Cohen (2023), Rahimzadeh and colleagues (2023), and Porsdam Mann and colleagues (2023) have written thorough and well-canvassed pieces about the ethical and conceptual challenges of large language...

Artificial Intelligence is playing an increasingly prevalent role in our lives. Whether its landing a job interview, getting a bank loan or accessing a government program, organizations are using automated systems informed by AI enabled technologies in ways that have significant consequences for people. At the same time, there is a lack of transparency around how AI technologies work and whether they are ethical, fair or accurate. This paper examines a body of literature related to the ethical considerations surrounding the (...) use of artificial intelligence and the role of ethical codes. It identifies and explores core issues including bias, fairness and transparency and looks at who is setting the agenda for AI ethics in Canada and globally. Lastly, it offers some suggestions for next steps towards a more inclusive discussion. (shrink)

The relationship between a human being and an AI system has to be considered as a collaborative process between two agents during the performance of an activity. When there is a collaboration between two people, a fundamental characteristic of that collaboration is that there is co-supervision, with each agent supervising the actions of the other. Such supervision ensures that the activity achieves its objectives, but it also means that responsibility for the consequences of the activity is shared. If there is (...) no co-supervision, neither collaborator can be held co-responsible for the actions of the other. When the collaboration is between a person and an AI system, co-supervision is also necessary to ensure that the objectives of the activity are achieved, but this also means that there is co-responsibility for the consequences of the activities. Therefore, if each agent's responsibility for the consequences of the activity depends on the effectiveness and efficiency of the supervision that that agent performs over the other agent's actions, it will be necessary to take into account the way in which that supervision is carried out and the factors on which it depends. In the case of the human supervision of the actions of an AI system, there is a wealth of psychological research that can help us to establish cognitive and non-cognitive boundaries and their relationship to the responsibility of humans collaborating with AI systems. There is also psychological research on how an external observer supervises and evaluates human actions. This research can be used to programme AI systems in such a way that the boundaries of responsibility for AI systems can be established. In this article, we will describe some examples of how such research on the task of supervising the actions of another agent can be used to establish lines of shared responsibility between a human being and an AI system. The article will conclude by proposing that we should develop a methodology for assessing responsibility based on the results of the collaboration between a human being and an AI agent during the performance of one common activity. (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)

The increasing demand for transparency in AI has recently come under scrutiny. The question is often posted in terms of “epistemic double standards”, and whether the standards for transparency in AI ought to be higher than, or equivalent to, our standards for ordinary human reasoners. I agree that the push for increased transparency in AI deserves closer examination, and that comparing these standards to our standards of transparency for other opaque systems is an appropriate starting point. I suggest that a (...) more fruitful exploration of this question will involve a different comparison class. We routinely treat judgments made by highly trained experts in specialized fields as fair or well-grounded even though—by the nature of expert/layperson division of epistemic labor—an expert will not be able to provide an explanation of the reasoning behind these judgments that makes sense to most other people. Regardless, laypeople are thought to be acting reasonably—and ethically—in deferring to the judgments of experts that concern their areas of specialization. I suggest that we reframe our question regarding the appropriate standards of transparency in AI as one that asks when, why, and to what degree it would be ethical to accept opacity in AI. I argue that our epistemic relation to certain opaque AI technology may be relevantly similar to the layperson’s epistemic relation to the expert in certain respects, such that the successful expert/layperson division of epistemic labor can serve as a blueprint for the ethical use of opaque AI. (shrink)

Advances in artificial intelligence and robotics stand to change many aspects of our lives, including our values. If trends continue as expected, many industries will undergo automation in the near future, calling into question whether we can still value the sense of identity and security our occupations once provided us with. Likewise, the advent of social robots driven by AI, appears to be shifting the meaning of numerous, long-standing values associated with interpersonal relationships, like friendship. Furthermore, powerful actors’ and institutions’ (...) increasing reliance on AI to make decisions that may affect how people live their lives may have a significant impact on privacy while also raising issues about algorithmic transparency and human control. In this paper, building and expanding on previous works, we will look at how the deployment of Artificial Intelligence technology may lead to changes in identity, security, and other crucial values (such as friendship, fairness, and privacy). We will discuss what challenges we may face in the process, while critically reflecting on whether such changes may be desirable. Finally, drawing on a series of considerations underlying virtue ethics, we will formulate a set of preliminary suggestions, which—we hope—can be used to more carefully guide the future roll out of AI technologies for human flourishing; that is, for social and moral good. (shrink)

With the growing influence of artificial intelligence (AI) in our lives, the ethical implications of AI have received attention from various communities. Building on previous work on trust in people and technology, we advance a multidimensional, multilevel conceptualization of trust in AI and examine the relationship between trust and ethics using the data from a survey of a national sample in the U.S. This paper offers two key dimensions of trust in AI—human-like trust and functionality trust—and presents a multilevel (...) conceptualization of trust with dispositional, institutional, and experiential trust each significantly correlated with trust dimensions. Along with trust in AI, we examine perceptions of the importance of seven ethics requirements of AI offered by the European Commission’s High-Level Expert Group. Then the association between ethics requirements and trust is evaluated through regression analysis. Findings suggest that the ethical requirement of societal and environmental well-being is positively associated with human-like trust in AI. Accountability and technical robustness are two other ethical requirements, which are significantly associated with functionality trust in AI. Further, trust in AI was observed to be higher than trust in other institutions. Drawing from our findings, we offer a multidimensional framework of trust that is inspired by ethical values to ensure the acceptance of AI as a trustworthy technology. (shrink)

Purpose Digital transformation leverages digital technologies to change current processes and introduce new processes in any organisation’s business model, customer/user experience and operational processes. Artificial intelligence plays a significant role in achieving DT. As DT is touching each sphere of humanity, AI led DT is raising many fundamental questions. These questions raise concerns for the systems deployed, how they should behave, what risks they carry, the monitoring and evaluation control we have in hand, etc. These issues call for the need (...) to integrate ethics in AI led DT. The purpose of this study is to develop an “AI led ethical digital transformation framework”. Design/methodology/approach Based on the literature survey, various existing business ethics decision-making models were synthesised. The authors mapped essential characteristics such as intensity and the individual, organisational and opportunity factors of ethics models with the proposed AI led ethical DT. The DT framework is evaluated using a thematic analysis of 23 expert interviews with relevant AI ethics personas from industry and society. The qualitative data of the interviews and opinion data has been analysed using MAXQDA software. Findings The authors have explored how AI can drive the ethical DT framework and have identified the core constituents of developing an AI led ethical DT framework. Backed by established ethical theories, the paper presents how DT pillars are related and sequenced to ethical factors. This research provides the potential to examine theoretically sequenced ethical factors with practical DT pillars. Originality/value The study establishes deduced and induced ethical value codes based on thematic analysis to develop guidelines for the pursuit of ethical DT. The authors identify four unique induced themes, namely, corporate social responsibility, perceived value, standard benchmarking and learning willingness. The comprehensive findings of this research, supported by a robust theoretical background, have substantial implications for academic research and corporate applicability. The proposed AI led ethical DT framework is unique and can be used for integrated social, technological and economic ethical research. (shrink)

The focus of this chapter is on some of the ethical and philosophical issues at the intersection of robotics and artificial intelligence (AI) applications in the health care sector and medical assistance in dying (e.g. physician-assisted suicide and euthanasia), including: (1) Is there a role for robotic systems/AI to play in the orchestration or delivery of assisted dying?; (2) Can the use of robotic systems/AI make the orchestration of assisted dying more ethical?; and (3) What insights can be generated in (...) the ethical debate on physician assisted suicide and euthanasia from considering the prospect of robotic systems/AI assisting with the provision of or providing assistance in dying? The prospect of including robotic systems/AI in the context of assisted dying provides opportunity to revisit longstanding philosophical and ethical issues under new light. Indeed, reflecting on these questions may invigorate debate, for example in reconsidering the de-medicalization of assisted dying, reconsidering whether assisted dying is within the proper scope of medicine, and reconsidering which normative approach to the ethics of assisted dying is the most appropriate. (shrink)

The revolution of artificial intelligence (AI), particularly generative AI, and its implications for human–robot interaction (HRI) opened up the debate on crucial regulatory, business, societal, and ethical considerations. This paper explores essential issues from the anthropomorphic perspective, examining the complex interplay between humans and AI models in societal and corporate contexts. We provided a comprehensive review of existing literature on HRI, with a special emphasis on the impact of generative models such as ChatGPT. The scientometric study posits that due to (...) their advanced linguistic capabilities and ability to mimic human-like behavior, generative AIs like ChatGPT will continue to grow in popularity in pair with human rational empathy, tendency for personification and their advanced linguistic capabilities and ability to mimic human-like behavior. As they blur the boundaries between humans and robots, these models introduce fresh moral and philosophical dilemmas. Our research aims to extrapolate key trends and unique factors in HRI and to elucidate the technical aspects of generative AI that enhance its effectiveness in this field compared to traditional rule-based AI systems. We further discuss the challenges and limitations of applying generative AI in HRI, providing a future research agenda for AI optimization in diverse applications, including education, entertainment, and healthcare. (shrink)

This paper will examine the social and ethical impacts of using artificial intelligence (AI) in the agricultural sector. It will identify what are some of the most prevalent challenges and impacts identified in the literature, how this correlates with those discussed in the domain of AI ethics, and are being implemented into AI ethics guidelines. This will be achieved by examining published articles and conference proceedings that focus on societal or ethical impacts of AI in the agri-food sector, (...) through a thematic analysis of the literature. The thematic analysis will be divided based on the classifications outlined through 11 overarching principles, from an established lexicon (transparency, justice and fairness, non-maleficence, responsibility, privacy, beneficence, freedom and autonomy, trust, dignity, sustainability, and solidarity). While research on AI agriculture is still relatively new, this paper aims to map the debate and illustrate what the literature says in the context of social and ethical impacts. It aim is to analyse these impacts, based on these 11 principles. This research will contrast which impacts are not being discussed in agricultural AI and which issues are not being discussed in AI ethics guidelines, but which are discussed in relation to agricultural AI. The aim of this is to identify gaps within the agricultural literature, and gaps in AI ethics guidelines, that may need to be addressed. (shrink)

Since their public launch, a little over a year ago, large language models (LLMs) have inspired a flurry of analysis about what their implications might be for medical ethics, and for society more broadly. 1 Much of the recent debate has moved beyond categorical evaluations of the permissibility or impermissibility of LLM use in different general contexts (eg, at work or school), to more fine-grained discussions of the criteria that should govern their appropriate use in specific domains or towards (...) certain ends. 2 With each passing week, it seems more and more inevitable that LLMs will be a pervasive feature of many, if not most, of our lives. It would not be possible—and would not be desirable—to prohibit them across the board. We need to learn how to live with LLMs; to identify and mitigate the risks they pose to us, to our fellow creatures, and the... (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)

While artificial intelligence (AI) is increasingly applied for decision-making processes, ethical decisions pose challenges for AI applications. Given that humans cannot always agree on the right thing to do, how would ethical decision-making by AI systems be perceived and how would responsibility be ascribed in human-AI collaboration? In this study, we investigate how the expert type (human vs. AI) and level of expert autonomy (adviser vs. decider) influence trust, perceived responsibility, and reliance. We find that participants consider humans to be (...) more morally trustworthy but less capable than their AI equivalent. This shows in participants’ reliance on AI: AI recommendations and decisions are accepted more often than the human expert’s. However, AI team experts are perceived to be less responsible than humans, while programmers and sellers of AI systems are deemed partially responsible instead. (shrink)

Generative AI enables automated, effective manipulation at scale. Despite the growing general ethical discussion around generative AI, the specific manipulation risks remain inadequately investigated. This article outlines essential inquiries encompassing conceptual, empirical, and design dimensions of manipulation, pivotal for comprehending and curbing manipulation risks. By highlighting these questions, the article underscores the necessity of an appropriate conceptualisation of manipulation to ensure the responsible development of Generative AI technologies.

Bioethics, Volume 36, Issue 2, Page 194-200, February 2022.

This paper demonstrates that with increased philosophical scrutiny regarding the absurdity of life and meaningful living, the development of “Emotionalized AI” falls under ethical considerations. In this context, contemporary philosophical discussions on the meaning of life—as articulated by thinkers like Thomas Nagel, Joshua Seachris, Thaddeus Metz, and Susan Wolf—intersect with recent years’ reviews of AI ethics, particularly those related to meaningful relationships. The main analysis is conducted by explaining a philosophical perspective on meaningful life and revisiting AI ethics (...) reviews based on it while accepting a superficial understanding of “Emotionalized AI” to avoid metaphysical disputes. In this regard, the questions raised about AI ethics, considering the specialized literature in recent years, are first addressed. Similarly, the discussion focuses on a specific type of ethical concern regarding AI development: human relationships. It is then explained how “love” and, more generally, emotional relationships can be an important resource of life’s meaningfulness. Subsequently, an argument is presented showing that the development of emotionalized AI, by limiting our access to “emotional relationships,” poses a threat to life’s meaningfulness and is therefore ethically unjustifiable. To reinforce this view, I also use some near-reality dystopian scenarios for the AI era. Finally, I re-evaluate the conclusion based on a four-step process for assessing the meaningfulness gap created by emotionalized AI. Applying this process also confirms the result. (shrink)

Nurses have traditionally been regarded as clinicians that deliver compassionate, safe, and empathetic health care (Nurses again outpace other professions for honesty & ethics, 2018). Caring is a fundamental characteristic, expectation, and moral obligation of the nursing and caregiving professions (Nursing: Scope and standards of practice, American Nurses Association, Silver Spring, MD, 2015). Along with caring, nurses are expected to undertake ever‐expanding duties and complex tasks. In part because of the growing physical, intellectual and emotional demandingness, of nursing as (...) well as technological advances, artificial intelligence (AI) and AI care robots are rapidly changing the healthcare landscape. As technology becomes more advanced, efficient, and economical, opportunities and pressure to introduce AI into nursing care will only increase. In the first part of the article, we review recent and existing applications of AI in nursing and speculate on future use. Second, situate our project within the recent literature on the ethics of nursing and AI. Third, we explore three dominant theories of caring and the two paradigmatic expressions of caring (touch and presence) and conclude that AI—at least for the foreseeable future—is incapable of caring in the sense central to nursing and caregiving ethics. We conclude that for AI to be implemented ethically, it cannot transgress the core values of nursing, usurp aspects of caring that can only meaningfully be carried out by human beings, and it must support, open, or improve opportunities for nurses to provide the uniquely human aspects of care. (shrink)

:This article considers recent ethical topics relating to medical AI. After a general discussion of recent medical AI innovations, and a more analytic look at related ethical issues such as data privacy, physician dependency on poorly understood AI helpware, bias in data used to create algorithms post-GDPR, and changes to the patient–physician relationship, the article examines the issue of so-called robot doctors. Whereas the so-called democratization of healthcare due to health wearables and increased access to medical information might suggest a (...) positive shift in the patient-physician relationship, the physician’s ‘need to care’ might be irreplaceable, and robot healthcare workers might be seen as contributing to dehumanized healthcare practices. (shrink)

BackgroundDespite the recognition that developing artificial intelligence (AI) that is trustworthy is necessary for public acceptability and the successful implementation of AI in healthcare contexts, perspectives from key stakeholders are often absent from discourse on the ethical design, development, and deployment of AI. This study explores the perspectives of birth parents and mothers on the introduction of AI-based cardiotocography (CTG) in the context of intrapartum care, focusing on issues pertaining to trust and trustworthiness.MethodsSeventeen semi-structured interviews were conducted with birth parents (...) and mothers based on a speculative case study. Interviewees were based in England and were pregnant and/or had given birth in the last two years. Thematic analysis was used to analyze transcribed interviews with the use of NVivo. Major recurring themes acted as the basis for identifying the values most important to this population group for evaluating the trustworthiness of AI.ResultsThree themes pertaining to the perceived trustworthiness of AI emerged from interviews: (1) trustworthy AI-developing institutions, (2) trustworthy data from which AI is built, and (3) trustworthy decisions made with the assistance of AI. We found that birth parents and mothers trusted public institutions over private companies to develop AI, that they evaluated the trustworthiness of data by how representative it is of all population groups, and that they perceived trustworthy decisions as being mediated by humans even when supported by AI.ConclusionsThe ethical values that underscore birth parents and mothers’ perceptions of trustworthy AI include fairness and reliability, as well as practices like patient-centered care, the promotion of publicly funded healthcare, holistic care, and personalized medicine. Ultimately, these are also the ethical values that people want to protect in the healthcare system. Therefore, trustworthy AI is best understood not as a list of design features but in relation to how it undermines or promotes the ethical values that matter most to its end users. An ethical commitment to these values when creating AI in healthcare contexts opens up new challenges and possibilities for the design and deployment of AI. (shrink)

The growing number of ‘smart’ instruments, those equipped with AI, has raised concerns because these instruments make autonomous decisions; that is, they act beyond the guidelines provided them by programmers. Hence, the question the makers and users of smart instrument face is how to ensure that these instruments will not engage in unethical conduct. The article suggests that to proceed we need a new kind of AI program—oversight programs—that will monitor, audit, and hold operational AI programs accountable.

Sovereignty and strategic autonomy are felt to be at risk today, being threatened by the forces of rising international tensions, disruptive digital transformations and explosive growth of cybersecurity incidents. The combination of AI and cybersecurity is at the sharp edge of this development and raises many ethical questions and dilemmas. In this commentary, I analyse how we can understand the ethics of AI and cybersecurity in relation to sovereignty and strategic autonomy. The analysis is followed by policy recommendations, some (...) of which may appear to be controversial, such as the strategic use of ethics. I conclude with a reflection on underlying concepts as an invitation for further research. The goal is to inspire policy-makers, academics and business strategists in their work, and to be an input for public debate. (shrink)

This open access book explains how leading business organizations attempt to achieve the responsible and ethical use of artificial intelligence (AI) and other advanced information technologies. These technologies can produce tremendous insights and benefits. But they can also invade privacy, perpetuate bias, and otherwise injure people and society. To use these technologies successfully, organizations need to implement them responsibly and ethically. The question is: how to do this? Data ethics management, and this book, provide some answers. -/- The authors (...) interviewed and surveyed data ethics managers at leading companies. They asked why these experts see data ethics as important and how they seek to achieve it. This book conveys the results of that research on a concise, accessible way. -/- Much of the existing writing on data and AI ethics focuses either on macro-level ethical principles, or on micro-level product design and tooling. The interviews showed that companies need a third component: data ethics management. This third element consists of the management structures, processes, training and substantive benchmarks that companies use to operationalize their high-level ethical principles and to guide and hold accountable their developers. Data ethics management is the connective tissue makes ethical principles real. It is the focus of this book. -/- This book should be of use to organizations that wish to improve their own data ethics management efforts, legislators and policymakers who hope to build on existing management practices, scholars who study beyond compliance business behavior, and members of the public who want to understand better the threats that AI poses and how to reduce them. (shrink)

Current national and international guidelines for the ethical design and development of artificial intelligence (AI) and robotics emphasize ethical theory. Various governing and advisory bodies have generated sets of broad ethical principles, which institutional decisionmakers are encouraged to apply to particular practical decisions. Although much of this literature examines the ethics of designing and developing AI and robotics, medical institutions typically must make purchase and deployment decisions about technologies that have already been designed and developed. The primary problem facing (...) medical institutions is not one of ethical design but of ethical deployment. The purpose of this paper is to develop a practical model by which medical institutions may make ethical deployment decisions about ready-made advanced technologies. Our slogan is “more process, less principles.” Ethically sound decisionmaking requires that the process by which medical institutions make such decisions include participatory, deliberative, and conservative elements. We argue that our model preserves the strengths of existing frameworks, avoids their shortcomings, and delivers its own moral, practical, and epistemic advantages. (shrink)

Machine learning (ML) trains itself by discovering patterns of correlations that can be applied to new inputs. That is a very powerful form of generalization, but it is also very different from the sort of generalization that the west has valorized as the highest form of truth, such as universal laws in some of the sciences, or ethical principles and frameworks in moral reasoning. Machine learning’s generalizations synthesize the general and the particular in a new way, creating a multidimensional model (...) that often retains more of the complex differentiating patterns it has uncovered in the training process than the human mind can grasp. Particulars speak louder in these models than they do in traditional generalizing frameworks. This creates an odd analogy with recent movements in moral philosophy, particularly the feminist ethics of care which rejects the application of general moral frameworks in favor of caring responses to the particular needs and interests of those affected by a moral decision. This paper suggests that our current wide-spread and justified worries about ML’s inexplicability—primarily arising from its reliance on staggeringly complex patterns of particulars—may be preparing our culture more broadly for a valorizing of particulars as at least as determinative as generalizations, and that this might help further advance the importance of particulars in ideas such as those put forward by the ethics of care. (shrink)

The use of black box algorithms in medicine has raised scholarly concerns due to their opaqueness and lack of trustworthiness. Concerns about potential bias, accountability and responsibility, patient autonomy and compromised trust transpire with black box algorithms. These worries connect epistemic concerns with normative issues. In this paper, we outline that black box algorithms are less problematic for epistemic reasons than many scholars seem to believe. By outlining that more transparency in algorithms is not always necessary, and by explaining that (...) computational processes are indeed methodologically opaque to humans, we argue that the reliability of algorithms provides reasons for trusting the outcomes of medical artificial intelligence. To this end, we explain how computational reliabilism, which does not require transparency and supports the reliability of algorithms, justifies the belief that results of medical AI are to be trusted. We also argue that several ethical concerns remain with black box algorithms, even when the results are trustworthy. Having justified knowledge from reliable indicators is, therefore, necessary but not sufficient for normatively justifying physicians to act. This means that deliberation about the results of reliable algorithms is required to find out what is a desirable action. Thus understood, we argue that such challenges should not dismiss the use of black box algorithms altogether but should inform the way in which these algorithms are designed and implemented. When physicians are trained to acquire the necessary skills and expertise, and collaborate with medical informatics and data scientists, black box algorithms can contribute to improving medical care. (shrink)

In an era of technological ubiquity, artificial intelligence (AI) is reshaping not only industries but also fundamental human experiences, including artistic creativity. Rooted in a Posthumanist theoretical framework, this research scrutinizes the intricate ethical and aesthetic challenges that artists confront in AI-enabled art creation, with a particular focus on a novel phenomenon we term 'aesthetic loss of control.’ This phenomenon bears significant implications for notions of authorship, copyright, and business ethics in the art industry. Utilizing a mixed-methods approach, our (...) study involves a six-month-long collaboration with 34 artists from diverse artistic and cultural milieus, facilitated by AI algorithms versed in an array of artistic styles. Through iterative cycles of human input and AI output, coupled with in-depth interviews, observational studies, and diary analyses, we meticulously document the artists’ experiences and their emerging doubts over authorship and creative control. Our findings illuminate the nuanced complexities surrounding this 'aesthetic loss of control,’ extending current discussions in business ethics by offering empirically grounded insights and recommendations for navigating these ethical dilemmas. The study not only contributes new theoretical perspectives to the discourse but also provides actionable ethical guidelines for stakeholders in the art industry's commercial ecosystem. (shrink)

The Buddhist Jātaka tells the tale of a hare lounging under a palm tree who becomes convinced the Earth is coming to an end when a ripe bael fruit falls on its head. Soon all the hares are running; other animals join them, forming a stampede of deer, boar, elk, buffalo, wild oxen, rhinoceros, tigers and elephants, loudly proclaiming the earth is ending.1 In the American retelling, the hare is ‘chicken little,’ and the exaggerated fear is that the sky is (...) falling. The story offers a cautionary tale for considering the trend towards calamity thinking in artificial intelligence (AI). A growing chorus of tech leaders has warned that AI poses existential risk (X-Risk) that could result in the extinction of the human species, the collapse of civilisation, or a colossal decline in human potential and culture. In 2014, Hawking told the Washington Post that AI, ‘could spell the end of the human race’.2 Musk has repeatedly warned about AI’s perils, calling AI, ‘more dangerous than nukes’, recommending colonising Mars to ensure ‘a bolt-hole if AI goes rogue and turns on humanity’,3 and donating 10 million dollars to the Future of Life Institute to jumpstart research on AI’s X-Risk. Gates has stated, ‘I am in the camp…concerned about super intelligence…I agree with … Musk and some others on this and don’t understand why some people are not concerned’.4 Altman, CEO of OpenAI, has fretted that technologies OpenAI was building could endanger humanity—‘even destroying the world as we know it’.5 This paper offers a critical appraisal of the rise of calamity thinking in the scholarly AI ethics literature. It cautions against viewing X-Risk in isolation and highlights ethical considerations sidelined when X-Risk takes centre stage. Section I introduces a working definition of X-Risk, considers its likelihood and …. (shrink)

While the technologies that enable Artificial Intelligence (AI) continue to advance rapidly, there are increasing promises regarding AI’s beneficial outputs and concerns about the challenges of human–computer interaction in healthcare. To address these concerns, institutions have increasingly resorted to publishing AI guidelines for healthcare, aiming to align AI with ethical practices. However, guidelines as a form of written language can be analyzed to recognize the reciprocal links between its textual communication and underlying societal ideas. From this perspective, we conducted a (...) discourse analysis to understand how these guidelines construct, articulate, and frame ethics for AI in healthcare. We included eight guidelines and identified three prevalent and interwoven discourses: (1) AI is unavoidable and desirable; (2) AI needs to be guided with (some forms of) principles (3) trust in AI is instrumental and primary. These discourses signal an over-spillage of technical ideals to AI ethics, such as over-optimism and resulting hyper-criticism. This research provides insights into the underlying ideas present in AI guidelines and how guidelines influence the practice and alignment of AI with ethical, legal, and societal values expected to shape AI in healthcare. (shrink)

As the artificial intelligence (AI) ethics field is currently working towards its operationalisation, ethics review as carried out by research ethics committees (RECs) constitutes a powerful, but so far underdeveloped, framework to make AI ethics effective in practice at the research level. This article contributes to the elaboration of research ethics frameworks for research projects developing and/or using AI. It highlights that these frameworks are still in their infancy and in need of a structure and (...) criteria to ensure AI research projects advance in a way that respects norms and principles. This article proposes to draw from the European Union’s AI Act currently in development to shape these frameworks. Although, in the current form of the draft (as of August 2023), the obligations of the AI Act do not apply to scientific research, it is most likely that they will still have a strong impact on AI research considering the need to anticipate market placement or to test new tools in real world conditions. This article investigates what the risk-based approach in the AI Act implies for research ethics and highlights some AI Act obligations of particular value to implement for ethics review processes. (shrink)

There is enormous interest in using artificial intelligence (AI) in health care contexts. But before AI can be used in such settings, we need to make sure that AI researchers and organizations follow appropriate ethical frameworks and guidelines when developing these technologies. In recent years, a great number of ethical frameworks for AI have been proposed. However, these frameworks have tended to be abstract and not explain what grounds and justifies their recommendations and how one should use these recommendations in (...) practice. In this paper, I propose an AI ethics framework that is grounded in substantive, human rights theory and one that can help us address these questions. (shrink)

Societies of the East are often contrasted with those of the West in their stances toward technology. This paper explores these perceived differences in the context of international ethics guidance for artificial intelligence (AI) and robotics. Japan serves as an example of the East, while Europe and North America serve as examples of the West. The paper’s principal aim is to demonstrate that Western values predominate in international ethics guidance and that Japanese values serve as a much-needed corrective. (...) We recommend a hybrid approach that is more inclusive and truly ‘international’. Following an introduction, the paper examines distinct stances toward robots that emerged in the West and Japan, respectively, during the aftermath of the Second World War, reflecting history and popular culture, socio-economic conditions, and religious worldviews. It shows how international ethics guidelines reflect these disparate stances, drawing on a 2019 scoping review that examined 84 international AI ethics documents. These documents are heavily skewed toward precautionary values associated with the West and cite the optimistic values associated with Japan less frequently. Drawing insights from Japan’s so-called ‘moonshot goals’, the paper fleshes out Japanese values in greater detail and shows how to incorporate them more effectively in international ethics guidelines for AI and robotics. (shrink)

AI-supported methods for identifying and combating disinformation are progressing in their development and application. However, these methods face a litany of epistemic and ethical challenges. These include (1) robustly defining disinformation, (2) reliably classifying data according to this definition, and (3) navigating ethical risks in the deployment of countermeasures, which involve a mixture of harms and benefits. This paper seeks to expose and offer preliminary analysis of these challenges.

The risks AI presents to society are broadly understood to be manageable through ‘general calculus’, i.e., general frameworks designed to enable those involved in the development of AI to apprehend and manage risk, such as AI impact assessments, ethical frameworks, emerging international standards, and regulations. This paper elaborates how risk is apprehended and managed by a regulator, developer and cyber-security expert. It reveals that risk and risk management is dependent on mundane situated practices not encapsulated in general calculus. Situated practice (...) surfaces ‘iterable epistopics’, revealing how those involved in the development of AI know and subsequently respond to risk and uncover major challenges in their work. The ongoing discovery and elaboration of epistopics of risk in AI (a) furnishes a potential program of interdisciplinary inquiry, (b) provides AI developers with a means of apprehending risk, and (c) informs the ongoing evolution of general calculus. (shrink)

In their article ‘Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI’, Durán and Jongsma discuss the epistemic and ethical challenges raised by black box algorithms in medical practice. The opacity of black box algorithms is an obstacle to the trustworthiness of their outcomes. Moreover, the use of opaque algorithms is not normatively justified in medical practice. The authors introduce a formalism, called computational reliabilism, which allows generating justified beliefs on the (...) algorithm reliability and trustworthy outcomes of artificial intelligence systems by means of epistemic warrants, called reliability indicators. However, they remark the need for reliability indicators specific to black box algorithms and that justified knowledge is not sufficient to justify normatively the actions of the physicians using medical AI systems. Therefore, Durán and Jongsma advocate for a more transparent design and implementation of black box algorithms, providing a series of recommendations to mitigate the epistemic and ethical challenges behind their use in medical practice. In this response, I argue that a peculiar form of black box algorithm transparency, called design publicity, may efficiently implement these recommendations. Design publicity encodes epistemic, that is, reliability indicators, and ethical recommendations for black box algorithms by means of four subtypes of transparency. These target the values and goals, their translation into design requirements, the performance and consistency of the algorithm altogether. I discuss design publicity applying it to a use case focused on the automated classification of skin lesions from medical images. (shrink)

This book explores the intersection of legal frameworks, healthcare innovation, and computational intelligence, shedding light on how emerging technologies like AI and ML are reshaping the medical landscape. It presents real life challenges such as patient privacy, data security, and compliance issues in smart healthcare by engaging into associated ethical and regulatory implications. Comprising the concepts of predictive analytics, regulatory compliance algorithms, and legal decision-making processes, this book offers a roadmap for stakeholders to navigate the evolving landscape of healthcare innovation (...) responsibly and ethically. Investigates how artificial intelligence supports legal decision-making processes in smart healthcare, addressing ethical and accountability concerns Demonstrates the use of smart contracts and automation tools powered by computational intelligence to streamline legal processes and ensure compliance in smart healthcare initiatives Focuses on managing healthcare data within legal frameworks, emphasizing computational intelligence's role in ensuring privacy and security. Examines how computational intelligence enables predictive analytics models to anticipate legal challenges and compliance issues in smart healthcare. Highlights the development and implementation of computational algorithms for navigating evolving legal frameworks in healthcare. This reference book is a useful resource for scholars and researchers working on smart healthcare. (shrink)

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

As a normative concept, consent can perform the “moral magic” of transforming the moral relationship between two parties, rendering permissible otherwise impermissible actions. Yet, as a governance mechanism for achieving ethical data practices, consent has become strained—and AI has played no small part in its contentious state. In this chapter we will describe how consent has become such a controversial component of data protection as artificial intelligence systems have proliferated in our everyday lives, highlighting five distinct issues. We will then (...) lay out what we call consent’s “moral core,” which emphasizes five elements for meaningful consent. We next apply the moral core to AI systems, finding meaningful consent viable within a particular digital landscape. Finally, we discuss the forces driving some commentators away from individual consent and whether meaningful consent has a future in a smart world. (shrink)

This paper navigates the relationship between the growing influence of Artificial Intelligence (AI) and the foundational principles of academic integrity. It offers an in-depth analysis of how key academic stakeholders—publishers and universities—are crafting strategies and guidelines to integrate AI into the sphere of scholarly work. These efforts are not merely reactionary but are part of a broader initiative to harness AI’s potential while maintaining ethical standards. The exploration reveals a diverse array of stances, reflecting the varied applications of AI in (...) academic settings. These range from conservative approaches emphasizing the need for stringent AI regulations to more progressive strategies that seek to leverage AI’s capabilities. The paper accentuates the importance of guidelines that are adaptable, transparent, and culturally sensitive, acknowledging the global context of academic research. It also highlights the pressing need for effective implementation and enforcement of these guidelines to ensure that the integration of AI into academia does not compromise the integrity of scholarly work. The conclusion emphasizes the dynamic balance required between embracing AI’s transformative potential and upholding the timeless values of academic rigor and ethical research. This comprehensive analysis aims to provide a detailed perspective on the evolving role of AI in academia, fostering a dialog on how best to navigate this new technological frontier while preserving the sanctity of academic scholarship. (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)

The subject matter of research is contradictions and moral-legal antinomies arising in the philosophy of science, in relation to a set of technologies called “Anti-Plagiarism”. The formal-logical and formal-axiological aspects of the notions “property”, “common property”, “private property”, “theft”, “plundering” and others are considered. The paper argues for the urgent necessity to allow authors unlimited reuse of any fragments of their previously published texts in their new publications actually containing novel scientific results. The condition is that such duplication is indispensable (...) for understanding and rechecking the novel discovers. The scientific novelty: theoretic arguments are exemplified by a hitherto never published new scientific result made within a discrete mathematical model of natural law of private and common ownership. (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)

The somewhat superannuated “problem of other minds” has unexpectedly risen from the dead, and, in its current incarnation, concerns the mental states of those who never lived.