Après les guerres du siècle dernier qui ont remis en cause l'idée de progrès, devant les formes multiples des guerres actuelles, peut-on encore penser la paix aujourd'hui? A l'encontre des idées conservatrices de la paix où cette dernière, conçue comme ordre et stabilité, a souvent été le corrélat des Etats policiers, la pensée de la paix, telle que l'élabore Levinas, relève d'une critique de l'autoritarisme politique et permet d'élaborer un nouveau concept de liberté affranchi de la tutelle des Etats. La (...) paix n'est pas ici une idée abstraite mais porte le sujet hors de toutes les formes de confort moral ou de conformisme politique. C'est à un Levinas neuf que nous avons ici à faire : tout en dialoguant avec la tradition moderne et contemporaine de la philosophie politique, il nous montre que la paix est une forme d'insubordination qui aspire à "désembourgeoiser la révolution". Une lecture intranquille de Levinas. (shrink)

Civics and Moral Educationwas implemented as a new moral education programme in Singapore schools in 1992. This paper argues that the underlying theme is that of citizenship training and that new measures are under way to strengthen the capacity of the school system to transmit national values for economic and political socialisation. The motives and motivation for retaining a formal moral education programme have remained strong. A discussion of the structure and content of key modules in Civics (...) and Moral Education shows how curriculum writers have attempted to integrate separate strands of civics and moral education from earlier programmes and present them in a less divisive manner. The paper also assesses the impact of the written civics and moral education programme on teachers and pupils, and the way in which strategies have been used by curriculum agencies to overcome possible obstacles at the curriculum implementation phase. (shrink)

This collection of quotes demonstrates the elegant simplicity of Ai Weiwei's thoughts on key aspects of his art, politics, and life. A master at communicating powerful ideas in astonishingly few words, Ai Weiwei is known for his innovative use of social media to disseminate his views. The book is organized into six categories: freedom of expression; art and activism; government, power, and moral choices; the digital world; history, the historical moment, and the future; and personal reflections. Together, these quotes (...) span some of the most revealing moments of Ai Weiwei's eventful career-from his risky investigation into student deaths in the 2008 Sichuan earthquake to his arbitrary arrest in 2011-providing a window into the mind of one of the world's most electrifying and courageous contemporary artists. Ai Weiwei is one of China's most influential and inspiring figures. Artist, architect, curator, and activist, he has been an outspoken critic of the Chinese government's stance on human rights and democracy. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Toleration and Justice in the Laozi:Engaging with Tao Jiang's Origins of Moral-Political Philosophy in Early ChinaAi Yuan (bio)IntroductionThis review article engages with Tao Jiang's ground-breaking monograph on the Origins of Moral-Political Philosophy in Early China with particular focus on the articulation of toleration and justice in the Laozi (otherwise called the Daodejing).1 Jiang discusses a naturalistic turn and the re-alignment of values in the Laozi, resulting in (...) a naturalization of justice (impartiality) by rejecting artificial humaneness and rigid hierarchical moral-political structures. In this Laozian just world, there is no room for human intervention and rigid top-down enforcement of values, thus leaving justice to naturalized Heaven or Dao as the ultimate source of the cosmos.Based on Jiang's interpretative context, I show that there is a twofold justification for the "paradoxical" and "elusive"2 value of political toleration.3 A negative expression of toleration focuses on non-interference with choices, and non-enforcement of values toward those whom one reasonably disagrees with and regards as morally wrong. Such an attitude of toleration results in practical advantages such as avoiding entrenchment in bloodshed, abandonment, and deprivation.A positive expression of toleration operating in the Laozian ideal world requires equal protection of people as capable knowers, including those to whom we object. Such protection includes their ways of expression and an unbiased recognition of them as equally capable knowers so that they are heard without bias and prejudice. Such a kind of toleration is revealed as a constituent component of justice since it enables equal contribution toward the shaping of society without discrimination. In other words, toleration is seen as naturalized justice in a Laozian world within which no one is "being wronged with the capacity as a knower"—a kind of epistemic justice articulated by Miranda Fricker (Fricker 2007).4 [End Page 466]This article discusses how Jiang's political reconstruction of Laozian philosophy contains the seeds for a discussion of toleration, a value associated with distributive justice and against epistemic injustice. First, it introduces Jiang's arguments on a naturalization of justice and impartiality as Heavenly attributes in the Laozi. Second, it articulates the value of tolerance in the Laozi. Finally, I compare Laozian toleration with Confucian toleration using youwei- wuwei metaethical criticism.Moral-Political Philosophy in the LaoziJiang's interpretative framework about the origins of distributive justice in the Laozi starts with a discussion of the cosmogonic-mystical worldviews that signal "a new understanding of the nature of the cosmos, as well as a broad reorientation in the Heaven-human relationship in the mid-Warring States period" (p. 192). Differing from the interests in the origins of human culture or civilization in the Ru and Mo traditions, Jiang agrees with Franklin Perkins and identifies a "cosmogonic turn" in the late fourth century b.c. that reveals a demotion of anthropomorphic Heaven. By paring Heaven with earth and accordingly decentering human beings in the intellectual discourse, the Laozi directs equal values to the ten thousand things, with humans as just one among them (Perkins 2016, quoted in Jiang 2021, p. 196).5 Laozi's philosophy thus stands out by rejecting Heaven as caring for human affairs while taking Dao as the ultimate origin of the cosmos, as claimed in chapter 5 of the received text: "Heaven and earth are not humane; they treat ten-thousand things in the world as straw dogs; the sage is not humane; he treats people as straw dogs" (p. 199).With Jiang's reading of a naturalistic heaven, we see a re-alignment between cosmos and the human since sage-rulers follow the Heaven-Dao. The Laozian Dao is "self-so-ing" (ziran 自然), "accommodating" (rong 容), and "impartial" (gong 公), and thus marks a sharp contrast to Confucian familial bias resulting in "actively intervening in human affairs on behalf of the (certain) humans by appointing them to carry out its mission" (p. 200). With the sage emulating the ultimate Dao, the Laozi replaces the Confucian anthropocentric Heaven, which focuses on partiality (qin 親) and humaneness (ren 仁).This Laozian understanding of the cosmos also grounds its philosophical departure from Mohism despite both having a political... (shrink)

We suggest that as people move to construe robots as social agents, interact with them, and treat them as capable of social ties, they might develop (close) relationships with them. We then ask what kind of relationships can people form with bots, what functions can bots fulfill, and what are the societal and moral implications of such relationships.

Since some AI algorithms with high predictive power have impacted human integrity, safety has become a crucial challenge in adopting and deploying AI. Although it is impossible to prevent an algorithm from failing in complex tasks, it is crucial to ensure that it fails safely, especially if it is a critical system. Moreover, due to AI’s unbridled development, it is imperative to minimize the methodological gaps in these systems’ engineering. This paper uses the well-known Box-Jenkins method for statistical modeling as (...) a framework to identify engineering pitfalls in the adjustment and validation of AI models. Step by step, we point out state-of-the-art strategies and good practices to tackle these engineering drawbacks. In the final step, we integrate an internal and external validation scheme that might support an iterative evaluation of the normative, perceived, substantive, social, and environmental safety of all AI systems. (shrink)

Drawing upon the General Learning Model, the present study developed a moderated mediation model to provide an in-depth understanding of whether and how adolescents’ reading prosocial content in books predicts their prosocial behavior. The target population in this study is Chinese adolescents, and we adopted a paper-based survey to collect data. The age range of the sample was from 12 to 19. Among all participants, 49.3% were female, and 50.7% were male. PROCESS SPSS Macro was used to analyze the proposed (...) moderated mediation model. The results showed that prosocial content reading was positively associated with adolescents’ prosocial behavior. The positive association included a direct relationship and an indirect relationship through the mediation of moral identity. Furthermore, this study revealed the moderation effect of age on the relationships among prosocial content reading, moral identity, and prosocial behavior. Specifically, as age increases, the effects of prosocial content reading on moral identity and prosocial behavior attenuate, and the mediation effect of moral identity also decreases. The study adds to the body of knowledge on the prosocial media effect by extending it to book reading. (shrink)

Background Caring behaviour is critical for nursing quality, and the clinical internship environment is a crucial setting for preparing nursing students for caring behaviours. Evidence about how to develop nursing students’ caring behaviour in the clinical environment is still emerging. However, the mechanism between the clinical internship environment and caring behaviour remains unclear, especially the mediating role of moral sensitivity and the moderating effect of self-efficacy. Research objective This study aimed to examine the mediating effect of moral sensitivity (...) and the moderating function of self-efficacy on the association between the clinical internship environment and caring behaviours. Research design A cross-sectional design used acceptable validity scales. The hypothesised moderated mediation model was tested in the SPSS PROCESS macro. Participants and research context This survey collected data from 504 nursing students in an internship at a teaching hospital in Changsha, China. Ethical considerations This study was pre-approved by the ethics committee of the medical school (No. E2022210). Informed consent was obtained from all students. Results The clinical internship environment ( B = 0.450, 95% CI = [0.371, 0.530]) and moral sensitivity ( B = 1.352, 95% CI = [1.090, 1.615]) had positive direct effects on nursing students' caring behaviours. Clinical internship environment also indirectly influenced students’ caring behaviours via moral sensitivity ( B = 0.161, 95% CI = [0.115, 0.206]). In addition, self-efficacy played a moderating role between the clinical internship environment and caring behaviours ( B = 0.019, 95% CI = [0.007, 0.031]), as well as the relationship between the clinical internship environment and moral sensitivity ( B = 0.006, 95% CI = [0.003, 0.010]). Conclusion Moral sensitivity mediates the effect of the clinical internship environment on caring behaviour, and self-efficacy strengthens both direct and indirect effects. This study emphasises the importance of self-efficacy in developing moral sensitivity and caring behaviours in nursing students. (shrink)

Providing meaningful and actionable explanations for end-users is a situated problem requiring the intersection of multiple disciplines to address social, operational, and technical challenges. However, the explainable artificial intelligence community has not commonly adopted or created tangible design tools that allow interdisciplinary work to develop reliable AI-powered solutions. This paper proposes a formative architecture that defines the explanation space from a user-inspired perspective. The architecture comprises five intertwined components to outline explanation requirements for a task: (1) the end-users’ mental models, (...) (2) the end-users’ cognitive process, (3) the user interface, (4) the Human-Explainer Agent, and (5) the agent process. We first define each component of the architecture. Then, we present the Abstracted Explanation Space, a modeling tool that aggregates the architecture’s components to support designers in systematically aligning explanations with end-users’ work practices, needs, and goals. It guides the specifications of what needs to be explained (content: end-users’ mental model), why this explanation is necessary (context: end-users’ cognitive process), to delimit how to explain it (format: Human-Explainer Agent and user interface), and when the explanations should be given. We then exemplify the tool’s use in an ongoing case study in the aircraft maintenance domain. Finally, we discuss possible contributions of the tool, known limitations or areas for improvement, and future work to be done. (shrink)

Examining AI spirituality can illuminate problematic assumptions about human spirituality and AI cognition, suggest possible directions for AI development, reduce uncertainty about future AI, and yield a methodological lens sufficient to investigate human-AI sociotechnical interaction and morality. Incompatible philosophical assumptions about human spirituality and AI limit investigations of both and suggest a vast gulf between them. An emergentist approach can replace dualist assumptions about human spirituality and identify emergent behavior in AI computation to overcome overly reductionist assumptions about computation. Using (...) general systems theory to organize models of human experience yields insight into human morality and spirituality, upon which AI modeling can also draw. In this context, the pragmatist Josiah Royce’s semiotic philosophy of spirituality identifies unanticipated overlap between symbolic AI and spirituality and suggests criteria for a human-AI community focused on modeling morality that would result in an emergent Interpreter-Spirit sufficient to influence the ongoing development of human and AI morality and spirituality. (shrink)

The main objective of this article is to demonstrate why despite the growing interest in justifying AI’s trustworthiness, one can argue for AI’s reliability. By analyzing why trustworthiness ethics in Nickel’s sense provides some wellgrounded hints for rethinking the rational, affective and normative accounts of trust in respect to AI, I examine some concerns about the trustworthiness of Savulescu and Maslen’s model of moral AI as an adviser. Specifically, I tackle one of its exemplifications regarding Klincewicz’s hypothetical scenario of (...) John which is refracted through the lens of the HLEG’s fifth requirement of trustworthy artificial intelligence (TAI), namely, that of Diversity, non-discrimination and fairness. (shrink)

With the increasing degrees A.I.s and machines are applied across different social contexts, the need for implementing ethics in A.I.s is pressing. In this paper, we argue for a pluralist hybrid model for the implementation of moral A.I.s. We first survey current approaches to moral A.I.s and their inherent limitations. Then we propose the pluralist hybrid approach and show how these limitations of moral A.I.s can be partly alleviated by the pluralist hybrid approach. The core ethical decision-making (...) capacity of an A.I. based on the pluralist hybrid approach consists of two systems. The first is a deterministic algorithm system that incorporates different moral rules for making explicit moral decisions. The second is a machine learning system that calculates the value of the variables required by the application of moral principles. The pluralist hybrid system is better than the existing proposals as (i) it better addresses the moral disagreement problem of the top-down approach by including different moral principles, while (ii) it reduces the opacity of ethical decision-making compared with a bottom-up system by implementing explicit moral principles for moral decision making. (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)

Background Machine learning (ML) is utilized increasingly in health care, and can pose harms to patients, clinicians, health systems, and the public. In response, regulators have proposed an approach that would shift more responsibility to ML developers for mitigating potential harms. To be effective, this approach requires ML developers to recognize, accept, and act on responsibility for mitigating harms. However, little is known regarding the perspectives of developers themselves regarding their obligations to mitigate harms.Methods We conducted 40 semi-structured interviews with (...) developers of ML predictive analytics applications for health care in the United States.Results Participants varied widely in their perspectives on personal responsibility and included examples of both moral engagement and disengagement, albeit in a variety of forms. While most (70%) of participants made a statement indicative of moral engagement, most of these statements reflected an awareness of moral issues, while only a subset of these included additional elements of engagement such as recognizing responsibility, alignment with personal values, addressing conflicts of interests, and opportunities for action. Further, we identified eight distinct categories of moral disengagement reflecting efforts to minimize potential harms or deflect personal responsibility for preventing or mitigating harms.Conclusions These findings suggest possible facilitators and barriers to the development of ethical ML that could act by encouraging moral engagement or discouraging moral disengagement. Regulatory approaches that depend on the ability of ML developers to recognize, accept, and act on responsibility for mitigating harms might have limited success without education and guidance for ML developers about the extent of their responsibilities and how to implement them. (shrink)

Is it OK to lie to Siri? Is it bad to mistreat a robot for our own pleasure? Under what condition should we grant a moral status to an artificial intelligence (AI) system? This paper looks at different arguments for granting moral status to an AI system: the idea of indirect duties, the relational argument, the argument from intelligence, the arguments from life and information, and the argument from sentience. In each but the last case, we find unresolved (...) issues with the particular argument, which leads us to move to a different one. We leave the idea of indirect duties aside since these duties do not imply considering an AI system for its own sake. The paper rejects the relational argument and the argument from intelligence. The argument from life may lead us to grant a moral status to an AI system, but only in a weak sense. Sentience, by contrast, is a strong argument for the moral status of an AI system—based, among other things, on the Aristotelian principle of equality: that same cases should be treated in the same way. The paper points out, however, that no AI system is sentient given the current level of technological development. (shrink)

This chapter evaluates whether AI systems are or will be rights-holders, explaining the conditions under which people should recognize AI systems as rights-holders. It develops a skeptical stance toward the idea that current forms of artificial intelligence are holders of moral rights, beginning with an articulation of one of the most prominent and most plausible theories of moral rights: the Interest Theory of rights. On the Interest Theory, AI systems will be rights-holders only if they have interests or (...) a well-being. Current AI systems are not bearers of well-being, and so fail to meet the necessary condition for being rights-holders. This argument is robust against a range of different objections. However, the chapter also shows why difficulties in assessing whether future AI systems might have interests or be bearers of well-being—and so be rights-holders—raise difficult ethical challenges for certain developments in AI. (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)

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)

Even though morally competent artificial agents have yet to emerge in society, we need insights from empirical science into how people will respond to such agents and how these responses should inform agent design. Three survey studies presented participants with an artificial intelligence agent, an autonomous drone, or a human drone pilot facing a moral dilemma in a military context: to either launch a missile strike on a terrorist compound but risk the life of a child, or to cancel (...) the strike to protect the child but risk a terrorist attack. Seventy-two percent of respondents were comfortable making moral judgments about the AI in this scenario and fifty-one percent were comfortable making moral judgments about the autonomous drone. These participants applied the same norms to the two artificial agents and the human drone pilot. However, people ascribed different patterns of blame to humans and machines as a function of the agent’s decision of how to solve the dilemma. These differences in blame seem to stem from different assumptions about the agents’ embeddedness in social structures and the moral justifications those structures afford. Specifically, people less readily see artificial agents as embedded in social structures and, as a result, they explained and justified their actions differently. As artificial agents will perform many actions with moral significance, we must heed such differences in justifications and blame and probe how they affect our interactions with those agents. (shrink)

AI systems are bringing an augmentation of human capabilities to shape the world. They may also drag a replacement of human conscience in large chunks of life. AI systems can be designed to leave moral control in human hands, to obstruct or diminish that moral control, or even to prevent it, replacing human morality with pre-packaged or developed ‘solutions’ by the ‘intelligent’ machine itself. Artificial Intelligent systems (AIS) are increasingly being used in multiple applications and receiving more attention (...) from the public and private organisations. The purpose of this article is to offer a mapping of the technological architectures that support AIS, under the specific focus of the moral agency. Through a literature research and reflection process, the following areas are covered: a brief introduction and review of the literature on the topic of moral agency; an analysis using the BDI logic model (Bratman 1987); an elemental review of artificial ‘reasoning’ architectures in AIS; the influence of the data input and the data quality; AI systems’ positioning in decision support and decision making scenarios; and finally, some conclusions are offered about regarding the potential loss of moral control by humans due to AIS. This article contributes to the field of Ethics and Artificial Intelligence by providing a discussion for developers and researchers to understand how and under what circumstances the ‘human subject’ may, totally or partially, lose moral control and ownership over AI technologies. The topic is relevant because AIS often are not single machines but complex networks of machines that feed information and decisions into each other and to human operators. The detailed traceability of input-process-output at each node of the network is essential for it to remain within the field of moral agency. Moral agency is then at the basis of our system of legal responsibility, and social approval is unlikely to be obtained for entrusting important functions to complex systems under which no moral agency can be identified. (shrink)

Detailed summary in vernacular field only.

Cohen’s (2023) mapping exercise of possible bioethical issues emerging from the use of ChatGPT in medicine provides an informative, useful, and thought-provoking trigger for discussions of AI ethic...

Practical wisdom enables moral decision-making and action by aligning one’s apprehension of proximate goods with a distal, socially embedded interpretation of a more ultimate Good. A focus on purpose within the overall process mutually informs human moral psychology and moral AI development in their examinations of practical wisdom. AI practical wisdom could ground an AI system’s apprehension of reality in a sociotechnical moral process committed to orienting AI development and action in light of a pluralistic, diverse (...) interpretation of that Good. Defining AI purpose in terms of the human struggle to determine the moral “ought” (versus the way reality is) can overcome practical and normative limitations of principle-based approaches to AI ethics without requiring idiosyncratic moral reasoning or a premature commitment to a single ethical theory, which could hinder navigating the moral implications of future unknowns. Dividing practical wisdom into proximate, mediating, and distal processes enables each division to incorporate the moral dimensions of the situation, individual AI system, and sociotechnical community, respectively. Drawing upon externalist (4E) and ecological cognitive psychology theories to extend Xavier Zubiri’s sentient intelligence, proximate apprehension of what an object affords the perceiver includes affordances for practical and moral purposes. Those apprehensions are structured by moral and other social-cognitive schemas that mediate moral constructs, the apprehended situation, and individual commitments and other motivating identifications in a process Ignacio Ellacuria calls historical reality. Distally, Josiah Royce’s community of interpretation characterizes one way of organizing a sociotechnical process to synthesize diverse interpretations of the Good, orient AI moral practice, and align AI development with human and AI moral purpose. (shrink)

With AI permeating our lives, there is widespread concern regarding the proper framework needed to morally assess and regulate it. This has given rise to many attempts to devise ethical guidelines that infuse guidance for both AI development and deployment. Our main concern is that, instead of a genuine ethical interest for AI, we are witnessing moral diplomacies resulting in moral bureaucracies battling for moral supremacy and political domination. After providing a short overview of what we term (...) ‘ethics washing’ in the AI industry, we analyze the 2021 UNESCO Intergovernmental Meeting of Experts (Category II) tasked with drafting the Recommendation on the Ethics of Artificial Intelligence and show why the term ‘moral diplomacy’ is better suited to explain what is happening in the field of the ethics of AI. Our paper ends with some general considerations regarding the future of the ethics of AI. (shrink)

This paper investigates how the introduction of AI to decision making increases moral distance and recommends the ethics of care to augment the ethical examination of AI decision making. With AI decision making, face-to-face interactions are minimized, and decisions are part of a more opaque process that humans do not always understand. Within decision-making research, the concept of moral distance is used to explain why individuals behave unethically towards those who are not seen. Moral distance abstracts those (...) who are impacted by the decision and leads to less ethical decisions. The goal of this paper is to identify and analyze the moral distance created by AI through both proximity distance (in space, time, and culture) and bureaucratic distance (derived from hierarchy, complex processes, and principlism). We then propose the ethics of care as a moral framework to analyze the moral implications of AI. The ethics of care brings to the forefront circumstances and context, interdependence, and vulnerability in analyzing algorithmic decision making. (shrink)

Several proposals for moral enhancement would use AI to augment (auxiliary enhancement) or even supplant (exhaustive enhancement) human moral reasoning or judgment. Exhaustive enhancement proposals conceive AI as some self-contained oracle whose superiority to our own moral abilities is manifest in its ability to reliably deliver the ‘right’ answers to all our moral problems. We think this is a mistaken way to frame the project, as it presumes that we already know many things that we are (...) still in the process of working out, and reflecting on this fact reveals challenges even for auxiliary proposals that eschew the oracular approach. We argue there is nonetheless a substantial role that ‘AI mentors’ could play in our moral education and training. Expanding on the idea of an AI Socratic Interlocutor, we propose a modular system of multiple AI interlocutors with their own distinct points of view reflecting their training in a diversity of concrete wisdom traditions. This approach minimizes any risk of moral disengagement, while the existence of multiple modules from a diversity of traditions ensures pluralism is preserved. We conclude with reflections on how all this relates to the broader notion of moral transcendence implicated in the project of AI moral enhancement, contending it is precisely the whole concrete socio-technical system of moral engagement that we need to model if we are to pursue moral enhancement. (shrink)

이 논문은 인공지능이 도덕적 행위자로서 간주될 수 있는지의 문제와 관련된 쟁점을 ‘쉬운 문제’와 ‘어려운 문제’로 나누어 접근하고 그 해결책을 모색한다. 이 두 문제의 성격을 분명히 드러내기 위해 자율주행차의 사례를 분석한다. 그리고 ‘쉬운 문제’의 해결에는 기존 윤리적 개념의 확장이 중요하다는 점을 법인 개념의 탄생 과정과 변화 과정을 통해 설명한다. 또한 ‘어려운 문제’의 해결은 결국 ‘쉬운 문제’의 해결이 축적되는 과정에서 구성적으로 얻어질 것이라고 주장한다. 마지막으로 이 두 문제 해결과정이 서로 본질적으로 연결되어 있기에 논문이 제시하는 방식의 해결이 불가능하다는 반론을 소개하고, 이 반론이 해결책을 (...) 찾는 과정을 구성적, 사회적 과정으로 보지 않고 순간적, 원리적 과정으로 보기 때문이라고 답변한다. (shrink)

In this article, our aim is to show why increasing the effectiveness of detecting doping fraud in sport by the use of artificial intelligence (AI) may be morally wrong. The first argument in favour of this conclusion is that using AI to make a non-ideal antidoping policy even more effective can be morally wrong. Whether the increased effectiveness is morally wrong depends on whether you believe that the current antidoping system administrated by the World Anti-Doping Agency is already morally wrong. (...) The second argument is based on the possibility of scenarios in which a more effective AI system may be morally worse than a less effective but non-AI system. We cannot, of course, conclude that the increased effectiveness of doping detection is always morally wrong. But our point is that whether the introduction of AI to increase detection of doping fraud is a moral improvement depends on the moral plausibility of the current system and the distribution of harm that will follow from false positive and false negative errors. (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)

이 논문은 도덕적 기계의 가능성을 정초하는 첫 단추로, 기계의 도덕적 지위를 적극 옹호하고 있는 윤리적 행동주의를 비판적으로 천착하는 데 그 목적이 있다. 이를 위해 필자는 먼저 기계의 도덕적 지위를 부인하는 표준적 입장에 대한 대안으로 기능주의 입장을, 특히 존 다나허의 윤리적 행동주의의 입장을 해명할 것이다. 윤리적 행동주의는 도덕적 지위를 근거짓는 속성의 동등성이 아니라, 관찰 가능한 행동의 동등성을 토대로 비교와 유비추론의 방법을 통해 도덕적 지위를 추론할 수 있다고 주장한다. 이러한 접근법은 탈형이상학적이고 또 인간중심적이라는 편견에서 벗어난다는 장점을 지니지만, 행동 패턴의 동등성에서 도덕적 지위를 (...) 추론하는 방법론으로 인해 근본적인 두 물음을 낳는다. 하나는 왜 행동의 동등성이냐의 물음이고, 다른 하나는 어떤 종류의 행동인가의 물음이다. 윤리적 행동주의는 첫 번째 물음에 대해서는 인식론적 논변을 통해 합당한 설명을 할 수 있지만, 두 번째 물음에 대해서는 한계를 지닐 수밖에 없다. 즉, 윤리적 행동주의는 비교와 유비가 존재론적 동질성을 전제한다는 사실로 인해, 원래 의도와 달리 두 번째 물음에 합당하게 답하자면, 도덕적 지위를 근거 짓는 속성에 대해 중립적일 수 없게 된다. 이처럼 기계의 도덕적 지위 물음에 대해서는 윤리적 행동주의가 한계를 지니지만, 기계 행동 자체의 윤리를 정초 짓는 접근법으로 우리는 얼마든지 이를 수정하여 발전시켜나갈 수 있다. (shrink)

We are moving towards a future where Artificial Intelligence (AI) based agents make many decisions on behalf of humans. From healthcare decision-making to social media censoring, these agents face problems, and make decisions with ethical and societal implications. Ethical behaviour is a critical characteristic that we would like in a human-centric AI. A common observation in human-centric industries, like the service industry and healthcare, is that their professionals tend to break rules, if necessary, for pro-social reasons. This behaviour among humans (...) is defined as pro-social rule breaking. To make AI agents more human-centric, we argue that there is a need for a mechanism that helps AI agents identify when to break rules set by their designers. To understand when AI agents need to break rules, we examine the conditions under which humans break rules for pro-social reasons. In this paper, we present a study that introduces a ‘vaccination strategy dilemma’ to human participants and analyzes their response. In this dilemma, one needs to decide whether they would distribute COVID-19 vaccines only to members of a high-risk group (follow the enforced rule) or, in selected cases, administer the vaccine to a few social influencers (break the rule), which might yield an overall greater benefit to society. The results of the empirical study suggest a relationship between stakeholder utilities and pro-social rule breaking (PSRB), which neither deontological nor utilitarian ethics completely explain. Finally, the paper discusses the design characteristics of an ethical agent capable of PSRB and the future research directions on PSRB in the AI realm. We hope that this will inform the design of future AI agents, and their decision-making behaviour. (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)

Is it OK to lie to Siri? Is it bad to mistreat a robot for our own pleasure? Under what condition should we grant a moral status to an artificial intelligence system? This paper looks at different arguments for granting moral status to an AI system: the idea of indirect duties, the relational argument, the argument from intelligence, the arguments from life and information, and the argument from sentience. In each but the last case, we find unresolved issues (...) with the particular argument, which leads us to move to a different one. We leave the idea of indirect duties aside since these duties do not imply considering an AI system for its own sake. The paper rejects the relational argument and the argument from intelligence. The argument from life may lead us to grant a moral status to an AI system, but only in a weak sense. Sentience, by contrast, is a strong argument for the moral status of an AI system—based, among other things, on the Aristotelian principle of equality: that same cases should be treated in the same way. The paper points out, however, that no AI system is sentient given the current level of technological development. (shrink)

The development of machine learning and deep learning (DL) in the field of AI (artificial intelligence) is the direct result of the advancement of nano-electronics. Machine learning is a function that provides the system with the capacity to learn from data without being programmed explicitly. It is basically a mathematical and probabilistic model. DL is part of machine learning methods based on artificial neural networks, simply called neural networks (NNs), as they are inspired by the biological NNs that constitute organic (...) brains. Despite its similarity to biological organs such as human brains, major problems arise in trying to attribute moral responsibility to autonomic systems based on hardware including nano-electronic devices, which are sought to replace humans (moral agents) in the context of AI. It is suggested that the required emotional environment which enables actions according to reasons in humans is not witnessed at AI devices. Though AI technology raises an enticing resemblance to human actions, this resemblance is to be considered with a skeptical eye. It is because actions are associated with reasons while causes are connected to operations. Human agents are capable of acting upon their reasons, while AI devices are limited only to operations as they are conditioned by their programming, which is considered as an embodiment of some causes. As moral responsibility goes hand in hand with the capacity to act (according to reasons), attributing moral responsibility to AI devices is revealed to be but a misleading metaphor. (shrink)

Many social trends are conspiring to drive the adoption of greater automation in society, and we will certainly see a greater offloading of human decisionmaking to robots in the future. Many of these decisions are morally salient, including decisions about how benefits and burdens are distributed. Roboticists and ethicists have begun to think carefully about the moral decision making apparatus for machines. Their concerns often center around the plausible claim that robots will lack many of the mental capacities that (...) are indispensable in human moral decision making, such as empathy. To the extent that robots may be robustly artificially intelligent, these concerns subside, but they give way to new worries about creating artificial agents to do our bidding, if those artificial agents have moral standing. We suggest that the question of AI consciousness poses a dilemma. Whether artificially intelligent agents will be conscious or not, we will face serious difficulties in programming them to reliably make moral decisions. (shrink)

Meier et al. describe a pilot study that developed METHAD, an AI-based Medical Ethics Advisor tool that draws on the principlism approach and was tested using text-book cases and clinical et...

Many social trends are conspiring to drive the adoption of greater automation in society, and we will certainly see a greater offloading of human decisionmaking to robots in the future. Many of these decisions are morally salient, including decisions about how benefits and burdens are distributed. Roboticists and ethicists have begun to think carefully about the moral decision making apparatus for machines. Their concerns often center around the plausible claim that robots will lack many of the mental capacities that (...) are indispensable in human moral decision making, such as empathy. To the extent that robots may be robustly artificially intelligent, these concerns subside, but they give way to new worries about creating artificial agents to do our bidding, if those artificial agents have moral standing. We suggest that the question of AI consciousness poses a dilemma. Whether artificially intelligent agents will be conscious or not, we will face serious difficulties in programming them to reliably make moral decisions. (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)

One of the main difficulties in assessing artificial intelligence (AI) is the tendency for people to anthropomorphise it. This becomes particularly problematic when we attach human moral activities to AI. For example, the European Commission’s High-level Expert Group on AI (HLEG) have adopted the position that we should establish a relationship of trust with AI and should cultivate trustworthy AI (HLEG AI Ethics guidelines for trustworthy AI, 2019, p. 35). Trust is one of the most important and defining activities (...) in human relationships, so proposing that AI should be trusted, is a very serious claim. This paper will show that AI cannot be something that has the capacity to be trusted according to the most prevalent definitions of trust because it does not possess emotive states or can be held responsible for their actions—requirements of the affective and normative accounts of trust. While AI meets all of the requirements of the rational account of trust, it will be shown that this is not actually a type of trust at all, but is instead, a form of reliance. Ultimately, even complex machines such as AI should not be viewed as trustworthy as this undermines the value of interpersonal trust, anthropomorphises AI, and diverts responsibility from those developing and using them. (shrink)

Our ability to locate moral responsibility is often thought to be a necessary condition for conducting morally permissible medical practice, engaging in a just war, and other high-stakes endeavors. Yet, with increasing reliance upon artificially intelligent systems, we may be facing a wideningresponsibility gap, which, some argue, cannot be bridged by traditional concepts of responsibility. How then, if at all, can we make use of crucial emerging technologies? According to Colin Allen and Wendell Wallach, the advent of so-called ‘artificial (...) moral agents’ (AMAs) is inevitable. Still, this notion may seem to push back the problem, leaving those who have an interest in developing autonomous technology with a dilemma. We may need to scale-back our efforts at deploying AMAs (or at least maintain human oversight); otherwise, we must rapidly and drastically update our moral and legal norms in a way that ensures responsibility for potentially avoidable harms. This paper invokes contemporary accounts of responsibility in order to show how artificially intelligent systems might be held responsible. Although many theorists are concerned enough to develop artificial conceptions ofagencyor to exploit our present inability to regulate valuable innovations, the proposal here highlights the importance of—and outlines a plausible foundation for—a workable notion ofartificial moral responsibility. (shrink)

Under what conditions would an artificially intelligent system have wellbeing? Despite its obvious bearing on the ethics of human interactions with artificial systems, this question has received little attention. Because all major theories of wellbeing hold that an individual’s welfare level is partially determined by their mental life, we begin by considering whether artificial systems have mental states. We show that a wide range of theories of mental states, when combined with leading theories of wellbeing, predict that certain existing artificial (...) systems have wellbeing. While we do not claim to demonstrate conclusively that AI systems have wellbeing, we argue that our metaphysical and moral uncertainty about AI wellbeing requires us dramatically to reassess our relationship with the intelligent systems we create. (shrink)

The prevailing discourse around AI ethics lacks the language and formalism necessary to capture the diverse ethical concerns that emerge when AI systems interact with individuals. Drawing on Sen and Nussbaum's capability approach, we present a framework formalizing a network of ethical concepts and entitlements necessary for AI systems to confer meaningful benefit or assistance to stakeholders. Such systems enhance stakeholders' ability to advance their life plans and well-being while upholding their fundamental rights. We characterize two necessary conditions for morally (...) permissible interactions between AI systems and those impacted by their functioning, and two sufficient conditions for realizing the ideal of meaningful benefit. We then contrast this ideal with several salient failure modes, namely, forms of social interactions that constitute unjustified paternalism, coercion, deception, exploitation and domination. The proliferation of incidents involving AI in high-stakes domains underscores the gravity of these issues and the imperative to take an ethics-led approach to AI systems from their inception. (shrink)