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The file attached to this record is the author's final peer reviewed version. Science-policy organisations are expected to be reflexive of their political influence on research and society. In this long-standing discourse on institutional reflexivity, formal organisations have largely been considered as a whole, and from a structural, or systemic perspective, whereas much less is known about everyday organisational practices; how individual organisational members reflect on and act upon their own as well as their organisation’s limits of knowledge and pre-commitments, if at all. We address this gap through an analysis of qualitative interviews with one national funding institution’s staff overseeing funding for research into information and communication technologies (ICT). We develop a bridging concept between individual and institutional reflexivity, which we call ‘collaborative reflexivity’. Through collaborative reflexive processes, individual employees contribute to the entire organisation’s institutional reflexivity. Our findings help to better understand ‘responsible’ behaviour in funding processes, as part of the growing international movement of Responsible Research and Innovation (RRI).

Purpose The purpose of this paper is clearly illustrate this convergence and the prescriptive recommendations that such documents entail. There is a significant amount of research into the ethical consequences of artificial intelligence (AI). This is reflected by many outputs across academia, policy and the media. Many of these outputs aim to provide guidance to particular stakeholder groups. It has recently been shown that there is a large degree of convergence in terms of the principles upon which these guidance documents are based. Despite this convergence, it is not always clear how these principles are to be translated into practice. Design/methodology/approach In this paper, the authors move beyond the high-level ethical principles that are common across the AI ethics guidance literature and provide a description of the normative content that is covered by these principles. The outcome is a comprehensive compilation of normative requirements arising from existing guidance documents. This is not only required for a deeper theoretical understanding of AI ethics discussions but also for the creation of practical and implementable guidance for developers and users of AI. Findings In this paper, the authors therefore provide a detailed explanation of the normative implications of existing AI ethics guidelines but directed towards developers and organisational users of AI. The authors believe that the paper provides the most comprehensive account of ethical requirements in AI currently available, which is of interest not only to the research and policy communities engaged in the topic but also to the user communities that require guidance when developing or deploying AI systems. Originality/value The authors believe that they have managed to compile the most comprehensive document collecting existing guidance which can guide practical action but will hopefully also support the consolidation of the guidelines landscape. The authors’ findings should also be of academic interest and inspire philosophical research on the consistency and justification of the various normative statements that can be found in the literature.

The COVID-19 pandemic has brought about the first real opportunity to test the efficacy of the Responsible Research and Innovation framework or RRI in a global health crisis. This is in view of the bold new approaches to health research and innovation that the pandemic has paved the way for. One such approach is the digital contact tracing application (CTA). Although contact tracing has been a fundamental part of infectious disease control for decades, this is the first time this technique has been used in mobile applications. Based on a Multivocal Literature Review, the development of CTAs in four countries – France, Germany, Spain, and the UK – was assessed to understand what dimensions of RRI can be identified in the governments’ response to COVID-19. This chapter shows that although from 2011, RRI has been promoted as a governance approach for increasing societal desirability of the processes and products of science and technology, very little is known about how the framework may be applied in a health crisis. Notwithstanding that RRI was not explicitly referenced during the development of CTAs in France, the UK, Spain, and Germany, the analysis has identified some interesting linkage to this framework. It shows that while no RRI approach was explicitly embraced by these governments, some key components were present – even though inadequately. It also indicates that, while it is challenging to apply RRI in crises, there is value in using it as an analytical tool for techno-social responses in situations, like those created by the COVID-19 health crisis.

open access article The concept of innovation ecosystems has become prominent due to its explanatory power. It offers a convincing account of innovation, explaining how and why innovation pathways change and evolve. It has been adopted to explain, predict, and steer innovation. The increasing importance of innovation for most aspects of human life calls for the inclusion of ethical and social rights aspects into the innovation ecosystems discourse. The current innovation ecosystems literature does not provide guidance on how the integration of ethical and social concerns into innovation ecosystems can be realised. One way to achieve this is to draw on the discussion of responsible research and innovation (RRI). This paper applies RRI to the innovation ecosystems discourse and proposes the concept of responsible innovation systems. It draws on the discussion of the ethics of artificial intelligence (AI) to explore how responsible AI innovation ecosystems can be shaped and realised.

The file attached to this record is the author's final peer reviewed version. Many individuals go to online health communities to obtain emotional and information support. We employ critical discourse analysis (CDA) to examine the discourse in five online childhood immunization forums to identify online practices related to engagement and emancipation. We identify four online engagement discourse types in online health communities: cognitive, emotional, behavioral, and political. Consistent with our critical perspective, we identify drivers and obstacles to emancipation. The obstacles to emancipation include the adverse consequences of building online knowledge and collective intelligence, shifts in patient-physician relationships, and “Big” institutional power. Members’ suggestions for empowering community members include encouraging members to conduct their own research on childhood immunization and to differentiate between facts and opinions. These findings suggest that sensemaking is one way to emancipate online health community members, while highlighting the challenges of using online communities to promote emancipation.

The file attached to this record is the author's final peer reviewed version. The ethics of artificial intelligence (AI) is a widely discussed topic. There are numerous initiatives that aim to develop principles and guidance to ensure that the development, deployment and use of AI are ethically acceptable. What is generally unclear is how organisations that make use of AI understand and address these ethical issues in practice. While there is an abundance of conceptual work on AI ethics, empirical insights are rare and often anecdotal. This paper fills the gap in our current understanding of how organisations deal with AI ethics by presenting empirical findings collected using a set of 10 case studies and providing an account of the cross-case analysis. The paper reviews the discussion of ethical issues of AI as well as mitigation strategies that have been proposed in the literature. Using this background, the cross-case analysis categorises the organisational responses that were observed in practice. The discussion shows that organisations are highly aware of the AI ethics debate and keen to engage with ethical issues proactively. However, they make use of only a relatively small subsection of the mitigation strategies proposed in the literature. These insights are of importance to organisations deploying or using AI, to the academic AI ethics debate, but maybe most valuable to policymakers involved in the current debate about suitable policy developments to address the ethical issues raised by AI.

Cross-cutting theoretical frameworks and analyses examine how open innovations in international development can empower poor and marginalized populations. Over the last ten years, “open” innovations—the sharing of information and communications resources without access restrictions or cost—have emerged within international development. But do these innovations empower poor and marginalized populations? This book examines whether, for whom, and under what circumstances the free, networked, public sharing of information and communication resources contribute (or not) toward a process of positive social transformation. The contributors offer cross-cutting theoretical frameworks and empirical analyses that cover a broad range of applications, emphasizing the underlying aspects of open innovations that are shared across contexts and domains. The book first outlines theoretical frameworks that span knowledge stewardship, trust, situated learning, identity, participation, and power decentralization. It then investigates these frameworks across a range of institutional and country contexts, considering each in terms of the key emancipatory principles and structural impediments it seeks to address. Taken together, the chapters offer an empirically tested theoretical direction for the field. Contributors Juan Pablo Alperin, Caitlin M. Bentley, Bidisha Chaudhuri, Nandini Chami, Arul Chib, Purnabha Dasgupta, Andy Dearden, Melissa Densmore, Helani Galpaya, Piyumi Gamage, Anita Gurumurthy, Onkar Hoysala, Linus Kendall, Rich Ling, Goodiel Moshi, Chiranthi Rajapakse, Katherine Reilly, Paul Mungai, Priya Parekh, Chiranthi Rajapakse, Anuradha Rao, Katherine Reilly, David Sadoway, Deo Shao, Parminder Jeet Singh, Matthew L. Smith, Janaki Srinivasan, Bernd Carsten Stahl, Satyarupa Shekhar Swain, John Traxler, Jean-Paul Van Belle, Marion Walton, Yingqin Zheng

This chapter analyses the concept of AI ecosystems with a view to identifying how the ecosystem metaphor can help deal with ethical questions. The first step is to introduce in more detail the concept of ecosystems, drawing specifically on the literature on innovation ecosystems. This allows the identification of characteristics of ecosystems such as their openness, the co-evolution and mutual learning of their members, and the interdependence and complex relationship between those members. These characteristics underlie the challenges that an ethics-driven approach to ecosystems must consider.

The conclusion briefly summarises the main arguments of the book. It focuses on the requirements for mitigation options to be used to address the ethical and human rights concerns of artificial intelligence. It also provides a high-level overview of the main recommendations brought forth in the book. It thereby shows how conceptual and empirical insights into the nature of AI, the ethical issues thus raised and the mitigation strategies currently being discussed can be used to develop practically relevant conclusions. These conclusions and recommendations help to ensure that AI ecosystems are developed and shaped in ways that are conducive to human flourishing.

Research-driven technology developments in the neurosciences present interesting and potentially complicated issues concerning data in general and, more specifically, brain data. The data that is produced from neural recordings is unlike names and addresses in that it may be produced involuntarily, and it can be processed and reprocessed for different aims. Its similarity with names, addresses, etc. is that it can be used to identify persons. The collection, retention, processing, storage and destruction of brain data are of high ethical importance. In terms of policy, as one strand of a broader fabric of measures to cope with this, we can ask: is current data protection regulation adequate in dealing with emerging data concerns that relate to consumer neurotechnology and consent?

open access article Recent advances in Artificial Intelligence (AI) have led to intense debates about benefits and concerns associated with this powerful technology. These concerns and debates have similarities with developments in other emerging technologies characterized by prominent impacts and uncertainties. Against this background, this paper asks, What can AI governance, policy and ethics learn from other emerging technologies to address concerns and ensure that AI develops in a socially beneficial way? From recent literature on governance, policy and ethics of emerging technologies, six lessons are derived focusing on inclusive governance with balanced and transparent involvement of government, civil society and private sector; diverse roles of the state including mitigating risks, enabling public participation and mediating diverse interests; objectives of technology development prioritizing societal benefits; international collaboration supported by science diplomacy, as well as learning from computing ethics and Responsible Innovation.

This chapter reviews the proposals that have been put forward to address ethical issues of AI. It divides them into policy-level proposals, organisational responses and guidance for individuals. It discusses how these mitigation options are reflected in the case studies exemplifying the social reality of AI ethics. The chapter concludes with an overview of the stakeholder groups affected by AI, many of whom play a role in implementing the mitigation strategies and addressing ethical issues in AI.

Any discussion of the ethics of AI needs to be based on a sound understanding of the concept of ethics. This chapter therefore provides a brief overview of some of the key approaches to ethics with a particular emphasis on virtue ethics and the idea of human flourishing. The chapter reviews the purposes for which AI can be used, as these have a bearing on an ethical evaluation. Three main purposes are distinguished: AI forefficiency, optimisation and profit maximisation, AI forsocial control and AI for human flourishing. Given the focus on human flourishing in this book, several theoretical positions are introduced that provide insights into different aspects and ways of promoting human flourishing. The chapter concludes with a discussion of the currently widespread principle-based approach to AI ethics.

open access article The technical and economic benefits of artificial intelligence (AI) are counterbalanced by legal, social and ethical issues. It is challenging to conceptually capture and empirically measure both benefits and downsides. We therefore provide an account of the findings and implications of a multi-dimensional study of AI, comprising 10 case studies, five scenarios, an ethical impact analysis of AI, a human rights analysis of AI and a technical analysis of known and potential threats and vulnerabilities. Based on our findings, we separate AI ethics discourse into three streams: (1) specific issues related to the application of machine learning, (2) social and political questions arising in a digitally enabled society and (3) metaphysical questions about the nature of reality and humanity. Human rights principles and legislation have a key role to play in addressing the ethics of AI. This work helps to steer AI to contribute to human flourishing.

This chapter discusses the ethical issues that are raised by the development, deployment and use of AI. It starts with a review of the (ethical) benefits of AI and then presents the findings of the SHERPA project, which used case studies and a Delphi study to identify what people perceived to be ethical issues. These are discussed using the categorisation of AI technologies introduced earlier. Detailed accounts are given of ethical issues arising from machine learning, from artificial general intelligence and from broader socio-technical systems that incorporate AI.

In the past few decades, there have been significant advances in the sciences concerned with the brain and its functions. As science has advanced, the scientific and practical utility of neurodata – data concerning the nervous system’s nature, structure and function – has also grown. This expansion in utility, however, has brought with it ever increasing ethical and legal scrutiny on the legitimate use of neurodata. There is every reason to believe that the expansion in the scientific and practical utility of neurodata, as well as the increased attention given to attendant ethical and legal concerns, will continue apace. It seems likely that many of the forthcoming legal discussions will concern questions as to whether the collection and use of neurodata should be subject to specific and novel legal, ethical considerations: questions of neuroexceptionalism. In order for such discussions to take place in a structured and logical manner, a framework, in the form of an elaborated set of boundary conditions, for what constitutes a legitimate neuroexceptionalist position, is necessary. Unfortunately, no such framework has hitherto been elaborated. This paper attempts to fill this gap by elaborating such a framework in the form of relevant boundary conditions in relation to: i) the conditions under which neurodata might be considered exceptional; and ii) the conditions under which specific, novel, legal rules are required in response.

The introductory chapter describes the motivation behind this book and provides a brief outline of the main argument. The book offers a novel categorisation of artificial intelligence that lends itself to a classification of ethical and human rights issues raised by AI technologies. It offers an ethical approach based on the concept of human flourishing. Following a review of currently discussed ways of addressing and mitigating ethical issues, the book analyses the metaphor of AI ecosystems. Taking the ecosystems metaphor seriously allows the identification of requirements that mitigation measures need to fulfil. On the basis of these requirements the book offers a set of recommendations that allow AI ecosystems to be shaped in ways that promote human flourishing.

A discussion of the ethics of artificial intelligence hinges on the definition of the term. In this chapter I propose three interrelated but distinct concepts of AI, which raise different types of ethical issues. The first concept of AI is that of machine learning, which is often seen as an example of “narrow” AI. The second concept is that of artificial general intelligence standing for the attempt to replicate human capabilities. Finally, I suggest that the term AI is often used to denote converging socio-technical systems. Each of these three concepts of AI has different properties and characteristics that give rise to different types of ethical concerns.

This chapter develops the conclusions that can be drawn from the application of the ecosystemmetaphor to AI. It highlights the challenges that arise for the ethical governance of AI ecosystems. These provide the basis for the definition of requirements that successful governance interventions have to fulfil. Three main requirements become apparent: the need for a clear delimitation of the boundaries of the ecosystem in question, the provision and maintenance of knowledge and capacities within the ecosystem, and the need for adaptable, flexible and careful governance structures that are capable of reacting to environmental changes. Based on these requirements, the chapter then spells out some recommendations for interventions that are likely to be able to shape AI ecosystems in ways that are conducive to human flourishing.

In this chapter we extend the breadth of the assessment of whether or why Responsible Research and Innovation (RRI) is carried out to include the concepts of how and how much RRI should be undertaken. We propound the concept of RRI intensity level (RIL) as a method of selecting what type and how much RRI activity is feasible, appropriate and effective. We propose an approach to defining the RIL based upon assessments of both the anticipated technology readiness level and the innovation potential of the research to determine the choice and scale of RRI activities undertaken.

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.

Purpose The purpose of this paper is to explore how relationships between different actors are being shaped to allow industry to come to acceptable and desirable uses of research and innovation (R&I) that address societal challenges. Design/methodology/approach Building on existing notions of responsibility proposed in the literature, the paper develops a theoretical account of “networks of responsibility” which capture the interlinked nature of responsibility relationships. The usefulness of the approach is evaluated by exploring two cases of R&I in industry deploying a qualitative research approach that involves interviewing and document analysis. For this, a multinational company from Germany was involved, as well as a small- and medium-sized company from Denmark. Findings The study surfaced 68 responsibility relationships involving a range of different objects, subjects, authorities and norms. By describing overlaps in objects, subjects and other aspects across relationships, the theoretical model proved adequate in untangling and displaying interrelatedness of responsibilities. Furthermore, the analysis surfaced characteristics of responsible research and innovation (RRI) that are already in place in the R&I processes of two innovative companies, such as anticipation, foresight and stakeholder engagement. Not all aspects of responsibility outlined in the theoretical model could be extracted from the interview data for every responsibility relationship, pointing to the need for further research. Practical implications The paper is practically relevant because it supports policy development on an organisational, as well as societal level. Moreover, the networks of responsibility model offer a fine-grained assessment of responsibilities in R&I practice by mapping existing responsibilities which supports translating RRI principles into everyday organisational practices. Social implications RRI sets an ambitious agenda to ensure a more social and ethical R&I. Much work is still needed to bridge the gap between these theoretical and political aspirations and daily R&I practice, especially in non-academic contexts such as industry. By offering a way to understand and untangle the complexity of responsibility relationships, the networks of responsibility model seem to offer a promising approach that can support this endeavour. Originality/value The paper offers a novel theoretical approach to understanding and analysing responsibility allocations in R&I in industry. It demonstrates the reliability of this theoretical position empirically. It is practically important because it supports policy development on an organisational as well as societal level.

The file attached to this record is the author's final peer reviewed version. Scenarios are a methodology of futures and foresight research that has been established for more than half a century. Despite the rich literature on scenarios and ways of constructing them, the current scenario methodologies, current approaches to scenario constructions are often not well aligned with the needs of policymakers. Emerging technologies that are likely to achieve high social relevance in the short to mid-term future and that call for quick policy interventions are difficult to reflect using current scenario approaches. We have therefore developed a new type of scenario that we call a policy scenario. This paper develops the principles and justification for policy scenarios. We provide a detailed description of how they can be constructed, focusing on their key characteristics of policy requirements, plausibility, probability, credibility, expertise, objectivity and legitimacy. Following our stakeholder-based approach allows researchers to construct scenarios that are uniquely suited to inform policymakers and, in effect, the policy development process.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. An increase in the number of companies that publish corporate social responsibility (CSR) statements, and a rise in their ‘sustainability’ research, reflects a growing acceptance that broad ethical considerations are key for any type of company. However, little is known about how companies consider moral objectives for their research and development activities, or the basis upon which these activities are chosen. This research involves qualitative investigation into Responsible Research and Innovation (RRI) in the Information and Communication Technology (ICT) industry, comprising 30 in-depth, pan-European interviews with key personnel in the industry, and focus groups with employees at 14 different companies. Through investigation of the ‘responsible’ activities these companies currently undertake, we shed light on the types of moral goals they set and their underlying ethical standpoints. By reviewing both the responsible innovation and sustainability discourses, and presenting phenomenological evidence, we demonstrate that companies have adopted some aspects of RRI, even though it might not be recognised as such. Our findings indicate that these innovators recognise some of the ethical and societal concerns associated with their activities but their approach is often piecemeal; primary focus is upon the most immediate issues and on legal compliance, to the detriment of broader societal issues and wider challenges. We recommend explicit mechanisms that draw upon established ethical thought and practical academic work to improve companies’ abilities to carry out their sustainability activities, and incorporate them into a responsible business strategy. We conclude with recommendations for innovators, corporate research and development, and policy.