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)

Introduces contemporary American philosophy of technology through six of its leading figures. The six American philosophers of technology whose work is profiled in this clear and concise introduction to the field—Albert Borgmann, Hubert Dreyfus, Andrew Feenberg, Donna Haraway, Don Ihde, and Langdon Winner—represent a new, empirical direction in the philosophical study of technology that has developed mainly in North America. In place of the grand philosophical schemes of the classical generation of European philosophers of technology, the contemporary (...) class='Hi'>American generation addresses concrete technological practices and the co-evolution of technology and society in modern culture. Six Dutch philosophers associated with Twente University survey and critique the full scope and development of their American colleagues’ work, often illustrating shifts from earlier to more recent interests. Individual chapters focus on Borgmann’s engagement with technology and everyday life; Dreyfus’s work on the limits of artificial intelligence; Feenberg’s perspectives on the cultural and social possibilities opened by technologies; Haraway’s conception of the cyborg and its attendant blurring of boundaries; Ihde’s explorations of the place of technology in the lifeworld; and Winner’s fascination with the moral and political implications of modern technologies. American Philosophy of Technology offers an insightful and readable introduction to this new and distinctly American philosophical turn. Contributors are Hans Achterhuis, Philip Brey, René Munnik, Martijntje Smits, Pieter Tijmes, and Peter-Paul Verbeek. (shrink)

Introduces contemporary American philosophy of technology through six of its leading figures. The six American philosophers of technology whose work is profiled in this clear and concise introduction to the field--Albert Borgmann, Hubert Dreyfus, Andrew Feenberg, Donna Haraway, Don Ihde, and Langdon Winner--represent a new, empirical direction in the philosophical study of technology that has developed mainly in North America. In place of the grand philosophical schemes of the classical generation of European philosophers of technology (including Martin Heidgger, (...) Jacques Ellul, and Hans Jonas), the contemporary American generation addresses concrete technological practices and the co-evolution of technology and society in modern culture. Six Dutch philosophers associated with Twente University survey and critique the full scope and development of their American colleagues' work, often illustrating shifts from earlier to more recent interests. Individual chapters focus on Borgmann's engagement with technology and everyday life; Dreyfus's work on the limits of artificial intelligence; Feenberg's perspectives on the cultural and social possibilities opened by technologies; Haraway's conception of the cyborg and its attendant blurring of boundaries; Ihde's explorations of the place of technology in the lifeworld; and Winner's fascination with the moral and political implications of modern technologies. American Philosophy of Technology offers an insightful and readable introduction to this new and distinctly American philosophical turn. (shrink)

Recent years have witnessed intensive efforts to specify which requirements ethical artificial intelligence (AI) must meet. General guidelines for ethical AI consider a varying number of principles important. A frequent novel element in these guidelines, that we have bundled together under the term explicability, aims to reduce the black-box character of machine learning algorithms. The centrality of this element invites reflection on the conceptual relation between explicability and the four bioethical principles. This is important because the application of (...) general ethical frameworks to clinical decision-making entails conceptual questions: Is explicability a free-standing principle? Is it already covered by the well-established four bioethical principles? Or is it an independent value that needs to be recognized as such in medical practice? We discuss these questions in a conceptual-ethical analysis, which builds upon the findings of an empirical document analysis. On the example of the medical specialty of radiology, we analyze the position of radiological associations on the ethical use of medical AI. We address three questions: Are there references to explicability or a similar concept? What are the reasons for such inclusion? Which ethical concepts are referred to? (shrink)

Artificial intelligence is becoming part of our everyday experience and is expected to be ever more integrated into ordinary life for many years to come. Thus, it is important for those in product development, research, and public policy to understand how the public’s perception of AI is shaped. In this study, we conducted focus groups and an online survey to determine the knowledge of AI held by the American public, and to judge whether entertainment media is a (...) major influence on how Americans perceive AI. What we found is that the American public’s knowledge of AI is patchy: some have a good understanding of what is and what is not AI, but many do not. When it came to understanding what AI can do, most respondents believe that AI could “replace human jobs” but few thought that it could “feel emotion.” Most respondents were optimistic about the future and impact of AI, though about one third were not sure. Most respondents also did not think they could develop an emotional bond with or be comfortable being provided care by an AI. Regarding the influence of entertainment media on perceptions of AI, we found a significant relationship between people’s beliefs about AI in entertainment media and their beliefs about AI in reality. Those who believe AI is realistically depicted in entertainment media were more likely to see AIs as potential emotional partners or apocalyptic robots than to imagine AIs taking over jobs or operating as surveillance tools. (shrink)

As China and the United States strive to be the primary global leader in AI, their visions are coming into conflict. This is frequently painted as a fundamental clash of civilisations, with evidence based primarily around each country’s current political system and present geopolitical tensions. However, such a narrow view claims to extrapolate into the future from an analysis of a momentary situation, ignoring a wealth of historical factors that influence each country’s prevailing philosophy of technology and thus their overarching (...) AI strategies. In this article, we build a philosophy-of-technology-grounded framework to analyse what differences in Chinese and American AI policies exist and, on a fundamental level, why they exist. We support this with Natural Language Processing methods to provide an evidentiary basis for our analysis of policy differences. By looking at documents from three different American presidential administrations––Barack Obama, Donald Trump, and Joe Biden––as well as both national and local policy documents (many available only in Chinese) from China, we provide a thorough comparative analysis of policy differences. This article fills a gap in US–China AI policy comparison and constructs a framework for understanding the origin and trajectory of policy differences. By investigating what factors are informing each country’s philosophy of technology and thus their overall approach to AI policy, we argue that while significant obstacles to cooperation remain, there is room for dialogue and mutual growth. (shrink)

This comprehensive analysis dives deep into the intricate interplay between artificial intelligence (AI) and human agency, examining the remarkable capabilities and inherent limitations of large language models (LLMs) such as GPT-3 and ChatGPT. The paper traces the complex trajectory of AI's evolution, highlighting its operation based on statistical pattern recognition, devoid of self-consciousness or innate comprehension. As AI permeates multiple spheres of human life, it raises substantial ethical, legal, and societal concerns that demand immediate attention and deliberation. The (...) metaphorical illustration of humans as “shepherds” of AI, coupled with a thought-provoking examination of “agency without intelligence,” engenders deep reflections on the future of human-AI interplay and the associated philosophical, ethical, and practical ramifications. The paper further elucidates critical challenges associated with AI, such as ethical dilemmas, “agency without intelligence,” the role of human oversight, and the environmental impact of AI technologies. In the pursuit of solutions, the paper maps prospective directions, emphasizing the need for robust AI policies, improved AI literacy, sustainable AI practices, and a continued dialogue on the multifaceted issues surrounding AI. Through a holistic exploration of AI, this paper underlines the necessity for a multidisciplinary, collaborative approach to chart an ethical, beneficial, and sustainable AI future, enabling society to harness the potential of AI responsibly while mitigating the associated risks. (shrink)

This article is based on a roundtable held at the Association of Internet Researchers annual conference in 2017, in Tartu, Estonia. The roundtable was organized by the Oxford Internet Institute’s Digital Ethics Lab. It was entitled “Artificial Intelligence and the Good Society”. It brought together four scholars—Michael Zimmer, Stine Lomborg, Ben Zevenbergen, and Corinne Cath—to discuss the promises and perils of artificial intelligence, in particular what ethical frameworks are needed to guide AI’s rapid development and (...) increased use in societies. The paper covers three case studies. They give a distinct overview of the ethical issues raised by the use of AI at different levels of analysis: top-down application of AI, bottom-up use of AI, and how academics and governments have reacted to these new challenges. From the case studies, four areas emerged. They represent some of the most topical ethical questions related to AI: its uses, its users, its designers, and the data that fuel it. Each of them provided a specific subset of ethical concerns that need further investigation. In conclusion, three recommendations are formulated for researchers and regulators to ensure the AI has a net-positive impact on society. (shrink)

Artificial intelligence (AI) based clinical decision support systems (CDSS) are becoming ever more widespread in healthcare and could play an important role in diagnostic and treatment processes. For this reason, AI‐based CDSS has an impact on the doctor–patient relationship, shaping their decisions with its suggestions. We may be on the verge of a paradigm shift, where the doctor–patient relationship is no longer a dual relationship, but a triad. This paper analyses the role of AI‐based CDSS for shared decision‐making (...) to better comprehend its promises and associated ethical issues. Moreover, it investigates how certain AI implementations may instead foster the inappropriate paradigm of paternalism. Understanding how AI relates to doctors and influences doctor–patient communication is essential to promote more ethical medical practice. Both doctors' and patients' autonomy need to be considered in the light of AI. (shrink)

This is a draft of the written version of comments on a paper by David Cole, presented orally at the American Philosophical Association Central Division meeting in New Orleans, 27 April 1990. Following the written comments are 2 appendices: One contains a letter to Cole updating these comments. The other is the handout from the oral presentation.

Artificial intelligence (AI) assurance is an umbrella term describing many approaches—such as impact assessment, audit, and certification procedures—used to provide evidence that an AI system is legal, ethical, and technically robust. AI assurance approaches largely focus on two overlapping categories of harms: deployment harms that emerge at, or after, the point of use, and individual harms that directly impact a person as an individual. Current approaches generally overlook upstream collective and societal harms associated with the development of systems, (...) such as resource extraction and processing, exploitative labour practices and energy intensive model training. Thus, the scope of current AI assurance practice is insufficient for ensuring that AI is ethical in a holistic sense, i.e. in ways that are legally permissible, socially acceptable, economically viable and environmentally sustainable. This article addresses this shortcoming by arguing for a broader approach to AI assurance that is sensitive to the full scope of AI development and deployment harms. To do so, the article maps harms related to AI and highlights three examples of harmful practices that occur upstream in the AI supply chain and impact the environment, labour, and data exploitation. It then reviews assurance mechanisms used in adjacent industries to mitigate similar harms, evaluating their strengths, weaknesses, and how effectively they are being applied to AI. Finally, it provides recommendations as to how a broader approach to AI assurance can be implemented to mitigate harms more effectively across the whole AI supply chain. (shrink)

The study of law and information technology comes with an inherent contradiction in that while technology develops rapidly and embraces notions such as internationalization and globalization, traditional law, for the most part, can be slow to react to technological developments and is also predominantly confined to national borders. However, the notion of the rule of law defies the phenomenon of law being bound to national borders and enjoys global recognition. However, a serious threat to the rule of law is looming (...) in the form of an assault by technological developments within artificial intelligence (AI). As large strides are made in the academic discipline of AI, this technology is starting to make its way into digital decision-making systems and is in effect replacing human decision-makers. A prime example of this development is the use of AI to assist judges in making judicial decisions. However, in many circumstances this technology is a ‘black box’ due mainly to its complexity but also because it is protected by law. This lack of transparency and the diminished ability to understand the operation of these systems increasingly being used by the structures of governance is challenging traditional notions underpinning the rule of law. This is especially so in relation to concepts especially associated with the rule of law, such as transparency, fairness and explainability. This article examines the technology of AI in relation to the rule of law, highlighting the rule of law as a mechanism for human flourishing. It investigates the extent to which the rule of law is being diminished as AI is becoming entrenched within society and questions the extent to which it can survive in the technocratic society. (shrink)

The article examines the problem of ensuring the political stability of a democratic social system with a shortage of a vital commodity (like vaccine against COVID-19). In such a system, members of society citizens assess the authorities. Thus, actions by the authorities to increase the supply of this commodity can contribute to citizens' approval and hence political stability. However, this supply is influenced by random factors, the actions of competitors, etc. Therefore, citizens do not have sufficient information about all the (...) possibilities of supplying, and it is difficult for them to make the right decisions. Such citizen unawareness can be exploited by unscrupulous politicians to achieve personal targets. Therefore, it is necessary to organize public control to motivate politicians to use all available opportunities in supplying. The goal of the paper is to build such a digital mechanism of public control of the politicians by citizens, which would best assess and stimulate the activities of the authorities to improve the supply of a vital commodity. In the age of artificial intelligence, such digital public control in the face of uncertainty can be based on digital machine learning. In addition, it is necessary to take into account and model the activities of politicians associated with the presence of their own targets that do not coincide with public ones. Such politicians can use the learning of citizens for their own targets. The objective of the article is to build an optimal digital mechanism of public control in a two-level model of a democratic social system—a digital society. At its top level, there is the Citizen, who gives an assessment for the Politico located at the lower level. In turn, the Politico can influence the supplying of a vital commodity. Political stability is guaranteed if the Citizen regularly approves of the Politico’s actions to increase this supply. However, the Politico may not use the opportunities available to him to offer a commodity to achieve a personal target. To avoid this, the Politico’s control mechanism is proposed. It includes the procedure for digital learning of the Citizen, as well as a procedure for assessing the Politico activity. Sufficient conditions have been found for the synthesis of such the Politico’s control mechanism, at which stochastic possibilities of increasing the supply of a vital commodity are used. The example of such the Politico’s control mechanism is considered on the case of supply of the COVID-19 vaccine in England. (shrink)

The inquiry into the moral status of artificial intelligence (AI) is leading to prolific theoretical discussions. A new entity that does not share the material substrate of human beings begins to show signs of a number of properties that are nuclear to the understanding of moral agency. It makes us wonder whether the properties we associate with moral status need to be revised or whether the new artificial entities deserve to enter within the circle of moral consideration. (...) This raises the foreboding that we are at the gates of an anthropological crisis: the properties bound to moral agency have been exclusively possessed in the past by human beings and have shaped the very definition of being human. In this article, I will argue that AI does not lead us to an anthropological crisis and that, if we adhere to the history and philosophy of technology, we will notice that the debate on the moral status of AI uncritically starts from an anthropology of properties and loses sight of the relational dimension of technology. First, I will articulate three criteria for analyzing different anthropological views in philosophy of technology. Second, I will propose six anthropological models: traditional, industrial, phenomenological, postphenomenological, symmetrical, and cyborg. Third, I will show how the emergence of AI breaks with the dynamics of increased relationality in the history and philosophy of technology. I will argue that this aspect is central to debates about the moral status of AI, since it sheds light on an aspect of moral consideration that has been obscured. Finally, I will reject entirely relational approaches to moral status and propose two hybrid possibilities for rethinking it. (shrink)

The ethics of artificial intelligence and machine learning exemplify the conceptual struggle between applying familiar pathways of ethical analysis versus generating novel strategies. Mel...

The world’s current model for economic development is unsustainable. It encourages high levels of resource extraction, consumption, and waste that undermine positive environmental outcomes. Transitioning to a circular economy (CE) model of development has been proposed as a sustainable alternative. Artificial intelligence (AI) is a crucial enabler for CE. It can aid in designing robust and sustainable products, facilitate new circular business models, and support the broader infrastructures needed to scale circularity. However, to date, considerations of the ethical (...) implications of using AI to achieve a transition to CE have been limited. This article addresses this gap. It outlines how AI is and can be used to transition towards CE, analyzes the ethical risks associated with using AI for this purpose, and supports some recommendations to policymakers and industry on how to minimise these risks. (shrink)

The development of artificial intelligence (AI) in medicine raises fundamental ethical issues. As one example, AI systems in the field of mental health successfully detect signs of mental disorders, such as depression, by using data from social media. These AI depression detectors (AIDDs) identify users who are at risk of depression prior to any contact with the healthcare system. The article focuses on the ethical implications of AIDDs regarding affected users’ health-related autonomy. Firstly, it presents the (ethical) discussion (...) of AI in medicine and, specifically, in mental health. Secondly, two models of AIDDs using social media data and different usage scenarios are introduced. Thirdly, the concept of patient autonomy, according to Beauchamp and Childress, is critically discussed. Since this concept does not encompass the specific challenges linked with the digital context of AIDDs in social media sufficiently, the current analysis suggests, finally, an extended concept of health-related digital autonomy. (shrink)

Coined in 1984 at the American Association of Artificial intelligence (now the Association for the Advancement of Artificial Intelligence or AAAI), the various boom and bust periods of AI research and funding lead AI researchers Marvin Minsky and Roger Schank to refer to the then-impending bust period as an AI Winter. Canadian AI researcher Daniel Crevier describes the phenomenon as a domino effect that begins with cynicism in the AI research community that then (...) trickles to mass media and finally to funding bodies. The result is a freeze in serious AI research and development. This initial pessimism is mainly attributed to the overly ambitious promises that AI can yield with the actual results being far humbler than expectations. (shrink)

Analysing how news media portray A.I. reveals what interpretative frameworks around the technology circulate in public discourses. This allows for critical reflections on the making of meaning in prevalent narratives about A.I. and its impact. While research on the public perception of datafication and automation is growing, only a few studies investigate news framing practices. The present study connects to this nascent research area by charting A.I. news frames in four internationally renowned media outlets: The New York Times, The Guardian, (...) Wired, and Gizmodo. The main goals are to identify dominant emphasis frames in AI news reporting over the past decade, to explore whether certain A.I. frames are associated with specific data risks, and what journalists and experts contribute to the media discourse. An automated content analysis serves for inductive frame detection, identification of risk references, and network analysis of news writers. The results show how A.I.’s ubiquity emerged rapidly in the mid-2010s, and that the news discourse became more critical over time. It is further argued that A.I. news reporting is an important factor in building critical data literacy among lay audiences. (shrink)

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

The development of the systems of artificial intelligence (AI) and digital transformation in general lead to the formation of multitude of autonomous agents of artificial and mixed genealogy, as well as to complex structures in the information and regulatory environment with many opportunities and pathologies and a growing level of uncertainty in making managerial decisions. The situation is complicated by the continuing plurality of understanding of the essence of AI systems. The modern expanded understanding of AI goes (...) back to ideas formulated more than 100 years ago. In official national policy documents on the development of AI, working definitions of AI are preferred. The current stage of AI systems life cycle can be assessed as the completion of the initial period in the development of systems associated with weak AI. The ability of artificial systems to realize themselves as a separate person becomes one of the serious scientific and practical challenges. Attention to the issues of the ethics of AIS indicates the expansion of the diversity of its forms and the beginning of the work in the field of goal-setting. New moral and ethical problems also arise in connection with the possibility of the creation of genuine conscious subjects in the foreseeable future. There is an increasing phenomenon of degradation of natural intelligence. It is required to take into account the issue of the heterogeneity of data generated by humans, electronic sensors and network devices in the dynamic complex environments of the digital economy, the issue of the complexity of the process of co-evolution of AI systems, collective and individual natural consciousness. A special area of opportunities and risks is the development of neurotechnologies. The object of control is digital twins, through which there can be manipulation of real attitudes, preferences, and behavior of individuals. As a result, there are the development of technological capabilities that provoke destructive phenomena as well as the formation of a new class of mass addictions. (shrink)

Drawing on the analytic of the “colonial matrix of power” developed by Aníbal Quijano within the Latin American modernity/coloniality research program, this article theorises how a system of coloniality underpins the structuring logic of artificial intelligence (AI) systems. We develop a framework for critiquing the regimes of global labour exploitation and knowledge extraction that are rendered invisible through discourses of the purported universality and objectivity of AI. ​​Through bringing the political economy literature on AI production into conversation (...) with scholarly work on decolonial AI and the modernity/coloniality research program, we advance three main arguments. First, the global economic and political power imbalances in AI production are inextricably linked to the continuities of historical colonialism, constituting the colonial supply chain of AI. Second, this is produced through an international division of digital labour that extracts value from majority world labour for the benefit of Western technology companies. Third, this perpetuates hegemonic knowledge production through Western values and knowledge that marginalises non-Western alternatives within AI’s production and limits the possibilities for decolonising AI. By locating the production of AI systems within the colonial matrix of power, we contribute to critical and decolonial literature on the legacies of colonialism in AI and the hierarchies of power and extraction that shape the development of AI today. (shrink)

Artificial intelligence is increasingly permeating many types of high-stake societal decision-making such as the work at the criminal courts. Various types of algorithmic tools have already been introduced into sentencing. This article concerns the use of algorithms designed to deliver sentence recommendations. More precisely, it is considered how one should determine whether one type of sentencing algorithm (e.g., a model based on machine learning) would be ethically preferable to another type of sentencing algorithm (e.g., a model based on (...) old-fashioned programming). Whether the implementation of sentencing algorithms is ethically desirable obviously depends upon various questions. For instance, some of the traditional issues that have received considerable attention are algorithmic biases and lack of transparency. However, the purpose of this article is to direct attention to a further challenge that has not yet been considered in the discussion of sentencing algorithms. That is, even if is assumed that the traditional challenges concerning biases, transparency, and cost-efficiency have all been solved or proven insubstantial, there will be a further serious challenge associated with the comparison of sentencing algorithms; namely, that we do not yet possess an ethically plausible and applicable criterion for assessing how well sentencing algorithms are performing. (shrink)

The advent of the intelligence age has injected new elements into the development of literature. The synergic modification of Anglo-American traumatic narrative literature by artificial intelligence technology and interactive design psychology will produce new possibilities in literary creation. First, by studying natural language processing technology, this study proposes a modification language model based on the double-layered recurrent neural network algorithm and constructs an intelligent language modification system based on the improved LM model. The results show that (...) the performance of the proposed model is excellent; only about 30% of the respondents like AAL literature; the lack of common cultural background, appreciation difficulties, and language barriers have become the main reasons for the decline of reading willingness of AAL literature. Finally, AI technology and ID psychology are used to modify a famous TN work respectively and synergically, and the modified work is appreciated by respondents to collect their comments. The results corroborate that 62% of the respondents like original articles, but their likability scores have decreased for individually modified work by AI or ID psychology. In comparison, under the synergic modification efforts of AI and ID psychology, the popularity of the modified work has increased slightly, with 65% of the respondents showing a likability to read. Therefore, it is concluded that literary modification by single ID psychology or AI technology will reduce the reading threshold by trading off the literary value of the original work. The core of literary creation depends on human intelligence, and AI might still not be able to generate high-standard literary works independently because human minds and thoughts cannot be controlled and predicted by machines. The research results provide new ideas and improvement directions for the field of AI-assisted writing. (shrink)

Trust and trustworthiness are central concepts in contemporary discussions about the ethics of and qualities associated with artificial intelligence (AI) and the relationships between people, organisations and AI. In this article we develop an interdisciplinary approach, using socio-technical software engineering and design anthropological approaches, to investigate how trust and trustworthiness concepts are articulated and performed by AI software practitioners. We examine how trust and trustworthiness are defined in relation to AI across these disciplines, and investigate how AI, trust (...) and trustworthiness are conceptualised and experienced through an ethnographic study of the work practices of nine practitioners in the software industry. We present key implications of our findings for the generation of trust and trustworthiness and for the training and education of future software practitioners. (shrink)

The article discusses the main trends in the development of artificial intelligence systems and robotics (AI&R). The main question that is considered in this context is whether artificial systems are going to become more and more anthropomorphic, both intellectually and physically. In the current article, the author analyzes the current state and prospects of technological development of artificial intelligence and robotics, and also determines the main aspects of the impact of these technologies on society and (...) economy, indicating the geopolitical strategic nature of this influence. The author considers various approaches to the definition of artificial intelligence and robotics, focusing on the subject-oriented and functional ones. It also compares AI&R abilities and human abilities in areas such as categorization, pattern recognition, planning and decision making, etc. Based on this comparison, we investigate in which areas AI&R’s performance is inferior to a human, and in which cases it is superior to one. The modern achievements in the field of robotics and artificial intelligence create the necessary basis for further discussion of the applicability of goal setting in engineering, in the form of a Turing test. It is shown that development of AI&R is associated with certain contradictions that impede the application of Turing’s methodology in its usual format. The basic contradictions in the development of AI&R technologies imply that there is to be a transition to a post-Turing methodology for assessing engineering implementations of artificial intelligence and robotics. In such implementations, on the one hand, the ‘Turing wall’ is removed, and on the other hand, artificial intelligence gets its physical implementation. (shrink)

The following paper presents both a historical and personal account of the societal and ethical issues arising in the development of artificial intelligence, tracking, where I was involved, the issues from the nineteen seventies onward. My own involvement in the AI narrative begins with the early discussions around whether machines can think. These first discussions, in time, evolved secondly, with the rise of the internet in the nineties, into perceptions of AI as distributed intelligence, addressing its impact (...) on social structures including basic ethical issues arising in daily life. Thirdly, in the sweeping application of AI to all kinds of societal goals and contexts, the awareness that all natural and artificial things might be digitally connected with each other and to human agents led my further involvement in the AI narrative. Tracing this evolution from start to finish, I conclude my own narrative in the history of AI by presenting some of the future challenges for the development and use of artificial intelligences. Through the application of recent research in academia, scientific associations and political bodies, I address the possibilities for the good life, both with and without artificial intelligences. (shrink)

The current research aims to answer the following question: “who will be held responsible for harm involving an artificial intelligence system?” Drawing upon the literature on moral judgments, we assert that when people perceive an AI system’s action as causing harm to others, they will assign blame to different entity groups involved in an AI’s life cycle, including the company, the developer team, and even the AI system itself, especially when such harm is perceived to be intentional. Drawing (...) upon the theory of mind perception, we hypothesized that two dimensions of mind: perceived agency—attributing intention, reasoning, pursuing goals, and communicating to AI, and perceived experience—attributing emotional states, such as feeling pain and pleasure, personality, and consciousness to AI—mediated the relationship between perceived intentional harm and blame judgments toward AI. We also predicted that people are likely to attribute higher mind characteristics to AI when harm is perceived to be directed to humans than when it is perceived to be directed to non-humans. We tested our research model in three experiments. In all experiments, we found that perceived intentional harm led to blame judgments toward AI. In two experiments, we found perceived experience, not agency, mediated the relationship between perceived intentional harm and blame judgments. We also found that companies and developers were held responsible for moral violations involving AI, with developers received the most blame among the entities involved. Our third experiment reconciles the findings by showing that perceived intentional harm directed to a non-human entity did not lead to increased attributions of mind to AI. These findings have implications for theory and practice concerning unethical outcomes and behavior associated with AI use. (shrink)

Conversational artificial intelligence (CAI) presents many opportunities in the psychotherapeutic landscape—such as therapeutic support for people with mental health problems and without access to care. The adoption of CAI poses many risks that need in-depth ethical scrutiny. The objective of this paper is to complement current research on the ethics of AI for mental health by proposing a holistic, ethical, and epistemic analysis of CAI adoption. First, we focus on the question of whether CAI is rather a tool (...) or an agent. This question serves as a framework for the subsequent ethical analysis of CAI focusing on topics of (self-) knowledge, (self-)understanding, and relationships. Second, we propose further conceptual and ethical analysis regarding human-AI interaction and argue that CAI cannot be considered as an equal partner in a conversation as is the case with a human therapist. Instead, CAI’s role in a conversation should be restricted to specific functions. (shrink)

인공지능(artificial intelligence) 로봇과 관련된 과학 기술의 비약적 발전으로 인해, 이들 존재의 현실화가 예견되면서 인격(person) 논의의 필요성이 부각되고 있다. 왜냐하면, 앞으로 이들과 인간과의 사회적 관계가 전개되는 과정에서 제기될 수 있는 법적 또는 도덕적 차원의 문제들에 규범적으로 대응할 수 있는 이론의 가장 기초적인 논의가 인격 개념이기 때문이다. 따라서 본 논문은 인공지능 시대에 적합한 인격 개념을 모색하고자 한다. 전통적인 인격 개념에 대한 기존 해석은, 해당 존재가 합리성이나 쾌고 감수 능력(sentiment)과 같은 특정한 내재적 속성을 보유했는가와 관련되는데, 이러한 기존의 인격 논의는 인간종내에서도 식물인간과 (...) 같은 가장자리 상황(marginal cases) 인간을 어떻게 대우해야 할지에 대해 분명한 입장을 제시하지 못할 뿐만 아니라, 자연종인 동물을 배제할 때 제기되는 종차별주의(speciesism) 비판에 취약하다. 더욱이 이러한 기존의 인격 개념은 자연종에 토대를 두고 있어, 인공지능 로봇과 같은 인공종을 고려 대상에서 처음부터 배제한다는 문제가 있다. 본 논문은 인공지능 시대에 적합한 인격 개념은 인정(recognition)에 토대를 둔 논의라고 주장하고 인정에 근거한 모델을 제시한다. 인정에 근거한 인격 개념 모델은 전통적인 인격 개념의 정의를 유지하면서도 내재적 속성 해석이 갖는 한계를 극복할 수 있을 뿐만 아니라, 인정의 범위에 대한 확장 가능성, 인정 논의 세분화를 통한 인격 개념의 분화 설명, 효과적인 규범적 판단 기준 제시 등의 장점이 있다. (shrink)

Forms of Artificial Intelligence (AI) are already being deployed into clinical settings and research into its future healthcare uses is accelerating. Despite this trajectory, more research is needed regarding the impacts on patients of increasing AI decision making. In particular, the impersonal nature of AI means that its deployment in highly sensitive contexts-of-use, such as in healthcare, raises issues associated with patients’ perceptions of (un) dignified treatment. We explore this issue through an experimental vignette study comparing individuals’ perceptions (...) of being treated in a dignified and respectful way in various healthcare decision contexts. Participants were subject to a 2 (human or AI decision maker) x 2 (positive or negative decision outcome) x 2 (diagnostic or resource allocation healthcare scenario) factorial design. We found evidence of a “human bias” (i.e., a preference for human over AI decision makers) and an “outcome bias” (i.e., a preference for positive over negative outcomes). However, we found that for perceptions of respectful and dignified interpersonal treatment, it matters more who makes the decisions in diagnostic cases and it matters more what the outcomes are for resource allocation cases. We also found that humans were consistently viewed as appropriate decision makers and AI was viewed as dehumanizing, and that participants perceived they were treated better when subject to diagnostic as opposed to resource allocation decisions. Thematic coding of open-ended text responses supported these results. We also outline the theoretical and practical implications of these findings. (shrink)

This article links three rarely considered dimensions related to the implementation of artificial intelligence (AI)-based technologies in the form of predictive policing and discusses them in relation to liberal democratic societies. The three dimensions are the theoretical embedding and the workings of AI within anomic conditions (1), potential normative disorders emerging from them in the form of thinking errors and discriminatory practices (2) as well as the consequences of these disorders on the psychosocial, and emotional level (3). Against (...) this background, AI-induced anomie is conceptualized as a field of tension that refers to a systematic deterioration of democratic norms that are supposed to create ‘normative orders’, but which, when implemented through AI-supported measures, can reproduce existing discriminations, and establish new kinds of discriminatory relations. In future, these AI-based measures have the potential to lead to opposing normative disorders by emerging in the form of false social norms to an equally false Second Nature. They deprive persons involved of the possibility of individual appropriation of social norms and the specific emotional development associated with it. (shrink)

Feasting on a plethora of social media platforms, news aggregators, and online marketplaces, recommender systems (RSs) are spreading pervasively throughout our daily online activities. Over the years, a host of ethical issues have been associated with the diffusion of RSs and the tracking and monitoring of users’ data. Here, we focus on the impact RSs may have on personal autonomy as the most elusive among the often-cited sources of grievance and public outcry. On the grounds of a philosophically nuanced notion (...) of autonomy, we illustrate three specific reasons why RSs may limit or compromise it: the threat of manipulation and deception associated with RSs; the RSs’ power to reshape users’ personal identity; the impact of RSs on knowledge and critical thinking. In our view, however, notwithstanding these legitimate concerns, RSs may effectively help users to navigate an otherwise overwhelming landscape. Our perspective, therefore, is not to be intended as a bulwark to protect the _status quo_ but as an invitation to carefully weigh these aspects in the design of ethically oriented RSs. (shrink)

The goal of this paper is to describe the mechanism of the public perception of risk of artificial intelligence. For that we apply the social amplification of risk framework to the public perception of artificial intelligence using data collected from Twitter from 2007 to 2018. We analyzed when and how there appeared a significant representation of the association between risk and artificial intelligence in the public awareness of artificial intelligence. A significant (...) finding is that the image of the risk of AI is mostly associated with existential risks that became popular after the fourth quarter of 2014. The source of that was the public positioning of experts who happen to be the real movers of the risk perception of AI so far instead of actual disasters. We analyze here how this kind of risk was amplified, its secondary effects, what are the varieties of risk unrelated to existential risk, and what is the dynamics of the experts in addressing their concerns to the audience of lay people. (shrink)

In their thought-provoking article, Sedlakova and Trachsel (2023) defend the view that the status—both epistemic and ethical—of Conversational Artificial Intelligence (CAI) used in psychotherapy is complicated. While therapeutic CAI seems to be more than a mere tool implementing particular therapeutic techniques, it falls short of being a “digital therapist.” One of the main arguments supporting the latter claim is that even though “the interaction with CAI happens in the course of conversation… the conversation is profoundly different from a (...) conversation with a human therapist” (Sedlakova and Trachsel 2023, 8). In particular, unlike a human therapist, CAI cannot help its users gain new insight and self-understanding (Sedlakova and Trachsel 2023). We agree that currently available therapeutic CAI cannot be considered a “digital therapist,” however, we think that the issue surrounding the acquisition of new self-understanding in the interaction with therapeutic CAI is more complicated than Sedlakova and Trachsel suggest. (shrink)

Previous studies have paid more attention to the impact of non-balanced reciprocity in the organization on employees’ behaviors and outcomes, and have expected that the reciprocity norm could improve employees’ compliance behavior. However, there are two distinct types of non-balanced reciprocity, and whether generalized reciprocity affects employees’ compliance behavior rather than negative reciprocity and its mechanisms has not been further explored so far. Building on the social exchange theory and cognitive appraisal theory, we established and examined a model in a (...) scenario-based experiment across a two-stage survey of 316 participants. In this article, we propose that generalized reciprocity positively influences employees’ compliance behavior, and thriving at work mediates its relationship. Furthermore, we argue that the positive association between generalized reciprocity and thriving at work is moderated by the perceived cognitive capabilities of artificial intelligence. This association is amplified for people high in the perceived cognitive capabilities of AI. We also propose that the positive association between thriving at work and compliance behavior is moderated by conscientiousness, such that the association is amplified for people high in conscientiousness. These findings have theoretical and practical implications. (shrink)

Increasing concerns have been raised regarding artificial intelligence (AI) bias, and in response, efforts have been made to pursue AI fairness. In this paper, we argue that the idea of structural injustice serves as a helpful framework for clarifying the ethical concerns surrounding AI bias—including the nature of its moral problem and the responsibility for addressing it—and reconceptualizing the approach to pursuing AI fairness. Using AI in healthcare as a case study, we argue that AI bias is a (...) form of structural injustice that exists when AI systems interact with other social factors to exacerbate existing social inequalities, making some groups of people more vulnerable to undeserved burdens while conferring unearned benefits to others. The goal of AI fairness, understood this way, is about pursuing a more just social structure with the development and usage of AI systems when appropriate. We further argue that all participating agents in the unjust social structure associated with AI bias bear a shared responsibility to join collective action with the goal of reforming the social structure, and we provide a list of practical recommendations for agents in various social positions to contribute to this collective action. (shrink)

The target article by Laacke et al. focuses on the specific context of identifying people in social media with a high risk of depression by using artificial intelligence technologies. I...

Applications of artificial intelligence/machine learning in health care are dynamic and rapidly growing. One strategy for anticipating and addressing ethical challenges related to AI/ml for health care is patient and public involvement in the design of those technologies – often referred to as ‘co-design’. Co-design has a diverse intellectual and practical history, however, and has been conceptualized in many different ways. Moreover, AI/ml introduces challenges to co-design that are often underappreciated. Informed by perspectives from critical data studies and (...) critical digital health studies, we review the research literature on involvement in health care, and involvement in design, and examine the extent to which co-design as commonly conceptualized is capable of addressing the range of normative issues raised by AI/ml for health care. We suggest that AI/ml technologies have amplified and modified existing challenges related to patient and public involvement, and created entirely new challenges. We outline three pitfalls associated with co-design for ethical AI/ml for health care and conclude with suggestions for addressing these practical and conceptual challenges. (shrink)

Sedlakova and Traschel argue that conversational artificial intelligence (CAI) is more than a mere tool, but not quite an agent, as it “simulates having a therapeutic conversation [but] does not re...

Artificial Neural Networks have reached “grandmaster” and even “super-human” performance across a variety of games, from those involving perfect information, such as Go, to those involving imperfect information, such as “Starcraft”. Such technological developments from artificial intelligence (AI) labs have ushered concomitant applications across the world of business, where an “AI” brand-tag is quickly becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong—an autonomous vehicle crashes, a chatbot exhibits “racist” behavior, (...) automated credit-scoring processes “discriminate” on gender, etc.—there are often significant financial, legal, and brand consequences, and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that “... all the impressive achievements of deep learning amount to just curve fitting.” The key, as Pearl suggests, is to replace “reasoning by association” with “causal reasoning” —the ability to infer causes from observed phenomena. It is a point that was echoed by Gary Marcus and Ernest Davis in a recent piece for theNew York Times: “we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets—often using an approach known as ‘Deep Learning’—and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space, and causality.” In this paper, foregrounding what in 1949 Gilbert Ryle termed “a category mistake”, I will offer an alternative explanation for AI errors; it is not so much that AI machinery cannot “grasp” causality, but that AI machinery (qua computation) cannot understand anything at all. (shrink)

Artificial Intelligence (AI) is set to bring changes to legal systems. These technologies may have positive practical implications when it comes to access, efficiency, and accuracy in Justice. However, there are still many uncertainties and challenges associated with the implementation of AI in the legal space. In this research, we surveyed Judges on critical challenges related to the Judging Profession in the AI paradigm; Automated Adjudication; and Legal Principles. Our results suggest that (i) Judges are hesitant about changes (...) in their profession. They signal the need for adequate training that fosters legal literacy in AI, but are less open to changes in legal writing or their social and institutional role; (ii) Judges believe higher levels of automation only lead to fair outcomes if used in earlier phases of adjudication; (iii) Judges believe and are concerned about AI leading to Techno-Legal Positivism; and (iv) Judges consider that Legal AI technologies may have a positive impact in some legal principles, as long as everyone has equal access to those technologies and cybersecurity and judge on the loop safeguards are in place; and (v) Judges are strongly concerned about the de-humanization of Justice. They consider that assessing evidence, analyzing arguments, and deciding on a legal case should be inherently human. By surveying these practitioners, we aim to foster a responsible, inclusive, and transparent innovation in Justice. (shrink)

This article is devoted to discussion of the philosophical aspects of the problems associated with the creation of a strong artificial intelligence. It is noted that even taking into account the fact that the study of the brain and consciousness are represented by separate areas, such as cognitive neuroscience and philosophy of consciousness, there are a number of problems of an ontological nature, which indicate that research in the field of creating systems with artificial intelligence, although (...) actively developing, is rather related to its weak definition and does not provide prerequisites for confidence in the possibility of creating a strong artificial intelligence. (shrink)

This paper investigates the claim that artificial Intelligence Systems cannot be held morally responsible because they do not have an ability for agential self-awareness e.g. they cannot be aware that they are the agents of an action. The main suggestion is that if agential self-awareness and related first person representations presuppose an awareness of a self, the possibility of responsible artificial intelligence systems cannot be evaluated independently of research conducted on the nature of the self. Focusing (...) on a specific account of the self from the phenomenological tradition, this paper suggests that a minimal necessary condition that artificial intelligence systems must satisfy so that they have a capability for self-awareness, is having a minimal self defined as ‘a sense of ownership’. As this sense of ownership is usually associated with having a living body, one suggestion is that artificial intelligence systems must have similar living bodies so they can have a sense of self. Discussing cases of robotic animals as examples of the possibility of artificial intelligence systems having a sense of self, the paper concludes that the possibility of artificial intelligence systems having a ‘sense of ownership’ or a sense of self may be a necessary condition for having responsibility. (shrink)

In this article, we analyse the role that artificial intelligence (AI) could play, and is playing, to combat global climate change. We identify two crucial opportunities that AI offers in this domain: it can help improve and expand current understanding of climate change, and it can contribute to combatting the climate crisis effectively. However, the development of AI also raises two sets of problems when considering climate change: the possible exacerbation of social and ethical challenges already associated with (...) AI, and the contribution to climate change of the greenhouse gases emitted by training data and computation-intensive AI systems. We assess the carbon footprint of AI research, and the factors that influence AI’s greenhouse gas (GHG) emissions in this domain. We find that the carbon footprint of AI research may be significant and highlight the need for more evidence concerning the trade-off between the GHG emissions generated by AI research and the energy and resource efficiency gains that AI can offer. In light of our analysis, we argue that leveraging the opportunities offered by AI for global climate change whilst limiting its risks is a gambit which requires responsive, evidence-based, and effective governance to become a winning strategy. We conclude by identifying the European Union as being especially well-placed to play a leading role in this policy response and provide 13 recommendations that are designed to identify and harness the opportunities of AI for combatting climate change, while reducing its impact on the environment. (shrink)