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Advances in neuroscience and other disciplines are producing large-scale brain data that consists of datasets from multiple organisms, disciplines, jurisdictions in different formats. However, due to the lack of an international data governance framework brain data is currently being produced under various contextual ethical and legal principles which may influence key stakeholders involved in the generation, collection, processing and sharing of brain data thereby raising ethical and legal challenges. Also, although calls for a culturally informed brain data governance framework have been made, practical steps and studies to understand the global perceptions of key stakeholders using neuroscientists who are influenced by these ethical legal principles is currently lacking. Therefore, using the research question how do ethical and legal principles influence data governance in neuroscience? we attempt to understand the perceptions of key actors on the principles, issues and concerns that can arise from brain data research. We carried out interviews with 21 leading international neuroscientists. The analytical insights revealed key ethical and legal principles, areas of convergence, visibility, and the issues and concerns that arise in brain data research around these principles. These issues and concerns mainly circulate around intimately connected areas which include ethics, human rights, regulations, policies and guidelines, and participatory governance. The research answers the call for a cross cultural study of global brain data governance and the results of the study will assist in understanding the issues and concerns that arise in brain data governance.

open access article Ethical and human rights issues of artificial intelligence (AI) are a prominent topic of research and innovation policy as well as societal and scientific debate. It is broadly recognised that AI-related technologies have properties that can give rise to ethical and human rights concerns, such as privacy, bias and discrimination, safety and security, economic distribution, political participation or the changing nature of warfare. Numerous ways of addressing these issues have been suggested. In light of the complexity of this discussion, we undertook a Delphi study with experts in the field to determine the most pressing issues and prioritise appropriate mitigation strategies. The results of the study demonstrate the difficulty of defining clear priorities. Our findings suggest that the debate around ethics and human rights of AI would benefit from being reframed and more strongly emphasising the systems nature of AI ecosystems.

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.

The recent exponential rate of AI development has led to a proliferation of AI national policies and strategies as global power blocs have sought to consolidate positions of strategic dominance. These policies have sought to promote the benefits and mitigate the risks of AI—and address ever more serious ethical concerns about these technologies. This raises the question of how countries less influential in the sphere of AI might seek to address such ethical issues themselves, and whether lessons can be learned from existing policies in addressing issues of ethics and human rights. From a consideration of the current AI ethics discourse and existing AI policies, the chapter goes on to explore how ethical concerns are addressed in the North African AI strategies and which gaps and opportunities in terms of coverage of ethical issues arise from the current state of these policies. We suggest that ethical issues should be addressed clearly in policy at the earliest possible stage to ensure that ethical standards are internally produced in line with social and cultural values, rather than being de facto applied by external actors.

Dignity is a very prominent concept in human rights instruments, in particular constitutions. It is also a concept that has many critics, including those who argue that it is useless in ethical debates. How useful or not dignity can be in artificial intelligence (AI) ethics discussions is the question of this chapter. Is it a conversation stopper, or can it help explain or even resolve some of the ethical dilemmas related to AI? The three cases in this chapter deal with groundless dismissal by an automated system, sex robotsand care robots. The conclusion argues that it makes perfect sense for human rights proponents to treat dignity as a prime value, which takes precedence over others in the case of extreme dignity violations such as torture, human trafficking, slavery and reproductive manipulation. However, in AI ethics debates, it is better seen as an equal among equals, so that the full spectrum of potential benefits and harms are considered for AI technologies using all relevant ethical values.

This chapter introduces the themes covered by the book. It provides an overview of the concept of artificial intelligence (AI) and some of the technologies that have contributed to the current high level of visibility of AI. It explains why using case studies is a suitable approach to engage a broader audience with an interest in AI ethics. The chapter provides a brief overview of the structure and logic of the book by indicating the content of the cases covered in each section. It concludes by identifying the concept of ethics used in this book and how it is located in the broader discussion of ethics, human rightsand regulationof AI.

The concluding chapter highlights broader lessons that can be learned from the artificial intelligence (AI) cases discussed in the book. It underlines the fact that, in many cases, it is not so much the technology itself that is the root cause of ethical concerns but the way it is applied in practice and its reliability. In addition, many of the cases do not differ radically from ethics cases related to other novel technologies, even though the use of AI can exacerbate existing concerns. Ethical issues can rarely be resolved to everybody’s full satisfaction, not least because they often involve the balancing of competing goods. What is essential is space for human reflection and decision-making within the use of AI. Questions about what we can and should do, why we should act in particular ways and how we evaluate the ethical quality of our actions and their outcomes are part of what it means to be human. Even though Immanuel Kant believed that a good will is the only thing in the world that is ethical per se, a good will alone does not suffice where complex consequences may not be obvious. The complex nature of AI systems and their interaction with their human, social and natural environment require constant vigilance and human input.

In 2015, 193 nations came together to agree Agenda 2030: 17 goals ranging from the elimination of poverty to the building of partnerships to achieve those goals. The spirit of the UN Sustainable Development Goals (SDGs) is to leave no one behind. Artificial intelligence (AI) has a great potential to assist in reaching the SDGs. For instance, using algorithms on new and vast agricultural data sets can improve the efficiency of agriculture practices and thereby contribute to SDG 1, “Zero hunger”. However, the high energy consumption, computational resources and levels of expertise required for AI can exacerbate existing inequalities. At the same time, potentially useful AI applications such as seasonal climate forecasting have led to the accelerated laying off of workers in Peru and credit denial to poor farmers in Zimbabweand Brazil. If AI for Good is to be truly realised, AI’s potential to worsen inequality, to overexploit resources, to be undertaken through “helicopter research” and to focus on SDG issues relevant mainly to high-income countries must be overcome, ideally in close collaboration and engagement with potential beneficiaries in resource-limited settings.

Privacy and data protection are concerns raised about most digital technologies. The advance of artificial intelligence (AI) has given even higher levels of prominence to these concerns. Three cases are presented as examples to highlight the way in which AI can affect or exacerbate privacy concerns. The first deals with the use of private data in authoritarian regimes. The second looks at the implications of AI use of genetic data. The third concerns problems linked to biometric surveillance. Then follows a description of how privacy concerns are currently addressed via data protection regulation and a discussion of where AI may raise new challenges to existing data protection regimes. Current European data protection law requires data protection impact assessment. This chapter suggests that a broader AI impact assessment could broaden the remit of such an assessment to offer more comprehensive coverage of possible privacy concerns linked to AI.

The concern that artificial intelligence (AI) can be used to manipulate individuals, with undesirable consequences for the manipulated individual as well as society as a whole, plays a key role in the debate on the ethics of AI. This chapter uses the case of the political manipulation of voters and that of the manipulation of vulnerable consumers as studies to explore how AI can contribute to and facilitate manipulation and how such manipulation can be evaluated from an ethical perspective. The chapter presents some proposed ways of dealing with the ethics of manipulation with reference to data protection, privacyand transparency in the of use of data. Manipulation is thus an ethical issue of AI that is closely related to other issues discussed in this book.

Artificial intelligence(AI) cansupport individuals’ enjoyment of life, libertyand security, but it can also have adverse effects on them in a variety of ways. This chapter covers three cases affecting human life, liberty and security: one in transportation(self-driving cars), one in the home (smart security systems) and one in healthcareservices (adversarial attacks). The chapter discusses ethical questions and three potential solutions to address AIhuman rights issues related to life, liberty and security of persons: defining and strengthening liability regimes, implementing quality management systems and adversarial robustness. AI developers, deployers and users must respect the sanctity of human life and embed, value and respect this principle in the design, development and use of their products and/or services. Critically, AI systems should not be programmed to kill or injure humans.

There is much debate about the ways in which artificial intelligence (AI) systems can include and perpetuate biases and lead to unfair and often illegal discrimination against individuals on the basis of protected characteristics, such as age, race, gender and disability. This chapter describes three cases of such discrimination. It starts with an account of the use of AI in hiring decisions that led to discrimination based on gender. The second case explores the way in which AI can lead to discrimination when applied in law enforcement. The final example looks at implications of bias in the detection of skin colour. The chapter then discusses why these cases are considered to be ethical issues and how this ethics debate relates to well-established legislation around discrimination. The chapter proposes two ways of raising awareness of possible discriminatory characteristics of AI systems and ways of dealing with them: AI impact assessments and ethics by design.

Surveillance capitalism hinges on the appropriationand commercialisationof personal datafor profit-making. This chapter spotlights three cases connected to surveillance capitalism: data appropriation, monetisationof health data and the unfair commercial practice when “free” isn’t “free”. It discusses related ethical concerns of powerinequality, privacy and data protection, and lack of transparencyand explainability. The chapter identifies responses to address concerns about surveillance capitalism and discusses three key responses put forward in policy and academic literature and advocated for their impact and implementation potential in the current socio-economicsystem: antitrustregulation, data sharingand access, and strengthening of data ownership claims of consumers/individuals. A combination of active, working governance measures is required to stem the growth and ill-effects of surveillance capitalism and protect democracy.

Ethical, social and human rights aspects of computing technologies have been discussed since the inception of these technologies. In the 1980s, this led to the development of a discourse often referred to as computer ethics. More recently, since the middle of the 2010s, a highly visible discourse on the ethics of artificial intelligence (AI) has developed. This paper discusses the relationship between these two discourses and compares their scopes, the topics and issues they cover, their theoretical basis and reference disciplines, the solutions and mitigations options they propose and their societal impact. The paper argues that an understanding of the similarities and differences of the discourses can benefit the respective discourses individually. More importantly, by reviewing them, one can draw conclusions about relevant features of the next discourse, the one we can reasonably expect to follow after the ethics of AI. The paper suggests that instead of focusing on a technical artefact such as computers or AI, one should focus on the fact that ethical and related issues arise in the context of socio-technical systems. Drawing on the metaphor of ecosystems which is widely applied to digital technologies, it suggests preparing for a discussion of the ethics of digital ecosystems. Such a discussion can build on and benefit from a more detailed understanding of its predecessors in computer ethics and the ethics of AI.

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).

The file attached to this record is the author's final peer reviewed version. open access article 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.

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. The Publisher's final version can be found by following the DOI link.

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.

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.