The advent of intelligent artificial systems has sparked a dispute about the question of who is responsible when such a system causes a harmful outcome. This paper champions the idea that this dispute should be approached as a conceptual engineering problem. Towards this claim, the paper first argues that the dispute about the responsibility gap problem is in part a conceptual dispute about the content of responsibility and related concepts. The paper then argues that the way forward is to (...) evaluate the conceptual choices we have, in the light of a systematic understanding of why the concept is important in the first place—in short, the way forward is to engage in conceptual engineering. The paper then illustrates what approaching the responsibility gap problem as a conceptual engineering problem looks like. It outlines argumentative pathways out of the responsibility gap problem and relates these to existing contributions to the dispute. (shrink)

In 2019, the IEEE launched the P7000 standards projects intended to address ethical issues in the design of autonomous and intelligent systems. This move came amidst a growing public concern over the unintended consequences of artificial intelligence (AI), compounded by the lack of an anticipatory process for attending to ethical impact within professional practice. However, the difficulty in moving from principles to practice presents a significant challenge to the implementation of ethical guidelines. Herein, we describe two complementary frameworks for integrating (...) ethical analysis into engineering practice to help address this challenge. We then provide the outcomes of an ethical analysis informed by these frameworks, conducted within the specific context of internetdelivered therapy in digital mental health. We hope both the frameworks and analysis can provide tools and insights, not only for the context of digital healthcare, but for data-enabled and intelligent technology development more broadly. (shrink)

The term ‘responsible AI’ has been coined to denote AI that is fair and non-biased, transparent and explainable, secure and safe, privacy-proof, accountable, and to the benefit of mankind. Since 2016, a great many organizations have pledged allegiance to such principles. Amongst them are 24 AI companies that did so by posting a commitment of the kind on their website and/or by joining the ‘Partnership on AI’. By means of a comprehensive web search, two questions are addressed by this (...) study: Did the signatory companies actually try to implement these principles in practice, and if so, how? What are their views on the role of other societal actors in steering AI towards the stated principles? It is concluded that some three of the largest amongst them have carried out valuable steps towards implementation, in particular by developing and open sourcing new software tools. To them, charges of mere ‘ethics washing’ do not apply. Moreover, some 10 companies from both the USA and Europe have publicly endorsed the position that apart from self-regulation, AI is in urgent need of governmental regulation. They mostly advocate focussing regulation on high-risk applications of AI, a policy which to them represents the sensible middle course between laissez-faire on the one hand and outright bans on technologies on the other. The future shaping of standards, ethical codes, and laws as a result of these regulatory efforts remains, of course, to be determined. (shrink)

During the last decade there has been burgeoning research concerning the ways in which we should think of and apply the concept of responsibility for Artificial Intelligence. Despite this conceptual richness, there is still a lack of consensus regarding what Responsible AI entails on both conceptual and practical levels. The aim of this paper is to connect the ethical dimension of responsibility in Responsible AI with Aristotelian virtue ethics, where notions of context and dianoetic virtues play a grounding (...) role for the concept of moral responsibility. The paper starts by highlighting the important difficulties in assigning responsibility to either technologies themselves or to their developers. Top-down and bottom-up approaches to moral responsibility are then contrasted, as we explore how they could inform debates about Responsible AI. We highlight the limits of the former ethical approaches and build the case for classical Aristotelian virtue ethics. We show that two building blocks of Aristotle’s ethics, dianoetic virtues and the context of actions, although largely ignored in the literature, can shed light on how we could think of moral responsibility for both AI and humans. We end by exploring the practical implications of this particular understanding of moral responsibility along the triadic dimensions of ethics by design, ethics in design and ethics for designers. (shrink)

Artificial intelligence and automation have become pervasive in news media, influencing journalism from news gathering to news distribution. As algorithms are increasingly determining editorial decisions, specific concerns have been raised with regard to the responsible and accountable use of AI-driven tools by news media, encompassing new regulatory and ethical questions. This contribution aims to analyze whether and to what extent the use of AI technology in news media and journalism is currently regulated and debated within the European Union and (...) the Council of Europe. Through a document analysis of official policy documents, combined with a data mining approach and an inductive thematic analysis, the study looks at how news media are dealt with, in particular regarding their responsibilities towards their users and society. The findings show that regulatory frameworks about AI rarely include media, but if they do, they associate them with issues such as disinformation, data, and AI literacy, as well as diversity, plurality, and social responsibility. (shrink)

Responsible AI (RAI) guidelines aim to ensure that AI systems respect democratic values. While a step in the right direction, they currently fail to impact practice. Our work discusses reasons for this lack of impact and clusters them into five areas: (1) the abstract nature of RAI guidelines, (2) the problem of selecting and reconciling values, (3) the difficulty of operationalising RAI success metrics, (4) the fragmentation of the AI pipeline, and (5) the lack of internal advocacy and accountability. (...) Afterwards, we introduce a number of approaches to RAI from a range of disciplines, exploring their potential as solutions to the identified challenges. We anchor these solutions in practice through concrete examples, bridging the gap between the theoretical considerations of RAI and on-the-ground processes that currently shape how AI systems are built. Our work considers the socio-technical nature of RAI limitations and the resulting necessity of producing socio-technical solutions. (shrink)

We think that Central Asia should come up with its own AI Ethics Principles which we propose to name as “Nomadic AI Principles”.

When artificial intelligence (AI) is used to make high-stakes decisions, some worry that this will create a morally troubling responsibility gap—that is, a situation in which nobody is morally responsible for the actions and outcomes that result. Since the responsibility gap might be thought to result from individuals lacking knowledge of the future behavior of AI systems, it can be and has been suggested that deploying explainable artificial intelligence (XAI) techniques will help us to avoid it. These techniques provide (...) humans with certain forms of understanding of the systems in question. In this paper, I consider whether existing XAI techniques can indeed close the responsibility gap. I identify a number of significant limits to their ability to do so. Ensuring that responsibility for AI-assisted outcomes is maintained may require using different techniques in different circumstances, and potentially also developing new techniques that can avoid each of the issues identified. (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)

In recent years, the European Union has advanced towards responsible and sustainable Artificial Intelligence (AI) research, development and innovation. While the Ethics Guidelines for Trustworthy AI released in 2019 and the AI Act in 2021 set the starting point for a European Ethical AI, there are still several challenges to translate such advances into the public debate, education and practical learning. This paper contributes towards closing this gap by reviewing the approaches that can be found in the existing literature (...) and by interviewing 11 experts across five countries to help define educational strategies, competencies and resources needed for the successful implementation of Trustworthy AI in Higher Education (HE) and to reach students from all disciplines. The findings are presented in the form of recommendations both for educators and policy incentives, translating the guidelines into HE teaching and practice, so that the next generation of young people can contribute to an ethical, safe and cutting-edge AI made in Europe. (shrink)

In the scenario of growing polarization of promises and dangers that surround artificial intelligence (AI), how to introduce responsible AI and robotics in healthcare? In this paper, we develop an ethical–political approach to introduce democratic mechanisms to technological development, what we call “Caring in the In-Between”. Focusing on the multiple possibilities for action that emerge in the realm of uncertainty, we propose an ethical and responsible framework focused on care actions in between fears and hopes. Using the theoretical (...) perspective of Science and Technology Studies and empirical research, “Caring in the In-Between” is based on three movements: the first is a change of focus from the world of promises and dangers to the world of uncertainties; the second is a conceptual shift from assuming a relationship with robotics based on a Human–Robot Interaction to another focused on the network in which the robot is embedded (the “Robot Embedded in a Network”); and the last is an ethical shift from a general normative framework to a discussion on the context of use. Based on these suggestions, “Caring in the In-Between” implies institutional challenges, as well as new practices in healthcare systems. It is articulated around three simultaneous processes, each of them related to practical actions in the “in-between” dimensions considered: monitoring relations and caring processes, through public engagement and institutional changes; including concerns and priorities of stakeholders, with the organization of participatory processes and alternative forms of representation; and making fears and hopes commensurable, through the choice of progressive and reversible actions. (shrink)

In his article, “What Should ChatGPT Mean for Bioethics?” Cohen (2023) highlights novel bioethical issues raised by the emergence of ChatGPT and generative AI more broadly. Among the thought-provok...

In the scenario of growing polarization of promises and dangers that surround artificial intelligence (AI), how to introduce responsible AI and robotics in healthcare? In this paper, we develop an ethical–political approach to introduce democratic mechanisms to technological development, what we call “Caring in the In-Between”. Focusing on the multiple possibilities for action that emerge in the realm of uncertainty, we propose an ethical and responsible framework focused on care actions in between fears and hopes. Using the theoretical (...) perspective of Science and Technology Studies and empirical research, “Caring in the In-Between” is based on three movements: the first is a change of focus from the world of promises and dangers to the world of uncertainties; the second is a conceptual shift from assuming a relationship with robotics based on a Human–Robot Interaction to another focused on the network in which the robot is embedded (the “Robot Embedded in a Network”); and the last is an ethical shift from a general normative framework to a discussion on the context of use. Based on these suggestions, “Caring in the In-Between” implies institutional challenges, as well as new practices in healthcare systems. It is articulated around three simultaneous processes, each of them related to practical actions in the “in-between” dimensions considered: monitoring relations and caring processes, through public engagement and institutional changes; including concerns and priorities of stakeholders, with the organization of participatory processes and alternative forms of representation; and making fears and hopes commensurable, through the choice of progressive and reversible actions. (shrink)

Advances in AI research have brought increasingly sophisticated capabilities to AI systems and heightened the societal consequences of their use. Researchers and industry professionals have responded by contemplating responsible principles and practices for AI system design. At the same time, defense institutions are contemplating ethical guidelines and requirements for the development and use of AI for warfare. However, varying ethical and procedural approaches to technological development, research emphasis on offensive uses of AI, and lack of appropriate venues for multistakeholder (...) dialogue have led to differing operationalization of responsible AI principles and practices among civilian and defense entities. We argue that the disconnect between civilian and defense responsible development and use practices leads to underutilization of responsible AI research and hinders the implementation of responsible AI principles in both communities. We propose a research roadmap and recommendations for dialogue to increase exchange of responsible AI development and use practices for AI systems between civilian and defense communities. We argue that generating more opportunities for exchange will stimulate global progress in the implementation of responsible AI principles. (shrink)

In recent years, research in robotics and artificial intelligence (AI) has made rapid progress. It is expected that robots and AI will play a part in the field of nursing and their role might broaden in the future. However, there are areas of nursing practice that cannot or should not be entrusted to robots and AI, because nursing is a highly humane practice, and therefore, there would, perhaps, be some practices that should not be replicated by robots or AI. Therefore, (...) this paper focuses on several ethical concepts (advocacy, accountability, cooperation, and caring) that are considered important in nursing practice, and examines whether it is possible to implement these ethical concepts in robots and AI by analyzing the concepts and the current state of robotics and AI technology. Advocacy: Among the components of advocacy, safeguarding and apprising can be more easily implemented, while elements that require emotional communication with patients, such as valuing and mediating, are difficult to implement. Accountability: Robotic nurses with explainable AI have a certain level of accountability. However, the concept of explanation has problems of infinite regression and attribution of responsibility. Cooperation: If robot nurses are recognized as members of a community, they require the same cooperation as human nurses. Caring: More difficulties are expected in care-receiving than in caregiving. However, the concept of caring itself is ambiguous and should be explored further. Accordingly, our analysis suggests that, although some difficulties can be expected in each of these concepts, it cannot be said that it is impossible to implement them in robots and AI. However, even if it were possible to implement these functions in the future, further study is needed to determine whether such robots or AI should be used for nursing care. In such discussions, it will be necessary to involve not only ethicists and nurses but also an array of society members. (shrink)

Today humanity is in the midst of the massive expansion of new and fundamental technology, represented by advanced artificial intelligence (AI) systems. The ongoing revolution of these technologies and their profound impact across various sectors, has triggered discussions about the characteristics and values that should guide their use and development in a responsible manner. In this paper, we conduct a systematic literature review with the aim of pointing out existing challenges and required principles in AI-based systems in different industries. (...) We discuss our findings and provide general recommendations to be considered during AI deployment in production. The results have shown many gaps and concerns towards responsible AI and integration of complex AI models in the industry that the research community could address. (shrink)

Most workers report that they are alienated from their jobs and find their workplaces to be stifling and uninviting. Given this condition, the introduction of computer technology, including AI, will only make matters worse, unless a more humane organizational culture is created. The key point in this article is the need to produce a responsible technology, so that employees are not further overworked and manipulated. To achieve this end, phenomenology is invoked, particularly the life world, to provide technology with (...) a human foundation. In practice, this shift in philosophy means that when AI is introduced into an organization, workers have a central role in the design, implementation, and evaluation of this technology. Computer technology can thus promote human flourishing instead of control and alienation in workplaces. (shrink)

Most workers report that they are alienated from their jobs and find their workplaces to be stifling and uninviting. Given this condition, the introduction of computer technology, including AI, will only make matters worse, unless a more humane organizational culture is created. The key point in this article is the need to produce a responsible technology, so that employees are not further overworked and manipulated. To achieve this end, phenomenology is invoked, particularly the life world, to provide technology with (...) a human foundation. In practice, this shift in philosophy means that when AI is introduced into an organization, workers have a central role in the design, implementation, and evaluation of this technology. Computer technology can thus promote human flourishing instead of control and alienation in workplaces. (shrink)

The purpose of this paper is to discuss the responsibility of AI experts for guiding the development of AI in a desirable direction. More specifically, the aim is to answer the following research question: To what extent are AI experts responsible in a forward-looking way for effects of AI technology that go beyond the immediate concerns of the programmer or designer? AI experts, in this paper conceptualised as experts regarding the technological aspects of AI, have knowledge and control of (...) AI technology that non-experts do not have. Drawing on responsibility theory, theories of the policy process, and critical algorithm studies, we discuss to what extent this capacity, and the positions that these experts have to influence the AI development, make AI experts responsible in a forward-looking sense for consequences of the use of AI technology. We conclude that, as a professional collective, AI experts, to some extent, are responsible in a forward-looking sense for consequences of use of AI technology that they could foresee, but with the risk of increased influence of AI experts at the expense of other actors. It is crucial that a diversity of actors is included in democratic processes on the future development of AI, but for this to be meaningful, AI experts need to take responsibility for how the AI technology they develop affects public deliberation. (shrink)

As more and more decisions that have a significant ethical dimension are being outsourced to AI systems, it is important to have a definition of moral responsibility that can be applied to AI systems. Moral responsibility for an outcome of an agent who performs some action is commonly taken to involve both a causal condition and an epistemic condition: the action should cause the outcome, and the agent should have been aware -- in some form or other -- of the (...) possible moral consequences of their action. This paper presents a formal definition of both conditions within the framework of causal models. I compare my approach to the existing approaches of Braham and van Hees (BvH) and of Halpern and Kleiman-Weiner (HK). I then generalize my definition into a degree of responsibility. (shrink)

With Artificial Intelligence entering our lives in novel ways—both known and unknown to us—there is both the enhancement of existing ethical issues associated with AI as well as the rise of new ethical issues. There is much focus on opening up the ‘black box’ of modern machine-learning algorithms to understand the reasoning behind their decisions—especially morally salient decisions. However, some applications of AI which are no doubt beneficial to society rely upon these black boxes. Rather than requiring algorithms to be (...) transparent we should focus on constraining AI and those machines powered by AI within microenvironments—both physical and virtual—which allow these machines to realize their function whilst preventing harm to humans. In the field of robotics this is called ‘envelopment’. However, to put an ‘envelope’ around AI-powered machines we need to know some basic things about them which we are often in the dark about. The properties we need to know are the: training data, inputs, functions, outputs, and boundaries. This knowledge is a necessary first step towards the envelopment of AI-powered machines. It is only with this knowledge that we can responsibly regulate, use, and live in a world populated by these machines. (shrink)

Michael A PetersBeijing Normal UniversityChatGPT is an AI chatbot released by OpenAI on November 30, 2022 and a ‘stable release’ on February 13, 2023. It belongs to OpenAI’s GPT-3 family (generativ...

In voicing commitments to the principle that the adoption of artificial-intelligence (AI) tools by armed forces should be done responsibly, a growing number of states have referred to a concept of “Responsible AI.” As part of an effort to help develop the substantive contours of that concept in meaningful ways, this position paper introduces a notion of “responsible reliance.” It is submitted that this notion could help the policy conversation expand from its current relatively narrow focus on interactions (...) between an AI system and its end-user to also encompass the wider set of interdependencies involved in fulfilling legal obligations concerning the use of AI in armed conflicts. The authors argue that to respect international humanitarian law and ensure accountability, states ought to devise and maintain a framework that ensures that natural persons involved in the use an AI tool in an armed conflict could responsibly rely at least on: (1) the tool’s technical aspects, (2) the conduct of other people involved in development and use of that AI tool; and (3) the policies and processes implemented at the state level. According to the authors, the “responsible reliance” notion could serve, among other examples, as a basis on which to articulate legal requirements, prohibitions, and permissions across diverse areas, from the design of AI tools to human-machine interactions to configuration of responsible-command frameworks. (shrink)

In this paper, we first classify different types of second opinions and evaluate the ethical and epistemological implications of providing those in a clinical context. Second, we discuss the issue of how artificial intelligent could replace the human cognitive labour of providing such second opinion and find that several AI reach the levels of accuracy and efficiency needed to clarify their use an urgent ethical issue. Third, we outline the normative conditions of how AI may be used as second opinion (...) in clinical processes, weighing the benefits of its efficiency against concerns of responsibility attribution. Fourth, we provide a ‘rule of disagreement’ that fulfils these conditions while retaining some of the benefits of expanding the use of AI-based decision support systems in clinical contexts. This is because the rule of disagreement proposes to use AI as much as possible, but retain the ability to use human second opinions to resolve disagreements between AI and physician-in-charge. Fifth, we discuss some counterarguments. (shrink)

This article examines ethical implications of the growing AI carbon footprint, focusing on the fair distribution of prospective responsibilities among groups of involved actors. First, major groups of involved actors are identified, including AI scientists, AI industry, and AI infrastructure providers, from datacenters to electrical energy suppliers. Second, responsibilities of AI scientists concerning climate warming mitigation actions are disentangled from responsibilities of other involved actors. Third, to implement these responsibilities nudging interventions are suggested, leveraging on AI competitive games which would (...) prize research combining better system accuracy with greater computational and energy efficiency. Finally, in addition to the AI carbon footprint, it is argued that another ethical issue with a genuinely global dimension is now emerging in the AI ethics agenda. This issue concerns the threats that AI-powered cyberweapons pose to the digital command, control, and communication infrastructure of nuclear weapons systems. (shrink)

Traditional medical practices and relationships are changing given the widespread adoption of AI-driven technologies across the various domains of health and healthcare. In many cases, these new technologies are not specific to the field of healthcare. Still, they are existent, ubiquitous, and commercially available systems upskilled to integrate these novel care practices. Given the widespread adoption, coupled with the dramatic changes in practices, new ethical and social issues emerge due to how these systems nudge users into making decisions and changing (...) behaviours. This article discusses how these AI-driven systems pose particular ethical challenges with regards to nudging. To confront these issues, the value sensitive design approach is adopted as a principled methodology that designers can adopt to design these systems to avoid harming and contribute to the social good. The AI for Social Good factors are adopted as the norms constraining maleficence. In contrast, higher-order values specific to AI, such as those from the EU High-Level Expert Group on AI and the United Nations Sustainable Development Goals, are adopted as the values to be promoted as much as possible in design. The use case of Amazon Alexa's Healthcare Skills is used to illustrate this design approach. It provides an exemplar of how designers and engineers can begin to orientate their design programs of these technologies towards the social good. (shrink)

This chapter argues for a structural injustice approach to the governance of AI. Structural injustice has an analytical and an evaluative component. The analytical component consists of structural explanations that are well-known in the social sciences. The evaluative component is a theory of justice. Structural injustice is a powerful conceptual tool that allows researchers and practitioners to identify, articulate, and perhaps even anticipate, AI biases. The chapter begins with an example of racial bias in AI that arises from structural injustice. (...) The chapter then presents the concept of structural injustice as introduced by the philosopher Iris Marion Young. The chapter moreover argues that structural injustice is well suited as an approach to the governance of AI and compares this approach to alternative approaches that start from analyses of harms and benefits or from value statements. The chapter suggests that structural injustice provides methodological and normative foundations for the values and concerns of Diversity, Equity, and Inclusion. The chapter closes with an outlook onto the idea of “structure” and on responsibility. The idea of a structure is central to justice. An open theoretical research question is to what extent AI is itself part of the structure of society. Finally, the practice of responsibility is central to structural injustice. Even if they cannot be held responsible for the existence of structural injustice, every individual and every organization has some responsibility to address structural injustice going forward. (shrink)

Bioethics, Volume 36, Issue 2, Page 162-169, February 2022.

The aim of this literature review was to compose a narrative review supported by a systematic approach to critically identify and examine concerns about accountability and the allocation of responsibility and legal liability as applied to the clinician and the technologist as applied the use of opaque AI-powered systems in clinical decision making. This review questions if it is permissible for a clinician to use an opaque AI system in clinical decision making and if a patient was harmed as a (...) result of using a clinician using an AIS’s suggestion, how would responsibility and legal liability be allocated? Literature was systematically searched, retrieved, and reviewed from nine databases, which also included items from three clinical professional regulators, as well as relevant grey literature from governmental and non-governmental organisations. This literature was subjected to inclusion/exclusion criteria; those items found relevant to this review underwent data extraction. This review found that there are multiple concerns about opacity, accountability, responsibility and liability when considering the stakeholders of technologists and clinicians in the creation and use of AIS in clinical decision making. Accountability is challenged when the AIS used is opaque, and allocation of responsibility is somewhat unclear. Legal analysis would help stakeholders to understand their obligations and prepare should an undesirable scenario of patient harm eventuate when AIS were used. (shrink)

Whatever one makes of the relationship between free will and moral responsibility – e.g. whether it’s the case that we can have the latter without the former and, if so, what conditions must be met; whatever one thinks about whether artificially intelligent agents might ever meet such conditions, one still faces the following questions. What is the value of moral responsibility? If we take moral responsibility to be a matter of being a fitting target of moral blame or praise, what (...) are the goods attached to them? The debate concerning ‘machine morality’ is often hinged on whether artificial agents are or could ever be morally responsible, and it is generally taken for granted (following Matthias 2004) that if they cannot, they pose a threat to the moral responsibility system and associated goods. In this paper, I challenge this assumption by asking what the goods of this system, if any, are, and what happens to them in the face of artificially intelligent agents. I will argue that they neither introduce new problems for the moral responsibility system nor do they threaten what we really (ought to) care about. I conclude the paper with a proposal for how to secure this objective. (shrink)

AI systems that demonstrate significant bias or lower than claimed accuracy, and resulting in individual and societal harms, continue to be reported. Such reports beg the question as to why such systems continue to be funded, developed and deployed despite the many published ethical AI principles. This paper focusses on the funding processes for AI research grants which we have identified as a gap in the current range of ethical AI solutions such as AI procurement guidelines, AI impact assessments and (...) AI audit frameworks. We highlight the responsibilities of funding bodies to ensure investment is channelled towards trustworthy and safe AI systems and provides case studies as to how other ethical funding principles are managed. We offer a first sight of two proposals for funding bodies to consider regarding procedures they can employ. The first proposal is for the inclusion of a Trustworthy AI Statement’ section in the grant application form and offers an example of the associated guidance. The second proposal outlines the wider management requirements of a funding body for the ethical review and monitoring of funded projects to ensure adherence to the proposed ethical strategies in the applicants Trustworthy AI Statement. The anticipated outcome for such proposals being employed would be to create a ‘stop and think’ section during the project planning and application procedure requiring applicants to implement the methods for the ethically aligned design of AI. In essence it asks funders to send the message “if you want the money, then build trustworthy AI!”. (shrink)

To support the trustworthiness of AI systems, it is essential to have precise methods to determine what or who is to account for the behaviour, or the outcome, of AI systems. The assignment of responsibility to an AI system is closely related to the identification of individuals or elements that have caused the outcome of the AI system. In this work, we present an overview of approaches that aim at modelling responsibility of AI systems, discuss their advantages and shortcomings to (...) deal with various aspects of the notion of responsibility, and present research gaps and ways forward. (shrink)

I argue for responsibility internalism. That is, moral responsibility (i.e., accountability, or being apt for praise or blame) depends only on factors internal to agents. Employing this view, I also argue that no one is responsible for what AI does but this isn’t morally problematic in a way that counts against developing or using AI. Responsibility is grounded in three potential conditions: the control (or freedom) condition, the epistemic (or awareness) condition, and the causal responsibility condition (or consequences). I (...) argue that causal responsibility is irrelevant for moral responsibility, and that the control condition and the epistemic condition depend only on factors internal to agents. Moreover, since what AI does is at best a consequence of our actions, and the consequences of our actions are irrelevant to our responsibility, no one is responsible for what AI does. That is, the so-called responsibility gap exits. However, this isn’t morally worrisome for developing or using AI. Firstly, I argue, current AI doesn’t generate a new kind of concern about responsibility that the older technologies don’t. Then, I argue that responsibility gap is not worrisome because neither responsibility gap, nor my argument for its existence, entails that no one can be justly punished, held accountable, or incurs duties in reparations when AI causes a harm. (shrink)

In this book, the author examines the ethical implications of Artificial Intelligence systems as they integrate and replace traditional social structures in new sociocognitive-technological environments. She discusses issues related to the integrity of researchers, technologists, and manufacturers as they design, construct, use, and manage artificially intelligent systems; formalisms for reasoning about moral decisions as part of the behavior of artificial autonomous systems such as agents and robots; and design methodologies for social agents based on societal, moral, and legal values. Throughout (...) the book the author discusses related work, conscious of both classical, philosophical treatments of ethical issues and the implications in modern, algorithmic systems, and she combines regular references and footnotes with suggestions for further reading. This short overview is suitable for undergraduate students, in both technical and non-technical courses, and for interested and concerned researchers, practitioners, and citizens. (shrink)

We discuss the implementation of Responsible Research and Innovation (RRI) within a project for the development of an AI-supported exergame for assisted movement training, outline outcomes and reflect on methodological opportunities and limitations. We adopted the responsibility-by-design (RbD) standard (CEN CWA 17796:2021) supplemented by methods for collaborative, ethical reflection to foster and support a shift towards a culture of trustworthiness inherent to the entire development process. An embedded ethicist organised the procedure to instantiate a collaborative learning effort and implement (...) RRI in a translational context. Within the interdisciplinary setting of the collaboration and with the support of a technoethicist, we successfully identified relevant, project-specific challenges and developed a roadmap with derived actions, thus meaningfully integrating RRI into the development process. We discuss the methodological procedure in terms of its effectiveness and efficiency, the allocation of responsibilities and roles, particularly regarding potential frictions in the interdisciplinary context with embedded ethics, and the challenges of the translational context. We conclude that the responsibility-by-design standard effectively established a productive workflow for collaborative investigation and work on ethical challenges. We reflect on methodological difficulties and propose possible avenues to our approach. (shrink)

The concept of agency as applied to technological artifacts has become an object of heated debate in the context of AI research because some AI researchers ascribe to programs the type of agency traditionally associated with humans. Confusion about agency is at the root of misconceptions about the possibilities for future AI. We introduce the concept of a triadic agency that includes the causal agency of artifacts and the intentional agency of humans to better describe what happens in AI as (...) it functions in real-world contexts. We use the VW emission fraud case to explain triadic agency since in this case a technological artifact, namely software, was an essential part of the wrongdoing and the software might be said to have agency in the wrongdoing. We then extend the case to include futuristic AI, imagining AI that becomes more and more autonomous. (shrink)

Upon previous research, the matrilineal system seems to oppose Islamic teaching. However, the matrilineal system practiced by the Minangkabau society in West Sumatra, Indonesia has its uniqueness. Thus, this study aims to examine the correlation between the Qur’an and gender roles within the context of Minangkabau customs, specifically focusing on the matrilineal aspect. The present study employs qualitative methods for conducting library research through critical analysis. This study discovered that the matrilineal system practiced by the Minangkabau society aligns with Qur’anic (...) teachings that respect the position of women in Islam. In the matrilineal system of Minangkabau, women hold significant strength and play an influential role in cultural and traditional practices. The Minangkabau tribe’s matrilineal system encompasses three key elements: marriage, inheritance and offspring. The marriage system is exogamous. Additionally, the practice of marriage is localised, with the husband typically residing in the wife’s household. Their inheritance is divided into pusako tinggi [from ancestors from generation to generation] and pusako randah (from the efforts of married couple). Women get pusako tinggi as they serve as protectors and a symbol of their community. In comparison, men take the responsibility of overseeing its preservation to prevent any form of misuse. Pusako randah is distributed as Islamic teaching. Lineage determination is primarily based on paternal lineage, aligning with Islamic teaching. Upon comprehensive analysis, it becomes evident that there is no conflict between the matrilineal system and Islamic teachings, particularly concerning principles of gender equality. Both women and men have significant roles, as the Qur’an provides extensive explanations regarding gender equality.Contribution: This research holds significant findings in establishing a great relation between Islamic teachings based on the Qur’an and the matrilineal principles practiced by the Minangkabau tribe. (shrink)

Recent work in AI ethics has come to bear on questions of responsibility. Specifically, questions of whether the nature of AI-based systems render various notions of responsibility inappropriate. While substantial attention has been given to backward-looking senses of responsibility, there has been little consideration of forward-looking senses of responsibility. This paper aims to plug this gap, and will concern itself with responsibility as moral obligation, a particular kind of forward-looking sense of responsibility. Responsibility as moral obligation is predicated on the (...) idea that agents have at least some degree of control over the kinds of systems they create and deploy. AI systems, by virtue of their ability to learn from experience once deployed, and their often experimental nature, may therefore pose a significant challenge to forward-looking responsibility. Such systems might not be able to have their course altered, and so even if their initial programming determines their goals, the means by which they achieve these goals may be outside the control of human operators. In cases such as this, we might say that there is a gap in moral obligation. However, in this paper, I argue that there are no “gaps” in responsibility as moral obligation, as this question comes to bear on AI systems. I support this conclusion by focusing on the nature of risks when developing technology, and by showing that technological assessment is not only about the consequences that a specific technology might have. Technological assessment is more than merely consequentialist, and should also include a hermeneutic component, which looks at the societal meaning of the system. Therefore, while it may be true that the creators of AI systems might not be able to fully appreciate what the consequences of their systems might be, this does not undermine or render improper their responsibility as moral obligation. (shrink)

In a representative vignette study in Germany with 1,653 respondents, we investigated laypeople’s attribution of moral responsibility in collaborative medical diagnosis. Specifically, we compare people’s judgments in a setting in which physicians are supported by an AI-based recommender system to a setting in which they are supported by a human colleague. It turns out that people tend to attribute moral responsibility to the artificial agent, although this is traditionally considered a category mistake in normative ethics. This tendency is stronger when (...) people believe that AI may become conscious at some point. In consequence, less responsibility is attributed to human agents in settings with hybrid diagnostic teams than in settings with human-only diagnostic teams. Our findings may have implications for behavior exhibited in contexts of collaborative medical decision making with AI-based as opposed to human recommenders because less responsibility is attributed to agents who have the mental capacity to care about outcomes. (shrink)

Artificial intelligence increasingly executes tasks that previously only humans could do, such as drive a car, fight in war, or perform a medical operation. However, as the very best AI systems tend to be the least controllable and the least transparent, some scholars argued that humans can no longer be morally responsible for some of the AI-caused outcomes, which would then result in a responsibility gap. In this paper, I assume, for the sake of argument, that at least some (...) of the most sophisticated AI systems do indeed create responsibility gaps, and I ask whether we can bridge these gaps at will, viz. whether certain people could take responsibility for AI-caused harm simply by performing a certain speech act, just as people can give permission for something simply by performing the act of consent. So understood, taking responsibility would be a genuine normative power. I first discuss and reject the view of Champagne and Tonkens, who advocate a view of taking liability. According to this view, a military commander can and must, ahead of time, accept liability to blame and punishment for any harm caused by autonomous weapon systems under her command. I then defend my own proposal of taking answerability, viz. the view that people can makes themselves morally answerable for the harm caused by AI systems, not only ahead of time but also when harm has already been caused. (shrink)

Deep learning AI systems have proven a wide capacity to take over human-related activities such as car driving, medical diagnosing, or elderly care, often displaying behaviour with unpredictable consequences, including negative ones. This has raised the question whether highly autonomous AI may qualify as morally responsible agents. In this article, we develop a set of four conditions that an entity needs to meet in order to be ascribed moral responsibility, by drawing on Aristotelian ethics and contemporary philosophical research. We (...) encode these conditions and generate a flowchart that we call the Moral Responsibility Test. This test can be used as a tool both to evaluate whether an entity is a morally responsible agent and to inform human moral decision-making over the influencing variables of the context of action. We apply the test to the case of Artificial Moral Advisors and conclude that this form of AI cannot qualify as morally responsible agents. We further discuss the implications for the use of AMAs as moral enhancement and show that using AMAs to offload human responsibility is inadequate. We argue instead that AMAs could morally enhance users if they are interpreted as enablers for moral knowledge of the contextual variables surrounding human moral decision-making, with the implication that such a use might actually enlarge human moral responsibility. (shrink)

We might worry that our seamless reliance on AI systems makes us prone to adopting the strange errors that these systems commit. One proposed solution is to design AI systems so that they are not phenomenally transparent to their users. This stops cognitive extension and the automatic uptake of errors. Although we acknowledge that some aspects of AI extension are concerning, we can address these concerns without discouraging transparent employment altogether. First, we believe that the potential danger should be put (...) into perspective – many unreliable technologies are unlikely to be used transparently precisely because they are unreliable. Second, an agent who transparently employs a resource may also reflect (opaquely) on its reliability. Finally, agents can rely on a process transparently and be yanked out of their transparent use when it turns unreliable. When an agent is responsive to the reliability of their process in this way, they have epistemically integrated it, and the beliefs they form with it are formed responsibly. This prevents the agent from automatically incorporating problematic beliefs. Responsible (and transparent) use of AI resources – and consequently responsible AI extension – is hence possible. We end the paper with several design and policy recommendations that encourage epistemic integration of AI-involving belief-forming processes. (shrink)

As sophisticated artificial intelligence software becomes more ubiquitously and more intimately integrated within domains of traditionally human endeavor, many are raising questions over how responsibility (be it moral, legal, or causal) can be understood for an AI’s actions or influence on an outcome. So called “responsibility gaps” occur whenever there exists an apparent chasm in the ordinary attribution of moral blame or responsibility when an AI automates physical or cognitive labor otherwise performed by human beings and commits an error. Healthcare (...) administration is an industry ripe for responsibility gaps produced by these kinds of AI. The moral stakes of healthcare are often life and death, and the demand for reducing clinical uncertainty while standardizing care incentivizes the development and integration of AI diagnosticians and prognosticators. In this paper, we argue that (1) responsibility gaps are generated by “black box” healthcare AI, (2) the presence of responsibility gaps (if unaddressed) creates serious moral problems, (3) a suitable solution is for relevant stakeholders to voluntarily responsibilize the gaps, taking on some moral responsibility for things they are not, strictly speaking, blameworthy for, and (4) should this solution be taken, black box healthcare AI will be permissible in the provision of healthcare. (shrink)

Artificial intelligence (AI) is becoming increasingly influential in most people’s lives. This raises many philosophical questions. One is what responsibility we have as individuals to guide the development of AI in a desirable direction. More specifically, how should this responsibility be distributed among individuals and between individuals and other actors? We investigate this question from the perspectives of five principles of distribution that dominate the discussion about responsibility in connection with climate change: effectiveness, equality, desert, need, and ability. Since much (...) is already written about these distributions in that context, we believe much can be gained if we can make use of this discussion also in connection with AI. Our most important findings are: (1) Different principles give different answers depending on how they are interpreted but, in many cases, different interpretations and different principles agree and even strengthen each other. If for instance ‘equality-based distribution’ is interpreted in a consequentialist sense, effectiveness, and through it, ability, will play important roles in the actual distributions, but so will an equal distribution as such, since we foresee that an increased responsibility of underrepresented groups will make the risks and benefits of AI more equally distributed. The corresponding reasoning is true for need-based distribution. (2) If we acknowledge that someone has a certain responsibility, we also have to acknowledge a corresponding degree of influence for that someone over the matter in question. (3) Independently of which distribution principle we prefer, ability cannot be dismissed. Ability is not fixed, however and if one of the other distributions is morally required, we are also morally required to increase the ability of those less able to take on the required responsibility. (shrink)

Many studies have reported neural correlates of visual awareness across several brain regions, including the sensory, parietal, and frontal areas. In most of these studies, participants were instructed to explicitly report their perceptual experience through a button press or verbal report. It is conceivable, however, that explicit reporting itself may trigger specific neural responses that can confound the direct examination of the neural correlates of visual awareness. This suggests the need to assess visual awareness without explicit reporting. One way to (...) achieve this is to develop a technique to predict the visual awareness of participants based on their peripheral responses. Here, we used eye movements and pupil sizes to decode trial-by-trial changes in the awareness of a stimulus whose visibility was deteriorated due to adaptation-induced blindness. In the experiment, participants judged whether they perceived a target stimulus and rated the confidence they had in their perceptual judgment, while their eye movements and pupil sizes were recorded. We found that not only perceptual decision but also perceptual confidence can be separately decoded from the eye movement and pupil size. We discuss the potential of this technique with regard to assessing visual awareness in future neuroimaging experiments. (shrink)

Generative artificial intelligence (AI) has the potential to transform many aspects of scholarly publishing. Authors, peer reviewers, and editors might use AI in a variety of ways, and those uses might augment their existing work or might instead be intended to replace it. We are editors of bioethics and humanities journals who have been contemplating the implications of this ongoing transformation. We believe that generative AI may pose a threat to the goals that animate our work but could also be (...) valuable for achieving those goals. In the interests of fostering a wider conversation about how generative AI may be used, we have developed a preliminary set of recommendations for its use in scholarly publishing. We hope that the recommendations and rationales set out here will help the scholarly community navigate toward a deeper understanding of the strengths, limits, and challenges of AI for responsible scholarly work. (shrink)

Philosophical and sociological approaches in technology have increasingly shifted toward describing AI (artificial intelligence) systems as ‘(moral) agents,’ while also attributing ‘agency’ to them. It is only in this way – so their principal argument goes – that the effects of technological components in a complex human-computer interaction can be understood sufficiently in phenomenological-descriptive and ethical-normative respects. By contrast, this article aims to demonstrate that an explanatory model only achieves a descriptively and normatively satisfactory result if the concepts of ‘(moral) (...) agent’ and ‘(moral) agency’ are exclusively related to human agents. Initially, the division between symbolic and sub-symbolic AI, the black box character of (deep) machine learning, and the complex relationship network in the provision and application of machine learning are outlined. Next, the ontological and action-theoretical basic assumptions of an ‘agency’ attribution regarding both the current teleology-naturalism debate and the explanatory model of actor network theory are examined. On this basis, the technical-philosophical approaches of Luciano Floridi, Deborah G. Johnson, and Peter-Paul Verbeek will all be critically discussed. Despite their different approaches, they tend to fully integrate computational behavior into their concept of ‘(moral) agency.’ By contrast, this essay recommends distinguishing conceptually between the different entities, causalities, and relationships in a human-computer interaction, arguing that this is the only way to do justice to both human responsibility and the moral significance and causality of computational behavior. (shrink)