifying positive and negative impacts early on to safeguard AI's benefits and avoid its downsides. This article describes the first systematic review of these AI-IAs. Working with a population of 181 documents, the authors identified 38 actual AI-IAs and subjected them to a rigorous qualitative analysis with regard to their purpose, scope, organisational context, expected issues, timeframe, process and methods, transparency and challenges. The review demonstrates some convergence between AI-IAs. It also shows that the field is not yet at the point of full agreement on content, structure and implementation. The article suggests that AI-IAs are best understood as means to stimulate reflection and discussion concerning the social and ethical consequences of AI ecosystems. Based on the analysis of existing AI-IAs, the authors describe a baseline process of implementing AI-IAs that can be implemented by AI developers and vendors and that can be used as a critical yardstick by regulators and external observers to evaluate organisations' approaches to AI.

Embedding responsibility in intelligent systems: from AI ethics to responsible AI ecosystems.

Intelligent systems that are capable of making autonomous decisions based on input from their environment have great potential to do good, but they also raise significant social and ethical concerns. The discourse on ethics and artificial intelligence (AI) has covered these concerns in depth and developed an array of possible ways of addressing them. This article argues that a shortcoming of this discourse is that it concentrates on specific issues and their mitigation but neglects the nature of intelligent systems as socio-technical systems of systems that are often described as ecosystems. Building on the discussion of ethics and AI, the article suggests that it would be beneficial to come to an understanding of what would constitute responsible AI ecosystems. By introducing the concept of meta-responsibility or higher-level responsibility, the article proposes characteristics that an ecosystem would have to fulfil, in order to be considered a responsible ecosystem. This perspective is theoretically interesting because it extends the current AI ethics discourse. It furthermore offers a novel perspective for researchers and developers of intelligent system and helps them reflect on the way they relate to ethical issues.

Building a responsible innovation toolkit as project legacy.

This article explores whether and in what way it is possible to employ toolkits for responsible research and innovation (RRI toolkits) as mechanisms for ensuring the legacy of RRI in research projects. Based on a review of the concept of responsible research and innovation as well as existing toolkits in the area, the article offers an account of the development of an RRI toolkit in the context of the EU- funded Human Brain Project. This toolkit is designed to integrate insights and practices of responsible research and innovation developed over a 10 year period into the project legacy, the EBRAINS research infrastructure. The article suggests that toolkits have the potential to contribute to ensuring a long- lasting legacy of work undertaken in responsible research and innovation, but that this potential requires further support from institutions and the broader research environment to become realized.

Corrigendum: Towards an understanding of global brain data governance: ethical positions that underpin global brain data governance discourse.

[This corrects the article DOI: 10.3389/fdata.2023.1240660.].

The ethical and legal landscape of brain data governance.

Neuroscience research is producing big brain data which informs both advancements in neuroscience research and drives the development of advanced datasets to provide advanced medical solutions. These brain data are produced under different jurisdictions in different formats and are governed under different regulations. The governance of data has become essential and critical resulting in the development of various governance structures to ensure that the quality, availability, findability, accessibility, usability, and utility of data is maintained. Furthermore, data governance is influenced by various ethical and legal principles. However, it is still not clear what ethical and legal principles should be used as a standard or baseline when managing brain data due to varying practices and evolving concepts. Therefore, this study asks what ethical and legal principles shape the current brain data governance landscape? A systematic scoping review and thematic analysis of articles focused on biomedical, neuro and brain data governance was carried out to identify the ethical and legal principles which shape the current brain data governance landscape. The results revealed that there is currently a large variation of how the principles are presented and discussions around the terms are very multidimensional. Some of the principles are still at their infancy and are barely visible. A range of principles emerged during the thematic analysis providing a potential list of principles which can provide a more comprehensive framework for brain data governance and a conceptual expansion of neuroethics.

Brain simulation as a cloud service: The Virtual Brain on EBRAINS.

The Virtual Brain (TVB) is now available as open-source services on the cloud research platform EBRAINS (ebrains.eu). It offers software for constructing, simulating and analysing brain network models including the TVB simulator; magnetic resonance imaging (MRI) processing pipelines to extract structural and functional brain networks; combined simulation of large-scale brain networks with small-scale spiking networks; automatic conversion of user-specified model equations into fast simulation code; simulation-ready brain models of patients and healthy volunteers; Bayesian parameter optimization in epilepsy patient models; data and software for mouse brain simulation; and extensive educational material. TVB cloud services facilitate reproducible online collaboration and discovery of data assets, models, and software embedded in scalable and secure workflows, a precondition for research on large cohort data sets, better generalizability, and clinical translation.

Ethical and Social Aspects of Neurorobotics.

The interdisciplinary field of neurorobotics looks to neuroscience to overcome the limitations of modern robotics technology, to robotics to advance our understanding of the neural system's inner workings, and to information technology to develop tools that support those complementary endeavours. The development of these technologies is still at an early stage, which makes them an ideal candidate for proactive and anticipatory ethical reflection. This article explains the current state of neurorobotics development within the Human Brain Project, originating from a close collaboration between the scientific and technical experts who drive neurorobotics innovation, and the humanities and social sciences scholars who provide contextualising and reflective capabilities. This article discusses some of the ethical issues which can reasonably be expected. On this basis, the article explores possible gaps identified within this collaborative, ethical reflection that calls for attention to ensure that the development of neurorobotics is ethically sound and socially acceptable and desirable.

Who is Responsible for Responsible Innovation? Lessons From an Investigation into Responsible Innovation in Health Comment on "What Health System Challenges Should Responsible Innovation in Health Address? Insights From an International Scoping Review".

Responsible innovation in health (RIH) takes the ideas of responsible research and innovation (RRI) and applies them to the health sector. This comment takes its point of departure from Lehoux et al which describes a structured literature review to determine the system-level challenges that health systems in countries at different levels of human development face. This approach offers interesting insights from the perspective of RRI, but it also raises the question whether and how RRI can be steered and achieved across healthcare systems. This includes the question who, if anybody, is responsible for responsible innovation and which insights can be drawn from the systemic nature RIH.

Responsible Data Governance of Neuroscience Big Data.

Current discussions of the ethical aspects of big data are shaped by concerns regarding the social consequences of both the widespread adoption of machine learning and the ways in which biases in data can be replicated and perpetuated. We instead focus here on the ethical issues arising from the use of big data in international neuroscience collaborations. Neuroscience innovation relies upon neuroinformatics, large-scale data collection and analysis enabled by novel and emergent technologies. Each step of this work involves aspects of ethics, ranging from concerns for adherence to informed consent or animal protection principles and issues of data re-use at the stage of data collection, to data protection and privacy during data processing and analysis, and issues of attribution and intellectual property at the data-sharing and publication stages. Significant dilemmas and challenges with far-reaching implications are also inherent, including reconciling the ethical imperative for openness and validation with data protection compliance and considering future innovation trajectories or the potential for misuse of research results. Furthermore, these issues are subject to local interpretations within different ethical cultures applying diverse legal systems emphasising different aspects. Neuroscience big data require a concerted approach to research across boundaries, wherein ethical aspects are integrated within a transparent, dialogical data governance process. We address this by developing the concept of "responsible data governance," applying the principles of Responsible Research and Innovation (RRI) to the challenges presented by the governance of neuroscience big data in the Human Brain Project (HBP).

Beyond Research Ethics: Dialogues in Neuro-ICT Research.

The increasing use of information and communication technologies (ICTs) to help facilitate neuroscience adds a new level of complexity to the question of how ethical issues of such research can be identified and addressed. Current research ethics practice, based on ethics reviews by institutional review boards (IRB) and underpinned by ethical principlism, has been widely criticized. In this article, we develop an alternative way of approaching ethics in neuro-ICT research, based on discourse ethics, which implements Responsible Research and Innovation (RRI) through dialogues. We draw on our work in Ethics Support, using the Human Brain Project (HBP) as empirical evidence of the viability of this approach.

The role of ethics in data governance of large neuro-ICT projects.

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.

Improving brain computer interface research through user involvement - The transformative potential of integrating civil society organisations in research projects.

Research on Brain Computer Interfaces (BCI) often aims to provide solutions for vulnerable populations, such as individuals with diseases, conditions or disabilities that keep them from using traditional interfaces. Such research thereby contributes to the public good. This contribution to the public good corresponds to a broader drive of research and funding policy that focuses on promoting beneficial societal impact. One way of achieving this is to engage with the public. In practical terms this can be done by integrating civil society organisations (CSOs) in research. The open question at the heart of this paper is whether and how such CSO integration can transform the research and contribute to the public good. To answer this question the paper describes five detailed qualitative case studies of research projects including CSOs. The paper finds that transformative impact of CSO integration is possible but by no means assured. It provides recommendations on how transformative impact can be promoted.

Critical theory as an approach to the ethics of information security.

Information security can be of high moral value. It can equally be used for immoral purposes and have undesirable consequences. In this paper we suggest that critical theory can facilitate a better understanding of possible ethical issues and can provide support when finding ways of addressing them. The paper argues that critical theory has intrinsic links to ethics and that it is possible to identify concepts frequently used in critical theory to pinpoint ethical concerns. Using the example of UK electronic medical records the paper demonstrates that a critical lens can highlight issues that traditional ethical theories tend to overlook. These are often linked to collective issues such as social and organisational structures, which philosophical ethics with its typical focus on the individual does not tend to emphasise. The paper suggests that this insight can help in developing ways of researching and innovating responsibly in the area of information security.