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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.

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.

ChatGPT is a high-profile technology that has inspired broad discussions about its capabilities and likely consequences. There has been much debate concerning ethical issues that it raises which are typically described as potentially harmful (or beneficial) consequences of ChatGPT. Concerns relating to issues such as privacy, biases, infringements of intellectual property, or discrimination are widely discussed. The article pursues the question of where these issues originate and where they are located. This article suggests that these ethical issues of the technology are located in the technology’s affordances. Affordances are part of the relationship between user and technology. Going beyond existing research on affordances and ChatGPT, the article suggests that affordances are not confined to the relationship between humans and technology. A proper understanding of affordances needs to consider the role of the socio-technical ecosystem within which these relationships unfold. The article concludes by explaining the implications of this position for research and practice.

This paper examines how the governance in AI policy documents have been framed as way to resolve public controversies surrounding AI. It draws on the studies of governance of emerging technologies, the concept of policy framing, and analysis of 49 recent policy documents dedicated to AI which have been prepared in the context of technological hype expecting fast advances of AI that will fundamentally change economy and society. The hype about AI is accompanied by major public controversy about positive and negative effects of AI. Against the backdrop of this policy controversy, governance emerges as one of the frames that diagnoses the problems and offers prescriptions. Accordingly, the current governance characterized by oligopoly of a small number of large companies is indicated as one of the reasons for problems such as lack of consideration of societal needs and concerns. To address these problems, governance frame in AI policy documents assigns more active and collaborative roles to the state and society. Amid public controversies, the state is assigned the roles of promoting and facilitating AI development while at the same time being a guarantor of risk mitigation and enabler of societal engagement. High expectations are assigned to public engagement with multiple publics as a way to increase diversity, representation and equality in AI development and use. While this governance frame might have a normative appeal, it is not specific about addressing some well-known challenges of the proposed governance mode such as risks of capture by vested interests or difficulties to achieve consensus.

The rapidly growing amount and importance of data across all aspects of organisations and society have led to urgent calls for better, more comprehensive and applicable approaches to data governance. One key driver of this is the use of data in machine learning systems, which hold the promise of producing much social and economic good, but which simultaneously raise significant concerns. Calls for data governance thus typically have an ethical component. This can refer to specific ethical values that data governance is meant to preserve, most obviously in the area of privacy and data protection. More broadly, responsible data governance is seen as a condition of the development and use of ethical and trustworthy digital technologies. This conceptual paper takes the already existing ethical aspect of the data governance discourse as a point of departure and argues that ethics should play a more central role in data governance. Drawing on Habermas’s Theory of Communicative Action and using the example of neuro data, this paper argues that data shapes and is shaped by discourses. Data is at the core of our shared ontological positions and influences what we believe to be real and thus also what it means to be ethical. These insights can be used to develop guidance for the further development of responsible data governance.

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.

Researchers have long referred to privacy, accuracy, property, and accessibility (PAPA) framework in discussing ethical issues in information systems. While all four constituent components remain relevant, technical progress and technological integration in organizations and society in the intervening almost 40 years call for researchers to consider the acronym. In response to Richardson, Petter, and Carter’s (2021) proposal to add the term “society”, I suggest that extending the acronym in more than one dimension would be useful. For example, one could extend the acronym to include stakeholders (e.g., individuals, organizations or society) and system-use stage (e.g., input, processing, and output). The third dimension is the ethical issue, which still includes PAPA but can be supplemented with others, such as bias, power distribution, and others. Therefore, I suggest that we not only need to extend PAPA to PAPAS but that we need to go beyond a list of ethical issues to capture the richness and complexity in which ethics and information systems interact.

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.

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).

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.