The ethics of artificial intelligence is an upcoming field of research that deals with the ethical assessment of emerging AI applications and addresses the new kinds of moral questions that the advent of AI raises. The argument presented in this article is that, even though there exist different approaches and subfields within the ethics of AI, the field resembles a critical theory. Just like a critical theory, the ethics of AI aims to diagnose as well as change (...) society and is fundamentally concerned with human emancipation and empowerment. This is shown through a power analysis that defines the most commonly addressed ethical principles and topics within the field of AI ethics as either to do with relational power or with dispositional power. Moreover, it is concluded that recognizing AI ethics as a critical theory and borrowing insights from the tradition of critical theory can help the field forward. (shrink)

Volume 23, Issue 3-4, November - December 2024, Page 251-263.

This paper explores the development of an ethical guardrail framework for AI systems, emphasizing the importance of customizable guardrails that align with diverse user values and underlying ethics. We address the challenges of AI ethics by proposing a structure that integrates rules, policies, and AI assistants to ensure responsible AI behavior, while comparing the proposed framework to the existing state-of-the-art guardrails. By focusing on practical mechanisms for implementing ethical standards, we aim to enhance transparency, user autonomy, and continuous (...) improvement in AI systems. Our approach accommodates ethical pluralism, offering a flexible and adaptable solution for the evolving landscape of AI governance. The paper concludes with strategies for resolving conflicts between ethical directives, underscoring the present and future need for robust, nuanced and context-aware AI systems. (shrink)

Over the last few years, various public, private, and NGO entities have adopted a staggering number of non-binding ethical codes to guide the development of artificial intelligence. However, this seemingly failed to drive better ethical practices within AI organizations. In light of this observation, this paper aims to reevaluate the roles the ethics of AI can play to have a meaningful impact on the development and implementation of AI systems. In doing so, we challenge the notion that AI (...) class='Hi'>ethics should focus primarily on instilling ethical principles in practitioners within AI organizations, as well as the claim that AI ethics can only lead to ethics washing. We propose a two-pronged institutionalist approach to AI ethics, focusing on shaping organizational decision-making processes and emphasizing the necessity of binding legal regulations. First, we argue that AI ethics should give priority to institutional design over the internalization of ethical principles by individual practitioners. We then contend that legally binding rules are needed to this end, both as a motivation for organizations and to contribute to the semantic determination of high-level ethical principles. We then show that promising proposals to operationalize ethical principles require the backing of binding legal norms to be effective. We conclude by highlighting the potential of AI ethics to contribute meaningfully to legislative innovation in AI governance. (shrink)

The ethics of artificial intelligence, or AI ethics, is a rapidly growing field, and rightly so. While the range of issues and groups of stakeholders concerned by the field of AI ethics is expanding, with speculation about whether it extends even to the machines themselves, there is a group of sentient beings who are also affected by AI, but are rarely mentioned within the field of AI ethics—the nonhuman animals. This paper seeks to explore the kinds (...) of impact AI has on nonhuman animals, the severity of these impacts, and their moral implications. We hope that this paper will facilitate the development of a new field of philosophical and technical research regarding the impacts of AI on animals, namely, the ethics of AI as it affects nonhuman animals. (shrink)

One objection to conventional AI ethics is that it slows innovation. This presentation responds by reconfiguring ethics as an innovation accelerator. The critical elements develop from a contrast between Stability AI’s Diffusion and OpenAI’s Dall-E. By analyzing the divergent values underlying their opposed strategies for development and deployment, five conceptions are identified as common to acceleration ethics. Uncertainty is understood as positive and encouraging, rather than discouraging. Innovation is conceived as intrinsically valuable, instead of worthwhile only as (...) mediated by social effects. AI problems are solved by more AI, not less. Permissions and restrictions governing AI emerge from a decentralized process, instead of a unified authority. The work of ethics is embedded in AI development and application, instead of functioning from outside. Together, these attitudes and practices remake ethics as provoking rather than restraining artificial intelligence. (shrink)

By mid-2019 there were more than 80 AI ethics guides available in the public domain. Despite this, 2020 saw numerous news stories break related to ethically questionable uses of AI. In part, this is because AI ethics theory remains highly abstract, and of limited practical applicability to those actually responsible for designing algorithms and AI systems. Our previous research sought to start closing this gap between the ‘what’ and the ‘how’ of AI ethics through the creation of (...) a searchable typology of tools and methods designed to translate between the five most common AI ethics principles and implementable design practices. Whilst a useful starting point, that research rested on the assumption that _all_ AI practitioners are aware of the ethical implications of AI, understand their importance, and are actively seeking to respond to them. In reality, it is unclear whether this is the case. It is this limitation that we seek to overcome here by conducting a mixed-methods qualitative analysis to answer the following four questions: what do AI practitioners understand about the need to translate ethical principles into practice? What motivates AI practitioners to embed ethical principles into design practices? What barriers do AI practitioners face when attempting to translate ethical principles into practice? And finally, what assistance do AI practitioners want and need when translating ethical principles into practice? (shrink)

This article provides a methodology for the interpretation of AI ethics principles to specify ethical criteria for the development and deployment of AI systems in high-risk domains. The methodology consists of a three-step process deployed by an independent, multi-stakeholder ethics board to: (1) identify the appropriate level of abstraction for modelling the AI lifecycle; (2) interpret prescribed principles to extract specific requirements to be met at each step of the AI lifecycle; and (3) define the criteria to inform (...) purpose- and context-specific balancing of the principles. The methodology presented in this article is designed to be agile, adaptable, and replicable, and when used as part of a pro-ethical institutional culture, will help to foster the ethical design, development, and deployment of AI systems. The application of the methodology is illustrated through reference to the UK Ministry of Defence AI ethics principles. (shrink)

Ethics has powerful teeth, but these are barely being used in the ethics of AI today – it is no wonder the ethics of AI is then blamed for having no teeth. This article argues that ‘ethics’ in the current AI ethics field is largely ineffective, trapped in an ‘ethical principles’ approach and as such particularly prone to manipulation, especially by industry actors. Using ethics as a substitute for law risks its abuse and misuse. (...) This significantly limits what ethics can achieve and is a great loss to the AI field and its impacts on individuals and society. This article discusses these risks and then highlights the teeth of ethics and the essential value they can – and should – bring to AI ethics now. (shrink)

AI ethics is proposed, by the Big Tech companies which lead AI research and development, as the cure for diverse social problems posed by the commercialization of data-intensive technologies. It aims to reconcile capitalist AI production with ethics. However, AI ethics is itself now the subject of wide criticism; most notably, it is accused of being no more than “ethics washing” a cynical means of dissimulation for Big Tech, while it continues its business operations unchanged. This (...) paper aims to critically assess, and go beyond the ethics washing thesis. I argue that AI ethics is indeed ethics washing, but not only that. It has a more significant economic function for Big Tech. To make this argument I draw on the theory of intellectual monopoly capital. I argue that ethics washing is better understood as a subordinated innovation network: a dispersed network of contributors beyond Big Tech’s formal employment whose research is indirectly planned by Big Tech, which also appropriates its results. These results are not intended to render AI more ethical, but rather to advance the business processes of data-intensive capital. Because the parameters of AI ethics are indirectly set in advance by Big tech, the ostensible goal that AI ethics sets for itself—to resolve the contradiction between business and ethics—is in fact insoluble. I demonstrate this via an analysis of the latest trend in AI ethics: the operationalization of ethical principles. (shrink)

Many popular artificial intelligence (AI) ethics frameworks center the principle of autonomy as necessary in order to mitigate the harms that might result from the use of AI within society. These harms often disproportionately affect the most marginalized within society. In this paper, we argue that the principle of autonomy, as currently formalized in AI ethics, is itself flawed, as it expresses only a mainstream mainly liberal notion of autonomy as rational self-determination, derived from Western traditional philosophy. In (...) particular, we claim that the adherence to such principle, as currently formalized, does not only fail to address many ways in which people’s autonomy can be violated, but also to grasp a broader range of AI-empowered harms profoundly tied to the legacy of colonization, and which particularly affect the already marginalized and most vulnerable on a global scale. To counter such a phenomenon, we advocate for the need of a relational turn in AI ethics, starting from a relational rethinking of the AI ethics principle of autonomy that we propose by drawing on theories on relational autonomy developed both in moral philosophy and Ubuntu ethics. (shrink)

The search for ethical guidance in the development of artificial intelligence (AI) systems, especially in healthcare and decision support, remains a crucial effort. So far, principles usually serve as the main reference points to achieve ethically correct implementations. Based on reviewing classical criticism of principle-based ethics and taking into account the severity and potentially life-changing relevance of decisions assisted by AI-driven systems, we argue for strengthening a complementary perspective that focuses on the life-world as ensembles of practices which shape (...) people’s lives. This perspective focuses on the notion of ethical judgment sensitive to life forms, arguing that principles alone do not guarantee ethicality in a moral world that is rather a joint construction of reality than a matter of mere control. We conclude that it is essential to support and supplement the implementation of moral principles in the development of AI systems for decision-making in healthcare by recognizing the normative relevance of life forms and practices in ethical judgment. (shrink)

In this chapter we take stock of some of the complexities of the sprawling field of AI ethics. We consider questions like "what is the proper scope of AI ethics?" And "who counts as an AI ethicist?" At the same time, we flag several potential uses and abuses of AI ethics. These include challenges for the AI ethicist, including what qualifications they should have; the proper place and extent of futuring and speculation in the field; and the (...) dilemmas concerning how we use our public and academic resources. (shrink)

Despite the increase in the research field of ethics in artificial intelligence, most efforts have focused on the debate about principles and guidelines for responsible AI, but not enough attention has been given to the “how” of applied ethics. This paper aims to advance the research exploring the gap between practice and principles in AI ethics by identifying how companies are applying those guidelines and principles in practice. Through a qualitative methodology based on 22 semi-structured interviews and (...) two focus groups, the goal of the current study is to understand how companies approach ethical issues related to AI systems. A structured analysis of the transcripts brought out many actual practices and findings, which are presented around the following main research topics: ethics and principles, privacy, explainability, and fairness. The interviewees also raised issues of accountability and governance. Finally, some recommendations are suggested such as developing specific sector regulations, fostering a data-driven organisational culture, considering the algorithm’s complete life cycle, developing and using a specific code of ethics, and providing specific training on ethical issues. Despite some obvious limitations, such as the type and number of companies interviewed, this work identifies real examples and direct priorities to advance the research exploring the gap between practice and principles in AI ethics, with a specific focus on Spanish companies. (shrink)

This paper puts forth Central Asian AI ethics principles and proposes a layered strategy tailored for the development of ethical principles in the field of artificial intelligence (AI) in Central Asian countries. This approach includes the customization of AI ethics principles to resonate with local nuances, the formulation of national and regional-level AI ethics principles, and the implementation of sector-specific principles. While countering the narrative of ineffectiveness of the AI ethics principles, this paper underscores the importance (...) of stakeholder collaboration, provides a comprehensive framework, and emphasizes the need for responsible AI practices. By adopting this approach, Central Asian region can contribute towards the regional integration and global discourse on AI ethics while promoting the responsible use of AI technology in their respective countries. (shrink)

There is now a robust literature on the ethics of artificial intelligence (AI) that pertains largely to non-military issues – issues of, among other things, bias, fairness, and unintended consequences. There is less published work, however, on how these lessons from industry and academia might inform the ethics of AI in the military context. In this article, I take small steps to demonstrate the ways in which the field of AI ethics might be relevant to military applications. (...) Ultimately, I argue that even though the outcomes of AI failures might look very different in the non-military context versus in the military context, the causes of those failures can be very similar; and addressing those potential causes of AI failures in the development of military AI systems will result in fewer ethical failures after deployment. (shrink)

Artificial intelligence (AI) ethics is proposed as an emerging and interdisciplinary field concerned with addressing the ethical issues of AI, such as the issue of moral decision-making. The conflict between our intuitive moral judgments constitutes an inevitable obstacle to decision-making in AI ethics. This article outlines the Moral Relevance Approach, which could provide a considerable moral foundation for AI ethics. Taking moral relevance as the precondition of the consequentialist principles, the Moral Relevance Approach aims to plausibly consider (...) individual moral claims. It is not only the common ethical target shaping our moral consensus but also the inherent moral ability connecting others with us. (shrink)

The popularisation of Artificial Intelligence (AI) technologies has sparked discussion about their ethical implications. This development has forced governmental organisations, NGOs, and private companies to react and draft ethics guidelines for future development of ethical AI systems. Whereas many ethics guidelines address values familiar to ethicists, they seem to lack in ethical justifications. Furthermore, most tend to neglect the impact of AI on democracy, governance, and public deliberation. Existing research suggest, however, that AI can threaten key elements of (...) western democracies that are ethically relevant. In this paper, Rawls’s theory of justice is applied to draft a set of guidelines for organisations and policy-makers to guide AI development towards a more ethical direction. The goal is to contribute to the broadening of the discussion on AI ethics by exploring the possibility of constructing AI ethics guidelines that are philosophically justified and take a broader perspective of societal justice. The paper discusses how Rawls’s theory of justice as fairness and its key concepts relate to the ongoing developments in AI ethics and gives a proposition of how principles that offer a foundation for operationalising AI ethics in practice could look like if aligned with Rawls’s theory of justice as fairness. (shrink)

There is now a robust literature on the ethics of artificial intelligence (AI) that pertains largely to non-military issues – issues of, among other things, bias, fairness, and unintended consequences. There is less published work, however, on how these lessons from industry and academia might inform the ethics of AI in the military context. In this article, I take small steps to demonstrate the ways in which the field of AI ethics might be relevant to military applications. (...) Ultimately, I argue that even though the outcomes of AI failures might look very different in the non-military context versus in the military context, the causes of those failures can be very similar; and addressing those potential causes of AI failures in the development of military AI systems will result in fewer ethical failures after deployment. (shrink)

Artificial intelligence (AI) is booming, and AI ethics is booming with it. Yet there is surprisingly little attention paid to what the discipline of AI ethics is and what it ought to be. This paper offers an ameliorative definition of AI ethics to fill this gap. We introduce and defend an original distinction between novel and applied research questions. A research question should count as AI ethics if and only if (i) it is novel or (ii) (...) it is applied and has gained new importance through the development of AI. We argue that a lack of such a definition contributes to six disciplinary problems: ethics washing and lobbying, limited applicability, dilution of the field, conceptual bloating, costs of AI ethics, and an internal dispute. Based on our definition, we construct a methodological framework for AI ethics and show how it helps address these problems. (shrink)

With the increased use of Artificial Intelligence (AI) in wildlife conservation, issues around whether AI-based monitoring tools in wildlife conservation comply with standards regarding AI Ethics are on the rise. This review aims to summarise current debates and identify gaps as well as suggest future research by investigating (1) current AI Ethics and AI Ethics issues in wildlife conservation, (2) Initiatives Stakeholders in AI for wildlife conservation should consider integrating AI Ethics in wildlife conservation. We find (...) that the existing literature weakly focuses on AI Ethics and AI Ethics in wildlife conservation while at the same time ignores AI Ethics integration in AI systems for wildlife conservation. This paper formulates an ethically aligned AI system framework and discusses pre-eminent on-demand AI systems in wildlife conservation. The proposed framework uses agile software life cycle methodology to implement guidelines towards the ethical upgrade of any existing AI system or the development of any new ethically aligned AI system. The guidelines enforce, among others, the minimisation of intentional harm and bias, diversity in data collection, design compliance, auditing of all activities in the framework and ease of code inspection. This framework will inform AI developers, users, conservationists, and policymakers on what to consider when integrating AI Ethics into AI-based systems for wildlife conservation. (shrink)

AI technology advancement has sparked a global initiative to educate youth on AI ethics. Understanding students’ prior knowledge is vital. This study explores the diverse perceptions of AI ethics among Korean middle school students using Q methodology. Four types emerged: Privacy Guardians, AI Coexistence Pursuers, AI Ethics Conservatives, and Domestic Distributive Justice Advocates. These classifications reflect the students’ concerns, attitudes toward AI, and value preferences. Despite differences, there is consensus on the importance of human dignity and disagreement (...) with the fair distribution of economic benefits of AI between countries. The findings suggest the need for tailored educational strategies based on students’ perceptions to foster balanced ethical reflection. (shrink)

Much of the current work on AI ethics has lost its connection to the real-world impact by making AI ethics operable. There exist significant limitations of hyper-focusing on the identification of abstract ethical principles, lacking effective collaboration among stakeholders, and lacking the communication of ethical principles to real-world applications. This position paper presents challenges in making AI ethics operable and highlights key obstacles to AI ethics impact. A preliminary practice example is provided to initiate practical implementations (...) of AI ethics. We aim to inspire discussions on making AI ethics operable and focus on its impact on real-world applications. (shrink)

Current advances in research, development and application of artificial intelligence systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. Designed as a semi-systematic evaluation, this paper analyzes and compares 22 guidelines, highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI (...) ethics. Finally, I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems—and how the effectiveness in the demands of AI ethics can be improved. (shrink)

One of the most common contemporary approaches for developing an ethics of artificial intelligence (AI) involves elaborating guiding principles. This essay explores the limitations of this approach, using the history of bioethics as a comparative case. The examples of bioethics and recent AI ethics suggest that principles are difficult to implement in everyday practice, fail to direct individual action, and can frequently result in a pure proceduralism. The essay encourages an additional attention to virtue, which forms the dispositions (...) of actors, directs them to valued ends, and encourages right action in particular situation to complement principles. Practices and professions like medicine or warfighting that already have rich traditions of reflection on virtue ethics will be especially fruitful fields for developing an AI ethics of virtue. (shrink)

Artificial intelligence ethics requires a united approach from policymakers, AI companies, and individuals, in the development, deployment, and use of these technologies. However, sometimes discussions can become fragmented because of the different levels of governance or because of different values, stakeholders, and actors involved. Recently, these conflicts became very visible, with such examples as the dismissal of AI ethics researcher Dr. Timnit Gebru from Google and the resignation of whistle-blower Frances Haugen from Facebook. Underpinning each debacle was a (...) conflict between the organisation’s economic and business interests and the morals of their employees. This paper will examine tensions between the ethics of AI organisations and the values of their employees, by providing an exploration of the AI ethics literature in this area, and a qualitative analysis of three workshops with AI developers and practitioners. Common ethical and social tensions will be discussed, along with proposals on how to avoid or reduce these conflicts in practice. Altogether, we suggest the following steps to help reduce ethical issues within AI organisations: improved and diverse ethics education and training within businesses; internal and external ethics auditing; the establishment of AI ethics ombudsmen, AI ethics review committees and an AI ethics watchdog; as well as access to trustworthy AI ethics whistle-blower organisations. (shrink)

A morally acceptable course of AI development should avoid two dangers: creating unaligned AI systems that pose a threat to humanity and mistreating AI systems that merit moral consideration in their own right. This paper argues these two dangers interact and that if we create AI systems that merit moral consideration, simultaneously avoiding both of these dangers would be extremely challenging. While our argument is straightforward and supported by a wide range of pretheoretical moral judgments, it has far-reaching moral implications (...) for AI development. Although the most obvious way to avoid the tension between alignment and ethical treatment would be to avoid creating AI systems that merit moral consideration, this option may be unrealistic and is perhaps fleeting. So, we conclude by offering some suggestions for other ways of mitigating mistreatment risks associated with alignment. (shrink)

This paper assesses leading Japanese philosophical thought since the onset of Japan’s modernity: namely, from the Meiji Restoration onwards. It argues that there are lessons of global value for AI ethics to be found from examining leading Japanese philosophers of modernity and ethics, each of whom engaged closely with Western philosophical traditions. Turning to these philosophers allows us to advance from what are broadly individualistically and Western-oriented ethical debates regarding emergent technologies that function in relation to AI, by (...) introducing notions of community, wholeness, sincerity, and heart. With reference to AI that pertains to profile, judge, learn, and interact with human emotion, this paper contends that Japan itself may internally make better use of historic indigenous ethical thought, especially as it applies to question of data and relationships with technology; but also that externally Western and global ethical discussion regarding emerging technologies will find valuable insights from Japan. The paper concludes by distilling from Japanese philosophers of modernity four ethical suggestions, or spices, in relation to emerging technological contexts for Japan’s national AI policies and international fora, such as standards development and global AI ethics policymaking. (shrink)

The recent proliferation of AI scandals led private and public organisations to implement new ethics guidelines, introduce AI ethics boards, and list ethical principles. Nevertheless, some of these efforts remained a façade not backed by any substantive action. Such behaviour made the public question the legitimacy of the AI industry and prompted scholars to accuse the sector of ethicswashing, machinewashing, and ethics trivialisation—criticisms that spilt over to institutional AI ethics boards. To counter this widespread issue, contributions (...) in the literature have proposed fixes that do not consider its systemic character and are based on a top-down, expert-centric governance. To fill this gap, we propose to make use of qualified informed lotteries: a two-step model that transposes the documented benefits of the ancient practice of sortition into the selection of AI ethics boards’ members and combines them with the advantages of a stakeholder-driven, participative, and deliberative bottom-up process typical of Citizens’ Assemblies. The model permits increasing the public’s legitimacy and participation in the decision-making process and its deliverables, curbing the industry’s over-influence and lobbying, and diminishing the instrumentalisation of ethics boards. We suggest that this sortition-based approach may provide a sound base for both public and private organisations in smart societies for constructing a decentralised, bottom-up, participative digital democracy. (shrink)

This open access book tackles the pressing problem of integrating concerns related to Artificial Intelligence (AI) ethics into higher education curriculums aimed at future AI developers in Africa and beyond. For doing so, it analyzes the present and future states of AI ethics education in local computer science and engineering programs. The authors share relevant best practices and use cases for teaching, develop answers to ongoing organizational challenges, and reflect on the practical implications of different theoretical approaches to (...) AI ethics. The book is of great interest to faculty members, researchers, and students in the fields of artificial intelligence, computer science, mathematics, computer engineering, and related areas, as well as higher education administration. (shrink)

We paraphrase Descartes’ famous dictum in the area of AI ethics where the “I doubt and therefore I am” is suggested as a necessary aspect of morality. Therefore AI, which cannot doubt itself, cannot possess moral agency. Of course, this is not the end of the story. We explore various aspects of the human mind that substantially differ from AI, which includes the sensory grounding of our knowing, the act of understanding, and the significance of being able to doubt (...) ourselves. The foundation of our argument is the discipline of ethics, one of the oldest and largest knowledge projects of human history, yet, we seem only to be beginning to get a grasp of it. After a couple of thousand years of studying the ethics of humans, we (humans) arrived at a point where moral psychology suggests that our moral decisions are intuitive, and all the models from ethics become relevant only when we explain ourselves. This recognition has a major impact on what and how we can do regarding AI ethics. We do not offer a solution, we explore some ideas and leave the problem open, but we hope somewhat better understood than before our study. (shrink)

This paper defends two main claims. First, that political philosophy deserves a central place in AI Ethics’ curricula. This is a claim about the content of the AI Ethics class. The second claim is about the form of the AI Ethics class: namely, that considerations originating in political philosophy must inform the way in which AI Ethics is taught. The basic idea animating both claims, is that AI has powerful political implications and that preparing students to (...) navigate these implications requires paying close attention to both the cognitive and practical learning goals of the AI Ethics course. (shrink)

Technological outsourcing is increasingly prevalent, with AI systems taking over many tasks once performed by humans. This shift has led to various discussions within AI ethics. A question that was largely ignored until recently, but is now increasingly being discussed, concerns the meaningfulness of such a lifestyle. The literature largely features skeptical views, raising several challenges. Many of these challenges can be grouped under what I identify as the “meaningfulness gap”. Although this gap is widely acknowledged, there is a (...) notable absence of systematic exploration in the literature. This paper aims to fill this void by offering a detailed, step-by-step guide for systematically exploring the different instances of the meaningfulness gap and aids in navigating their complexities. More specifically, it proposes differentiating the gaps according to their realms and objects, normative nature, scope, and severity. To make these areas manageable, the paper takes several taxonomies and distinctions on board. Finally, the guide is summarized, and some skeptical replies are anticipated and countered by clarificatory remarks. (shrink)

Digital Earth applications provide a common ground for visualizing, simulating, and modeling real-world situations. The potential of Digital Earth applications has increased significantly with the evolution of artificial intelligence systems and the capacity to collect and process complex amounts of geospatial data. Yet, the widespread techno-optimism at the root of Digital Earth must now confront concerns over high-risk artificial intelligence systems and power asymmetries of a datafied society. In this commentary, we claim that not only can current debates about data (...) governance and ethical artificial intelligence inform development in the field of Digital Earth, but that the specificities of geospatial data, together with the expectations surrounding Digital Earth applications, offer a fruitful lens through which to examine current debates on data governance and artificial intelligence ethics. In particular, we argue that for the implementation of ethical artificial intelligence and inclusive approaches to data governance, Digital Earth initiatives need to involve stakeholders and communities at the local level and be sensitive to social, legal, cultural, and institutional contexts, including conflicts that might arise within those contexts. (shrink)

The scientific relationship between neuroscience and artificial intelligence is generally acknowledged, and the role that their long history of collaboration has played in advancing both fields is often emphasized. Beyond the important scientific insights provided by their collaborative development, both neuroscience and AI raise a number of ethical issues that are generally explored by neuroethics and AI ethics. Neuroethics and AI ethics have been gaining prominence in the last few decades, and they are typically carried out by different (...) research communities. However, considering the evolving landscape of AI-assisted neurotechnologies and the various conceptual and practical intersections between AI and neuroscience—such as the increasing application of AI in neuroscientific research, the healthcare of neurological and mental diseases, and the use of neuroscientific knowledge as inspiration for AI—some scholars are now calling for a collaborative relationship between these two domains. This article seeks to explore how a collaborative relationship between neuroethics and AI ethics can stimulate theoretical and, ideally, governance efforts. First, we offer some reasons for calling for the collaboration of the ethical reflection on neuroscientific innovations and AI. Next, we explore some dimensions that we think could be enhanced by the cross-fertilization between these two subfields of ethics. We believe that considering the pace and increasing fusion of neuroscience and AI in the development of innovations, broad and underspecified calls for responsibility that do not consider insights from different ethics subfields will only be partially successful in promoting meaningful changes in both research and applications. (shrink)

Several competing narratives drive the contemporary AI ethics discourse. At the two extremes are sociotechnical dogmatism, which holds that society is full of inefficiencies and imperfections that can only be solved by better technology; and sociotechnical skepticism, which highlights the unacceptable risks AI systems pose. While both narratives have their merits, they are ultimately reductive and limiting. As a constructive synthesis, we introduce and defend sociotechnical pragmatism—a narrative that emphasizes the central role of context and human agency in designing (...) and evaluating emerging technologies. In doing so, we offer two novel contributions. First, we demonstrate how ethical and epistemological considerations are intertwined in the AI ethics discourse by tracing the dialectical interplay between dogmatic and skeptical narratives across disciplines. Second, we show through examples how sociotechnical pragmatism does more to promote fair and transparent AI than dogmatic or skeptical alternatives. By spelling out the assumptions that underpin sociotechnical pragmatism, we articulate a robust stance for policymakers and scholars who seek to enable societies to reap the benefits of AI while managing the associated risks through feasible, effective, and proportionate governance. (shrink)

Healthcare applications of AI have the potential to produce great benefit, but also come with significant ethical risks. This has brought ethics to the forefront of academic, policy and public debates about AI in healthcare. To help navigate these debates, we distinguish three general modes of ethical theorizing in contemporary secular AI ethics: (1) idealism, which seeks to articulate moral ideals that can be applied to concrete problems; (2) realism, which focuses on understanding complex social realities that underpin (...) ethical problems; and (3) pragmatism, which promotes deliberate methods that allow joint action in the present but also enable to future moral growth and progress. We discuss the strengths and weaknesses of each of these modes and illustrate their relevance to salient ethical problems arising from AI in healthcare. Finally, we argue that, while all three have merits, pragmatist approaches deserve more attention they have received thus far. (shrink)

In this paper, I draw on Hannah Arendt’s notion of ‘banality of evil’ to argue that as long as AI systems are designed to follow codes of ethics or particular normative ethical theories chosen by us and programmed in them, they are Eichmanns destined to commit evil. Since intelligence alone is not sufficient for ethical decision making, rather than strive to program AI to determine the right ethical decision based on some ethical theory or criteria, AI should be concerned (...) with avoiding making the wrong decisions, and this requires hardwiring the thinking activity as a prerequisite for decision making. (shrink)

For implementing ethics in AI technology, there are at least two major ethical challenges. First, there are various competing AI ethics guidelines and consequently there is a need for a systematic overview of the relevant values that should be considered. Second, if the relevant values have been identified, there is a need for an indicator system that helps assessing if certain design features are positively or negatively affecting their implementation. This indicator system will vary with regard to specific (...) forms of AI technology. An adequate indicator system for the ethical development of recommendation algorithms, for example, will diverge considerably from another for autonomous road vehicles, although both are based on shared values. In this contribution, we propose solutions to both challenges with regard to the special case of the development of an AI-assisted rescue robot. (shrink)