Bernd Stahl

This study investigates the ethical use of Big Data and Artificial Intelligence (AI) technologies (BD + AI)—using an empirical approach. The paper categorises the current literature and presents a multi-case study of 'on-the-ground' ethical issues that uses qualitative tools to analyse findings from ten targeted case-studies from a range of domains. The analysis coalesces identified singular ethical issues, (from the literature), into clusters to offer a comparison with the proposed classification in the literature. The results show that despite the variety of different social domains, fields, and applications of AI, there is overlap and correlation between the organisations’ ethical concerns. This more detailed understanding of ethics in AI + BD is required to ensure that the multitude of suggested ways of addressing them can be targeted and succeed in mitigating the pertinent ethical issues that are often discussed in the literature.

The ethics of artificial intelligence is a widely discussed topic. There are numerous initiatives that aim to develop the 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 ten 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.

This study investigates the ethical use of Big Data and Artificial Intelligence (AI) technologies (BD + AI)—using an empirical approach. The paper categorises the current literature and presents a multi-case study of 'on-the-ground' ethical issues that uses qualitative tools to analyse findings from ten targeted case-studies from a range of domains. The analysis coalesces identified singular ethical issues, (from the literature), into clusters to offer a comparison with the proposed classification in the literature. The results show that despite the variety of different social domains, fields, and applications of AI, there is overlap and correlation between the organisations’ ethical concerns. This more detailed understanding of ethics in AI + BD is required to ensure that the multitude of suggested ways of addressing them can be targeted and succeed in mitigating the pertinent ethical issues that are often discussed in the literature.

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.

In the last decade, several organisations, and national and international agencies have developed impact assessments (IAs) to mitigate the risks and impact of AI systems as well as to promote responsible, just and trustworthy design, development and deployment. However, through a critical review of current AI IAs, we identify the failure of these IAs to address fundamental questions regarding who defines problems, whose knowledge is valued, and who truly benefits from AI innovation or generally what we term the ‘coloniality problem’. Developed primarily within Global North normative frameworks, these IAs risk perpetuating the very inequalities they aim to address by neglecting Global South perspectives and the extractive logic underpinning data practices. Thus, we propose a novel approach: Decoloniality Impact Assessment (DIA) as a critical, context-sensitive evaluative approach that assesses AI systems in relation to their inherent colonial legacies, global power asymmetries, and epistemic injustices. It moves beyond traditional ethical frameworks by interrogating how the AI innovation lifecycle and practices reinforce structural inequalities, marginalise local knowledge systems, and perpetuate exploitative systems. The paper advocates for an AI innovation lifecycle approach to DIA, recognising that coloniality manifests at every stage of AI development, from ideation to deployment. DIA is not a new impact assessment framework but an approach that can be integrated into already existing frameworks such as the Council of Europe’s HUDERIA framework. It is a call to reframe AI innovation in a way that technological futures are rooted in justice, pluriversality, and sovereignty.

This article seeks to establish a link between the field of business ethics and information ethics by exploring the connection between responsibility and the Internet from an economic perspective. This link finds its expression in the moral theory of Discourse Ethics as suggested by German philosophers such as Juergen Habermas and Karl‐Otto Apel. The term responsibility points in the direction of communication and therefore leads easily to discourse ethics. The economy in general and most economic practices also seem to be intimately linked to questions of responsibility. The growing spread and importance of the Internet poses new challenges and problems but also creates new possibilities for ethics as well as for the economy. Here several of these new developments are analysed and their impact on ethics as well as economy assessed. Consequences for the use and development of information technology are considered.

PrivacyPrivacy and data protection are concerns raised about most digital technologies. The advance of artificial intelligenceArtificial intelligence (AI) has given even higher levels of prominence to these concerns. Three cases are presented as examples to highlight the way in which AIArtificial intelligence can affect or exacerbate privacyPrivacy 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 regulationRegulation and a discussion of where AI may raise new challenges to existing data protection regimes. Current European data protection law requires data protection impact assessmentImpact assessment. This chapter suggests that a broader AI impact assessmentImpact assessment could broaden the remit of such an assessment to offer more comprehensive coverage of possible privacyPrivacy concerns linked to AIArtificial intelligence.

Surveillance capitalismSurveillance capitalism hinges on the appropriationAppropriationand commercialisationCommercialisationof personal dataPersonal datafor profitProfit-making. This chapter spotlights three cases connected to surveillance capitalismSurveillance capitalism: data appropriationAppropriation, monetisationMonetisationof health dataHealth data and the unfair commercial practice when “free” isn’t “free”. It discusses related ethical concerns of powerPowerinequalityInequality, privacyPrivacy and data protection, and lack of transparencyTransparencyand explainabilityExplainability. The chapter identifies responses to address concerns about surveillance capitalismSurveillance 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-economicSocio-economicsystem: antitrustAntitrustregulationRegulation, data sharingData sharingand accessAccess, and strengthening of data ownershipData ownership claims of consumersConsumer/individualsIndividual. A combination of active, working governance measures is required to stem the growth and ill-effects of surveillance capitalismSurveillance capitalism and protect democracyDemocracy.

The concluding chapter highlights broader lessons that can be learned from the artificial intelligenceArtificial 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 AIArtificial intelligence 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 KantKant, Immanuel 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 AIArtificial intelligence systems and their interaction with their human, social and natural environment require constant vigilance and human input.

The concern that artificial intelligenceArtificial intelligence (AI) can be used to manipulate individualsIndividual, with undesirable consequences for the manipulated individualIndividual 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 manipulationManipulation of voters and that of the manipulationManipulation of vulnerable consumersConsumer as studies to explore how AI can contribute to and facilitate manipulationManipulation and how such manipulationManipulation can be evaluated from an ethical perspective. The chapter presents some proposed ways of dealing with the ethics of manipulationManipulation with reference to data protection, privacyPrivacyand transparencyTransparency in the of use of data. ManipulationManipulation is thus an ethical issue of AIArtificial intelligence that is closely related to other issues discussed in this book.

There is much debate about theUnfair discrimination ways in which artificial intelligenceArtificial intelligence (AI) systems can include and perpetuate biases and lead to unfair and often illegalIllegal discrimination against individualsIndividual on the basis of protected characteristics, such as age, race, gender and disabilityDisabilities. This chapter describes three cases of such discriminationDiscrimination. It starts with an account of the use of AIArtificial intelligence 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 enforcementLaw 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 discriminationDiscrimination. The chapter proposes two ways of raising awareness of possible discriminatory characteristics of AI systems and ways of dealing with them: AI impact assessmentsImpact assessment and ethics by design.

This chapter introduces the themes covered by the book. It provides an overview of the concept of artificial intelligenceArtificial intelligence (AI) and some of the technologies that have contributed to the current high level of visibility of AI. It explains why using case studiesCase study, 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 rightsHuman rightsand regulationRegulationof AIArtificial intelligence.

Dignity is a very prominent concept in human rightsHuman 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 intelligenceArtificial 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 robotsSex robotsand care robotsCare robots. The conclusion argues that it makes perfect sense for human rightsHuman 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, slaverySlavery and reproductive manipulationManipulation. However, in AIArtificial intelligence ethics debates, it is better seen as an equal among equals, so that the full spectrum of potential benefits and harmsHarm are considered for AI technologies using all relevant ethical values.

In 2015, 193 nations cameAI for Good together to agree Agenda 2030Agenda 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 GoalsSustainable Development Goals (SDGs) (SDGs) is to leave no one behind. Artificial intelligenceArtificial intelligence (AI) has a great potential to assist in reaching the SDGs. For instance, using algorithmsAlgorithms 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 AIArtificial intelligence can exacerbate existing inequalitiesInequality. At the same time, potentially useful AI applications such as seasonal climate forecastingSeasonal climate forecasting have led to the accelerated laying off of workers in PeruPeru and credit denial to poor farmers in ZimbabweZimbabweand BrazilBrazil. If AI for GoodAI for Good is to be truly realised, AI’s potential to worsen inequalityInequality, to overexploit resources, to be undertaken through “helicopter researchHelicopter research” and to focus on SDGSustainable Development Goals (SDGs) issues relevant mainly to high-income countries must be overcome, ideally in close collaboration and engagement with potential beneficiaries in resource-limited settingsResource-limited settings.

Artificial intelligenceArtificial intelligence(AI) canRight to life, liberty and security of personsupport individualsIndividual’ enjoyment of lifeLife, libertyLibertyand securitySecurity, but it can also have adverse effects on them in a variety of ways. This chapter covers three cases affecting human lifeLife, liberty and security: one in transportationTransport(self-driving carsSelf-driving cars), one in the home (smart securitySecurity systems) and one in healthcareHealthcareservices (adversarial attacksAdversarial attack). The chapter discusses ethical questions and three potential solutions to address AIArtificial intelligencehuman rightsHuman rights issues related to lifeLife, liberty and securitySecurity of persons: defining and strengthening liabilityLiability regimes, implementing quality managementQuality management systems and adversarial robustnessAdversarial robustness. AI developersDevelopers, deployers and usersUsers must respect the sanctity of human lifeLife and embed, value and respect this principle in the design, development and use of their products and/or services. Critically, AIArtificial intelligence systems should not be programmed to kill or injure humans.

In this article, we explore questions about the culture of trustworthy artificial intelligence (AI) through the lens of ecosystems. We draw on the European Commission’s Guidelines for Trustworthy AI and its philosophical underpinnings. Based on the latter, the trustworthiness of an AI ecosystem can be conceived of as being grounded by both the so-called rational-choice and motivation-attributing accounts—i.e., trusting is rational because solution providers deliver expected services reliably, while trust also involves resigning control by attributing one’s motivation, and hence, goals, onto another entity. Our research question is: What aspects contribute to a responsible AI ecosystem that can promote justifiable trustworthiness in a healthcare environment? We argue that especially within devising governance and support aspects of a medical AI ecosystem, considering the so-called motivation-attributing account of trust provides fruitful pointers. There can and should be specific ways and governance structures supporting and nurturing trustworthiness beyond mere reliability. After compiling a list of preliminary requirements for this, we describe the emergence of one particular medical AI ecosystem and assess its compliance with and future ways of improving its functioning as a responsible AI ecosystem that promotes trustworthiness.

Ethical issues of information and communication technologies (ICTs) are important because they can have significant effects on human liberty, happiness, their ability to lead a good life. They are also of functional interest because they can determine whether technologies are used and whether their positive potential can unfold. For these reasons policy makers are interested in finding out what these issues are and how they can be addressed. The best way of creating ICT policy that is sensitive to ethical issues would be to be proactive and address such issues at early stages of the technology life cycle. The present paper uses this position as a starting point and discusses how knowledge of ethical aspects of emerging ICTs can be gained. It develops a methodology that goes beyond established futures methodologies to cater for the difficult nature of ethical issues. The paper goes on to outline some of the preliminary findings of a European research project that has applied this method.

Ethical issues of information and communication technologies (ICTs) are important because they can have significant effects on human liberty, happiness, and people’s ability to lead a good life. They are also of functional interest because they can determine whether technologies are used and whether their positive potential can unfold. For these reasons, policy makers are interested in finding out what these issues are and how they can be addressed. The best way of creating ICT policy that is sensitive to ethical issues pertain to being proactive in addressing such issues at an early stage of the technology life cycle. The present paper uses this position as a starting point and discusses how knowledge of ethical aspects of emerging ICTs can be gained. It develops a methodology that goes beyond established futures methodologies to cater for the difficult nature of ethical issues. The authors outline how the description of emerging ICTs can be used for an ethical analysis.

Advances in neuroscience and other disciplines are producing large-scale brain data consisting of datasets from multiple organisms, disciplines, and 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. In addition, despite the demand for a brain data governance framework that accounts for culture, there is a gap in empirical research and actions to understand how key stakeholders around the world view these issues using neuroscientists who are affected by these ethical and legal principles. 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 contextual issues and concerns that arise in brain data research around these principles. These issues and concerns circulate around intimately connected areas which include ethics, human rights, regulations, policies and guidelines, and participatory governance. Also, key contextual insights around animal research and ethics were identified. The research identifies key principles, issues, and concerns that need to be addressed in advancing the development of a framework for global brain data governance. By presenting contextual insights from neuroscientists across regions, the study contributes to informing discussions and shaping policies aimed at promoting responsible and ethical practices in brain data research. 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.

Despite its successes, to date Artificial Intelligence (AI) is still characterized by a number of shortcomings with regards to different application domains and goals. These limitations are arguably both conceptual (e.g., related to the underlying theoretical models, such as symbolic vs.connectionist), and operational (e.g., related to robustness and ability to generalize). Biologically inspired AI, and more specifically brain-inspired AI, promises to provide further biological aspects beyond those that are already traditionally included in AI, making it possible to assess and possibly overcome some of its present shortcomings. This article examines some conceptual, technical, and ethical issues raised by the development and use of brain-inspired AI. Against this background, the paper asks whether there is anything ethically unique about brain-inspired AI. The aim of the paper is to introduce a method that has a heuristic nature and that can be applied to identify and address the ethical issues arising from brain-inspired AI (and from AI more generally). The conclusion resulting from the application of this method is that, compared to traditional AI, brain-inspired AI raises new foundational ethical issues and some new practical ethical issues, and exacerbates some of the issues raised by traditional AI.

Empirical research into the ethics of emerging technologies, often involving foresight studies, technology assessment or application of the precautionary principle, raises significant epistemological challenges by failing to explain the relative epistemic status of contentious normative claims about future states. This weakness means that it is unclear why the conclusions reached by these approaches should be considered valid, for example in anticipatory ethical assessment or governance of emerging technologies. This paper explains and responds to this problem by proposing an account of how the epistemic status of uncertain normative claims can be established in ethical and political discourses based on Jürgen Habermas’ discourse ethics. To better understand the nature of the problem, the relationship between norms, facts and the future is explored in light of potential meta-ethical fallacies faced in the field of empirical ethics. Weaknesses of current approaches to anticipatory ethical assessment and governance are then explored, including the Precautionary Principle and Technology Assessment. We argue that the epistemic status of uncertain normative claims can be understood within Habermas’ approach to political discourse, which requires ‘translation’ of uncertain claims to be comprehensible to other stakeholders in discourse. Translation thus provides a way to allow for uncertain normative claims to be considered alongside other types of validity claims in discourse. The paper contributes a conceptual account of the epistemic status of uncertain normative claims in discourse and begins to develop a ‘methodology of translation’ which can be further developed for approaches to research and ethical assessment supporting anticipatory evidence-based policy, governance and system design

Purpose This paper aims to critically assess approaches to sex and gender in the Human Brain Project (HBP) as a large information and communication technology (ICT) project case study using intersectionality. Design/methodology/approach The strategy of the HBP is contextualised within the wider context of the representation of women in ICT, and critically reflected upon from an intersectional standpoint. Findings The policy underpinning the approach deployed by the HBP in response to these issues parallels Horizon 2020 wording and emphasises economic outcomes, productivity and value, which aligns with other “equality” initiatives influenced by neoliberalised versions of feminism. Research limitations/implications Limitations include focussing on a single case study, the authors being funded as part of the Ethics and Society Subproject of the HBP, and the limited temporal period under consideration. Social implications The frameworks underpinning the HBP approach to sex and gender issues present risks with regard to the further entrenchment of present disparities in the ICT sector, may fail to acknowledge systemic inequalities and biases and ignore the importance of intersectionality. Shortcomings of the approach employed by the HBP up to March, 2018 included aspects of each of these risks, and replicated problematic understandings of sex, gender and diversity. Originality/value This paper is the first to use an intersectional approach to issues of sex and gender in the context of large-scale ICT research. Its value lies in raising awareness, opening a discursive space and presenting opportunities to consider and reflect upon potential, contextualised intersectional solutions to such issues.

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.

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 (R&D) 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.