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We stand at the threshold of a revolution in science. It is possible that soon scientific writing as we know it will undergo a rapid change, all thanks to the ChatGPT 3.5 language model. The appearance of this tool on the technology market in November 2022 caused a massive stir among Internet users and the academic world. It turned out that chat has the potential to generate completely new and unique scientific texts. In the wake of ChatGPT, similar tools by technology giants came and are coming. Given the above, the scientific community has only a brief moment to try to answer the following questions: whether the texts generated in this way have real scientific value, whether it is ethical for researchers to use them, how to regulate copyright on the use of AI, what are the potential capabilities of artificial intelligence for writing scientific papers. The indicated themes are reflected in the presented text. [ABSTRACT FROM AUTHOR]

This paper investigates the complex interplay between generative artificial intelligence (AI) and human intellect in academic writing and publishing. It examines the 'organic versus synthetic' paradox, emphasizing the implications of using generative AI tools in educational and academic integrity contexts. The paper critiques the prevalent 'publish or perish' culture in academia, highlighting the need for systemic reevaluation due to generative AI's emerging role in academic writing and reporting. It delves into the legal and ethical challenges of authorship and ownership, especially in relation to copyright laws and AI-generated content. The paper discusses generative AI's diverse roles and advocates for transparent reporting to uphold academic integrity. Additionally, it calls for a broader examination of generative AI tools and stresses the need for new mechanisms to identify generative AI use and ensure adherence to academic integrity and ethics. The implications of generative AI are also explored, suggesting the need for innovative AI-inclusive strategies in academia. The paper concludes by emphasizing the significance of generative AI in various information-processing domains, highlighting the urgency to adapt and transform academic practices in an era of rapid generative AI-driven change. [ABSTRACT FROM AUTHOR]

Purpose: The integration of artificial intelligence (AI) technologies like conversational AI and HR chatbots in international human resource development (HRD) presents both productivity benefits and ethical challenges. This study aims to examine the ethical dimensions of AI-driven HR chatbots, emphasizing the need for fairness, autonomy and nondiscrimination. It discusses inherent biases in AI systems and addresses linguistic, cultural and accessibility issues. The paper advocates for a comprehensive risk assessment approach to guide ethical integration, proposing a "risk management by design" framework. By embracing ethical principles and robust risk management strategies, organizations can navigate AI-driven HR technologies while upholding fairness and equity in global workforce management. Design/methodology/approach: Systematic literature review. Findings: The paper advocates for a comprehensive risk assessment approach to guide ethical integration, proposing a "risk management by design" framework. Practical implications: By embracing ethical principles and robust risk management strategies, organizations can navigate AI-driven HR technologies while upholding fairness and equity in global workforce management. Originality/value: This study explores the intricate ethical landscape surrounding AI-driven HR chatbots, spotlighting the imperatives of fairness, autonomy, and nondiscrimination. Uncovering biases inherent in AI systems, it addresses linguistic, cultural, and accessibility concerns. Proposing a pioneering "risk management by design" framework, the study advocates for a holistic approach to ethical integration, ensuring organizations navigate the complexities of AI-driven HR technologies while prioritizing fairness and equity in global workforce management. [ABSTRACT FROM AUTHOR]

If psychometrics has long concerned itself with validity, reliability, and fairness, then what could psychometrics learn from the cybernetic theories of AI? Through engagement with Burstein's (2023) Responsible AI Standards, this paper unpacks some paradigmatic differences between psychometrics and cybernetics, points to how recursivity and contingency are both a challenge and opportunity for psychometrics, and how this matters epistemologically, ethically and politically. Following these epistemological differences, the paper raises ethico-political concerns with the promise of the "human-in-the-loop". [ABSTRACT FROM AUTHOR]

This article discusses the application of artificially intelligent robots within eldercare and explores a series of ethical considerations, including the challenges that AI (Artificial Intelligence) technology poses to traditional Chinese Confucian filial piety. From the perspective of Confucian ethics, the paper argues that robots cannot adequately fulfill duties of care. Due to their detachment from personal relationships and interactions, the "emotions" of AI robots are merely performative reactions in different situations, rather than actual emotional abilities. No matter how "humanized" robots become, it is difficult to establish genuine empathy and a meaningful relationship with them for this reason. Even so, we acknowledge that AI robots are a significant tool in managing the demands of elder care and the growth of care poverty, and as such, we attempt to outline some parameters within which care robotics could be acceptable within a Confucian ethical system. Finally, the paper discusses the social impact and ethical considerations brought on by the interaction between humans and machines. It is observed that the relationship between humans and technology has always had both utopian and dystopian aspects, and robotic elder care is no exception. AI caregiver robots will likely become a part of elder care, and the transformation of these robots from "service providers" to "companions" seems inevitable. In light of this, the application of AI-augmented robotic elder care will also eventually change our understanding of interpersonal relationships and traditional requirements of filial piety. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Issues related to artificial intelligence (AI) and ethics have gained much traction worldwide. The impact of AI on society has been extensively discussed. This study presents a bibliometric analysis of research results, citation relationships among researchers, and highly referenced journals on AI and ethics on a global scale. Papers published on AI and ethics were recovered from the Microsoft Academic Graph Collection data set, and the subject terms included "artificial intelligence" and "ethics." With 66 nations' researchers contributing to AI and ethics research, 1585 papers on AI and ethics were recovered, up to 5 July 2021. North America, Western Europe, and East Asia were the regions with the highest productivity. The top ten nations produced about 94.37% of the wide variety of papers. The United States accounted for 47.59% (286 articles) of all papers. Switzerland had the highest research production with a million-person ratio (1.39) when adjusted for populace size. It was followed by the Netherlands (1.26) and the United Kingdom (1.19). The most productive authors were found to be Khatib, O. (n = 10), Verner, I. (n = 9), Bekey, G. A. (n = 7), Gennert, M. A. (n = 7), and Chatila, R., (n = 7). Current research shows that research on artificial intelligence and ethics has evolved dramatically over the past 70 years. Moreover, the United States is more involved with AI and ethics research than developing or emerging countries. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) and other large language models are rapidly infiltrating the world of education and educational research. These new technological developments raise questions about use and ethics throughout the world of educational research, particularly for qualitative methods given the philosophical and structural foundations of its associated designs. This paper seeks to interrogate the perceived ethics around the use of AI in qualitative research and draws on survey data from qualitative researchers (n = 101) collected from April-May 2023. Findings indicate that researchers were more apt to embrace the use of AI for transcription purposes, and to a lesser extent for preliminary coding. Researchers from high research productivity (R1) universities were generally less accepting of AI's use in the research process than other researchers. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this article is to attempt to answer the question of key ethical aspects of the development of artificial intelligence considering its current relevance to CSR principles. Design/methodology/approach: The main method is the analysis of the subject literature, research results and reports analysis. Findings: This paper provides research results that the dominant theme in the design of CSR activities should be the effects of introducing AI into public life, especially protecting society from abuse by new technologies. Social implications: An overview of the achievements, positions and insights contained in selected EPTA reports as a representative of European communities focused on the evaluation of new technologies allows to spread practical value in life of society. Originality/value: The article provides insight into the considerations of basic scopes of implications of AI ethics principles on CSR. [ABSTRACT FROM AUTHOR]

This paper lays out the conceptual groundwork for a long-term project examining ethical issues raised when addressing the value of knowledge to a knowledge economy. The project includes a series of papers on specific topics that interrelate to the subjects of knowledge, ethics and organizations. While some of the planned articles for the project will have a practical focus, others, such as this one, will be conceptual in nature. The following outlines selected key concepts for an ethics of knowledge and their relationship with cognate areas of inquiry and practice. [ABSTRACT FROM AUTHOR]

In the era of generative AI and specifically large language models (LLMs), exemplified by ChatGPT, the intersection of artificial intelligence and human reasoning has become a focal point of global attention. Unlike conventional search engines, LLMs go beyond mere information retrieval, entering into the realm of discourse culture. Their outputs mimic well-considered, independent opinions or statements of facts, presenting a pretense of wisdom. This paper explores the potential transformative impact of LLMs on democratic societies. It delves into the concerns regarding the difficulty in distinguishing ChatGPT-generated texts from human output. The discussion emphasizes the essence of authorship, rooted in the unique human capacity for reason—a quality indispensable for democratic discourse and successful collaboration within free societies. Highlighting the potential threats to democracy, this paper presents three arguments: the Substitution argument, the Authenticity argument, and the Facts argument. These arguments highlight the potential risks that are associated with an overreliance on LLMs. The central thesis posits that widespread deployment of LLMs may adversely affect the fabric of a democracy if not comprehended and addressed proactively and properly. In proposing a solution, we advocate for an emphasis on education as a means to mitigate risks. We suggest cultivating thinking skills in children, fostering coherent thought formulation, and distinguishing between machine-generated output and genuine, i.e., human, reasoning. The focus should be on the responsible development and usage of LLMs, with the goal of augmenting human capacities in thinking, deliberating and decision-making rather than substituting them. [ABSTRACT FROM AUTHOR]

With the increasing influence of machine learning algorithms in decision-making processes, concerns about fairness have gained significant attention. This area now offers significant literature that is complex and hard to penetrate for newcomers to the domain. Thus, a mapping study of articles exploring fairness issues is a valuable tool to provide a general introduction to this field. Our paper presents a systematic approach for exploring existing literature by aligning their discoveries with predetermined inquiries and a comprehensive overview of diverse bias dimensions, encompassing training data bias, model bias, conflicting fairness concepts, and the absence of prediction transparency, as observed across several influential articles. To establish connections between fairness issues and various issue mitigation approaches, we propose a taxonomy of machine learning fairness issues and map the diverse range of approaches scholars developed to address issues. We briefly explain the responsible critical factors behind these issues in a graphical view with a discussion and also highlight the limitations of each approach analyzed in the reviewed articles. Our study leads to a discussion regarding the potential future direction in ML and AI fairness. [ABSTRACT FROM AUTHOR]

The paper tries to investigate the connection between AI and the people who make the discourse in English is a complicated one with many facets The following objectives are set to achieve the aim of the paper: 1. Shed light on Ethics and the Use of Discourse Makers in English 2. Morals and new media The methods of the research are quantitative and qualitative. Frequency analysis, discourse analysis, and other methods will be used to study our corpora. [ABSTRACT FROM AUTHOR]

This research paper explores the use of AI, specifically ChatGPT, in interactive activities within online shopping and digital marketing. The paper examines the potential of AIpowered chatbots to enhance customer engagement, personalization, and marketing effectiveness. Through a literature review, case studies, and user feedback analysis, the research highlights the benefits of AI chatbots in providing real-time assistance, personalized recommendations, and improved customer experiences. The paper also addresses ethical and privacy concerns associated with AI implementation and discusses future research directions. The paper suggests that AI-powered chatbots have the potential to revolutionize online shopping and digital marketing by offering enhanced customer engagement, optimizing marketing strategies, and addressing customer needs. E-commerce preferences of human-mediated services regarding the application of AI in frontline services will be examined. [ABSTRACT FROM AUTHOR]

This paper examines how AI ethics has been developed at the national level in Japan, and what this process reveals about broader Japanese state imaginaries of how advanced technology should be developed and used, and what a future with these technologies should look like. Key developments in the Japanese government's approach to AI ethics and governance between 2014 and 2023 are laid out, based on an analysis of official reports and policy documents supplemented by data collected via semi-structured interviews with three expert members of the committees that formulated several key sets of ethical principles. The paper considers Japan's positioning in the global race to develop AI ethics principles over this period, as well as the imaginary of AI within the wider historical context of imaginaries about the knowledge society in Japan. I suggest three ways in which AI ethics has been understood and instrumentalized in the Japanese context, and argue that the main methodology used to date—ELSI—complements the government's utopian and techno-determinist imaginaries of the future while concealing a deeply conservative approach that serves to reproduce structural inequalities and discrimination despite the apparent internationalism and progressive values that are repeatedly expressed in state-promoted ethical principles. [ABSTRACT FROM AUTHOR]

As a result of globalization, and modern scientific and technological development, information technologies began to cover an increasing number of areas, including politics. The issue actualizes as we enter the Artificial Intelligence (AI) era. In times of AI, increasing scientific influence on politics was manifested in the development of a new scientific field — Digital Diplomacy. The development of AI and the changes taking place today can transform the political processes, imposing new threats to the political systems and the entire system of international relations. In this context, ethical issues are of particular concern because they can be blurred by the political decisions made by digital/computer program algorithms; that is, giving power to computer programs to make political decisions can destabilize the political system. This issue is particularly sensitive in terms of ensuring justice and protection of state political interests. The presented paper aims to discuss political transformation in the context of the modern digitalization process, the work of political institutions, and digital diplomacy. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has the potential to revolutionize healthcare, for example via decision support systems, computer vision approaches, or AI-based prevention tools. Initial results from AI applications in healthcare show promise but are rarely translated into clinical practice successfully and ethically. This occurs despite an abundance of "Trustworthy AI" guidelines. How can we explain the translational gaps of AI in healthcare? This paper offers a fresh perspective on this problem, showing that failing translation of healthcare AI markedly arises from a lack of an operational definition of "trust" and "trustworthiness". This leads to (a) unintentional misuse concerning what trust (worthiness) is and (b) the risk of intentional abuse by industry stakeholders engaging in ethics washing. By pointing out these issues, we aim to highlight the obstacles that hinder translation of Trustworthy medical AI to practice and prevent it from fulfilling its unmet promises. [ABSTRACT FROM AUTHOR]

The use of Artificial Intelligence and Big Data in health care opens up new opportunities for the measurement of the human. Their application aims not only at gathering more and better data points but also at doing it less invasive. With this change in health care towards its extension to almost all areas of life and its increasing invisibility and opacity, new questions of transparency arise. While the complex human-machine interactions involved in deploying and using AI tend to become non-transparent, the use of these technologies makes the patient seemingly transparent. Papers on the ethical implementation of AI plead for transparency but neglect the factor of the “transparent patient” as intertwined with AI. Transparency in this regard appears to be Janus-faced: The precondition for receiving help - e.g., treatment advice regarding the own health - is to become transparent for the digitized health care system. That is, for instance, to donate data and become visible to the AI and its operators. The paper reflects on this entanglement of transparent patients and (non-) transparent technology. It argues that transparency regarding both AI and humans is not an ethical principle per se but an infraethical concept. Further, it is no sufficient basis for avoiding harm and human dignity violations. Rather, transparency must be enriched by intelligibility following Judith Butler’s use of the term. Intelligibility is understood as an epistemological presupposition for recognition and the ensuing humane treatment. Finally, the paper highlights ways to testify intelligibility in dealing with AI in health care ex ante, ex post, and continuously. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is already integrated into news production strategies in some media outlets. Recently, generative AIs such as ChatGPT and others have demonstrated their ability to enhance productivity in content production tasks, raising the question of how journalism faculties can address this new technology. This paper presents an academic study on the application of AI in higher communication studies. The study involved 4 in-depth interviews and 28 semi-structured interviews with university lecturers and researchers. The findings confirm varying degrees of convergence and divergence on different aspects of the technology, including the integration of AI in communication faculties, student training in AI usage, the introduction of AI and journalism as a subject area, and the potential uses of AI in news production and consumption. Additionally, this paper proposes a comprehensive training program on AI and journalism, focusing on its foundations, technical competencies and ethical considerations. [ABSTRACT FROM AUTHOR]

This paper examines ways in which the ethics of data-driven technologies might be (re)politicised, particularly where educational institutions are involved. The recent proliferation of principles, guidelines, and frameworks for ethical 'AI' (artificial intelligence) have emerged from a plethora of organisations in recent years, and seem poised to impact educational governance. This trend will be firstly shown to align with a narrow form of ethics - deontology - and overlook other potential ways ethical reasoning might contribute to thinking about 'AI'. Secondly, the attention to ethical principles will be suggested to focus excessively on the technology itself, with the effect of masking political concerns for equity and justice. Thirdly and finally, the paper will propose a more radical form of participation in ethical decision-making that not only challenges the assumption of universal consensus, but also draws more authentically on the capacities for debate, contestation, and exchange inherent in the educational institution. [ABSTRACT FROM AUTHOR]

This contribution outlines a research proposal combining ethical guidelines on AI and a law-as-data approach. Building upon the definitions of soft law discussed in legal scholarship, it proposes a way of structuring the regulatory landscape on AI and of addressing the question of what is included in the "soft law of AI" today. By adopting a building-blocks approach (combining distinct definitional components of soft law), the paper shows that the state of current soft law on AI depends on which position on international law one defends. Concretely, the paper firstly offers a complete codebook for identifying the different types of soft law. Secondly, it applies this codebook as a proof-of-concept for the research proposal by analyzing 40+ ethical guidelines and by clustering preliminary results according to the actor enacting the guidelines and the legally relevant effects they could deploy. Four paradigmatic types of soft law emerge: statist and international organization soft law, process-oriented soft law, expertiseoriented soft law, and de facto relevant standards soft law. These results illustrate the contributions which are to be expected from a law-as-data research proposal. [ABSTRACT FROM AUTHOR]

This essay argues that the popular misrepresentation of the nature of AI has important consequences concerning how we view the need for regulations. Considering AI as something that exists in itself, rather than as a set of cognitive technologies whose characteristics—physical, cognitive, and systemic—are quite different from ours (and that, at times, differ widely among the technologies) leads to inefficient approaches to regulation. This paper aims at helping the practitioners of responsible AI to address the way in which the technical aspects of the tools they are developing and promoting directly have important social and political consequences. [ABSTRACT FROM AUTHOR]

Normative theorists of migration are beginning to shift their focus away from an earlier obsession with whether the 'liberal' or 'legitimate' state should have a right to exclude, and toward evaluation of how states engage in immigration control. However, with some notable exceptions – such as work of Rebecca Buxton, David Owen, Serena Parekh, and Alex Sager – this work tends not to focus on the global coordination of such control, and is still largely concerned with issues of membership. In this paper I aim to show the value of shifting normative attention to the fundamentally interdependent nature of state control of migration, and the management of all forms of movement – not just settlement. This global management is greatly facilitated by the rapid digitisation of border controls. As such, I outline three aspects of the way digital border controls work – profiling, biometric identification, and the data sharing practices upon which they rest – and highlight ethical challenges of accountability, consent and the reach of the state, and entrenching global inequalities in access to movement. Ultimately, I hope to show that the globally interconnected nature of migration management is a combination of practices that normative theorists of migration should turn their attention to. [ABSTRACT FROM AUTHOR]

The paper intends to propose a reading of Ian McEwan’s 2019 novel, Machines Like Me, qua insightful reflection on the topic of Artificial Intelligence and its bearings on different aspects of human life, from interpersonal relationships to moral behaviour. The novel also engages in a reflection on the value and the prospects of literature, whose very premises might be called in question in a posthuman context. Machines Like Me is set in a 1980s world whose contours radically deviate from historical facts. The novel introduces the simulacrum in the form of an android, Adam, a hypersophisticated machine that enters the characters’ life and upsets it thoroughly. The troublesome relationship with Adam forces Charlie, the protagonistnarrator, to ponder on his own system of values, posing questions about the Other which inevitably end up throwing new light on the Self and on what it ultimately means to be human. By way of his “What if novel” set in an alternative past, McEwan tackles pressing issues of our present, while trying to envisage a future that is not far to come. The paper intends to explore both the ethical and the metaliterary level of the story on the background of the contemporary theoretical debate on transhumanism and posthumanism, pointing to the simulacrum as the uncanny catalyser of the major topics the author intends to address. [ABSTRACT FROM AUTHOR]

This paper argues that the Value Sensitive Design (VSD) methodology provides a principled approach to embedding common values into AI systems both early and throughout the design process. To do so, it draws on an important case study: the evidence and final report of the UK Select Committee on Artificial Intelligence. This empirical investigation shows that the different and often disparate stakeholder groups that are implicated in AI design and use share some common values that can be used to further strengthen design coordination efforts. VSD is shown to be both able to distill these common values as well as provide a framework for stakeholder coordination. [ABSTRACT FROM AUTHOR]

Can artificial agents act? Conceptual constellation for a de-humanised theory of action This paper embarks on an exploration of the concept of agency, traditionally ascribed to humans, in the context of artificial intelligence (AI). In the first two sections, it challenges the conventional dichotomy of human agency and non- human instrumentality, arguing that advancements in technology have blurred these boundaries. In the third section, the paper introduces the reader to the philosophical perspective of new materialism, which assigns causal power to matter itself. This perspective suggests that agency is an emergent property of material configurations, prompting a re-evaluation of nonhuman agency. The fourth and fifth section revisit the legacy of cybernetics to understand systemic properties and feedback mechanisms, while re-admitting in the discourse also linear conditioning (discarded by new materialism) and assigning it a role in system dynamics. In the sixth section, in the light of the conceptual background examined so far, the paper proposes a revision of determinism (again partly in opposition to the new materialism and its indeterministic view) that can include both linear conditioning and circular interactions. The seventh section is devoted to propose a novel theory of action that includes AI systems – and artificial entities in general – as agents that can impact their environment and human systems. The exploration concludes with a discussion on the implications of this perspective for our understanding of action and responsibility in the age of AI.

Climate change is a global priority. In 2015, the United Nations (UN) outlined its Sustainable Development Goals (SDGs), which stated that taking urgent action to tackle climate change and its impacts was a key priority. The 2021 World Climate Summit finished with calls for governments to take tougher measures towards reducing their carbon footprints. However, it is not obvious how governments can make practical implementations to achieve this goal. One challenge towards achieving a reduced carbon footprint is gaining awareness of how energy exhaustive a system or mechanism is. Artificial Intelligence (AI) is increasingly being used to solve global problems, and its use could potentially solve challenges relating to climate change, but the creation of AI systems often requires vast amounts of, up front, computing power, and, thereby, it can be a significant contributor to greenhouse gas emissions. If governments are to take the SDGs and calls to reduce carbon footprints seriously, they need to find a management and governance mechanism to (i) audit how much their AI system 'costs' in terms of energy consumption and (ii) incentivise individuals to act based upon the auditing outcomes, in order to avoid or justify politically controversial restrictions that may be seen as bypassing the creativity of developers. The idea is thus to find a practical solution that can be implemented in software design that incentivises and rewards and that respects the autonomy of developers and designers to come up with smart solutions. This paper proposes such a sustainability management mechanism by introducing the notion of 'Sustainability Budgets'—akin to Privacy Budgets used in Differential Privacy—and by using these to introduce a 'Game' where participants are rewarded for designing systems that are 'energy efficient'. Participants in this game are, among others, the Machine Learning developers themselves, which is a new focus for this problem that this text introduces. The paper later expands this notion to sustainability management in general and outlines how it might fit into a wider governance framework. [ABSTRACT FROM AUTHOR]

A recent interpretation of artificial intelligence (AI) (Floridi 2013, 2022) suggests that the implementation of AI demands the investigation of the binding conditions that make it possible to build and integrate artifacts into our lived world. Such artifacts can successfully interact with the world because our environment has been designed to be compatible with intelligent machines (such as robots). As the use of AI becomes ubiquitous in society, possibly leading to the formation of increasingly intelligent bio-technological unions, there will likely be a coexistence of a plethora of micro-environments wrapped and tailored around humans and basic robots. The key element of this pervasive process will be the capacity to integrate biological realms in an infosphere suitable for the implementation of AI technologies. This process will require extensive datafication. This is because data is the basis of the logical-mathematical codes and models that drive and guide AI. This process will have huge consequences on workplaces, on workers, as well as on the decision-making processes required for the functioning of future societies. In this paper we will offer a comprehensive reflection on the moral and social implications of datafication as well as a set of considerations about its desirability, which will be informed by the following insights: (1) full protection of privacy may become structurally impossible, thus leading to undesirable forms of political and social control; (2) worker's freedom may be reduced; (3) human creativity, imagination, and even divergence from AI logic might be channeled and possibly discouraged; (4) there will likely be a push towards efficiency and instrumental reason, which will become preeminent in production lines as well as in society. [ABSTRACT FROM AUTHOR]

The increasing diffusion of novel digital and online sociotechnical systems for arational behavioral influence based on Artificial Intelligence (AI), such as social media, microtargeting advertising, and personalized search algorithms, has brought about new ways of engaging with users, collecting their data and potentially influencing their behavior. However, these technologies and techniques have also raised concerns about the potential for manipulation, as they offer unprecedented capabilities for targeting and influencing individuals on a large scale and in a more subtle, automated and pervasive manner than ever before. This paper, provides a narrative review of the existing literature on manipulation, with a particular focus on the role of AI and associated digital technologies. Furthermore, it outlines an account of manipulation based of four key requirements: intentionality, asymmetry of outcome, non-transparency and violation of autonomy. I argue that while manipulation is not a new phenomenon, the pervasiveness, automaticity, and opacity of certain digital technologies may raise a new type of manipulation, called "digital manipulation". I call "digital manipulation" any influence exerted through the use of digital technology that is intentionally designed to bypass reason and to produce an asymmetry of outcome between the data processor (or a third party that benefits thereof) and the data subject. Drawing on insights from psychology, sociology, and computer science, I identify key factors that can make manipulation more or less effective, and highlight the potential risks and benefits of these technologies for individuals and society. I conclude that manipulation through AI and associated digital technologies is not qualitatively different from manipulation through human–human interaction in the physical world. However, some functional characteristics make it potentially more likely of evading the subject's cognitive defenses. This could increase the probability and severity of manipulation. Furthermore, it could violate some fundamental principles of freedom or entitlement related to a person's brain and mind domain, hence called neurorights. To this end, an account of digital manipulation as a violation of the neuroright to cognitive liberty is presented. [ABSTRACT FROM AUTHOR]

The Editors-in-Chief of Communications in Information Literacy discuss the development of the journal's new generative artificial intelligence (AI) policy. [ABSTRACT FROM AUTHOR]

The ethics of robots and artificial intelligence (AI) typically centers on "giving ethics" to as-yet imaginary AI with human-levels of autonomy in order to protect us from their potentially destructive power. It is often assumed that to do that, we should program AI with the true moral theory (whatever that might be), much as we teach morality to our children. This paper argues that the focus on AI with human-level autonomy is misguided. The robots and AI that we have now and in the near future are "semi-autonomous" in that their ability to make choices and to act is limited across a number of dimensions. Further, it may be morally problematic to create AI with human-level autonomy, even if it becomes possible. As such, any useful approach to AI ethics should begin with a theory of giving ethics to semi-autonomous agents (SAAs). In this paper, we work toward such a theory by evaluating our obligations to and for "natural" SAAs, including nonhuman animals and humans with developing and diminished capacities. Drawing on research in neuroscience, bioethics, and philosophy, we identify the ways in which AI semi-autonomy differs from semi-autonomy in humans and nonhuman animals. We conclude on the basis of these comparisons that when giving ethics to SAAs, we should focus on principles and restrictions that protect human interests, but that we can only permissibly maintain this approach so long as we do not aim at developing technology with human-level autonomy. [ABSTRACT FROM AUTHOR]

As a means of preventive medicine early detection and prevention examinations can identify and treat possible health disorders or abnormalities from an early age onwards. However, pediatric examinations are often widely spaced, and thus only snapshots of the children’s and adolescents’ developments are obtained. With e-health applications parents and adolescents could record developmental parameters much more frequently and regularly and transmit data directly for ongoing evaluation. AI technologies could be used to search for new and previously unknown patterns. Although e-health applications could improve preventive healthcare, there are serious concerns about the unlimited use of big data in medicine. Such concerns range from general skepticism about big data in medicine to specific challenges and risks in certain medical areas. In this paper, we will focus on preventive health care in pediatrics and explore ethical implications of e-health applications. Specifically, we will address opportunities and risks of app-based data collection and AI-based data evaluation for complementing established early detection and prevention examinations. To this end, we will explore the principle of the best interest of the child. Furthermore, we shall argue that difficult trade-offs need to be made between group benefit on the one hand and individual autonomy and privacy on the other. [ABSTRACT FROM AUTHOR]

Healthcare organizations have realized that Artificial intelligence (AI) can provide a competitive edge through personalized patient experiences, improved patient outcomes, early diagnosis, augmented clinician capabilities, enhanced operational efficiencies, or improved medical service accessibility. However, deploying AI-driven tools in the healthcare ecosystem could be challenging. This paper categorizes AI applications in healthcare and comprehensively examines the challenges associated with deploying AI in medical practices at scale. As AI continues to make strides in healthcare, its integration presents various challenges, including production timelines, trust generation, privacy concerns, algorithmic biases, and data scarcity. The paper highlights that flawed business models and wrong workflows in healthcare practices cannot be rectified merely by deploying AI-driven tools. Healthcare organizations should re-evaluate root problems such as misaligned financial incentives (e.g., fee-for-service models), dysfunctional medical workflows (e.g., high rates of patient readmissions), poor care coordination between different providers, fragmented electronic health records systems, and inadequate patient education and engagement models in tandem with AI adoption. This study also explores the need for a cultural shift in viewing AI not as a threat but as an enabler that can enhance healthcare delivery and create new employment opportunities while emphasizing the importance of addressing underlying operational issues. The necessity of investments beyond finance is discussed, emphasizing the importance of human capital, continuous learning, and a supportive environment for AI integration. The paper also highlights the crucial role of clear regulations in building trust, ensuring safety, and guiding the ethical use of AI, calling for coherent frameworks addressing transparency, model accuracy, data quality control, liability, and ethics. Furthermore, this paper underscores the importance of advancing AI literacy within academia to prepare future healthcare professionals for an AI-driven landscape. Through careful navigation and proactive measures addressing these challenges, the healthcare community can harness AI's transformative power responsibly and effectively, revolutionizing healthcare delivery and patient care. The paper concludes with a vision and strategic suggestions for the future of healthcare with AI, emphasizing thoughtful, responsible, and innovative engagement as the pathway to realizing its full potential to unlock immense benefits for healthcare organizations, physicians, nurses, and patients while proactively mitigating risks. • This study categorizes AI applications in healthcare and analyzes their deployment challenges at scale in medical practices. • The paper emphasizes a cultural shift to view AI as a healthcare delivery enhancer and job creator, not a threat. • To integrate AI, investments in finance and human capital, continuous learning, and a supportive environment are needed. • Clear regulatory frameworks should be developed to build trust, ensure safety, and guide the ethical use of AI in healthcare. • The study provides strategic suggestions for responsible AI use in healthcare to maximize benefits and minimize risks. [ABSTRACT FROM AUTHOR]

Embodiment is typically given insufficient weight in debates concerning the moral status of Novel Synthetic Beings (NSBs) such as sentient or sapient Artificial Intelligences (AIs). Discussion usually turns on whether AIs are conscious or self-aware, but this does not exhaust what is morally relevant. Since moral agency encompasses what a being wants to do, the means by which it enacts choices in the world is a feature of such agency. In determining the moral status of NSBs and our obligations to them, therefore, we must consider how their corporeality shapes their options, preferences, values, and is constitutive of their moral universe. Analysing AI embodiment and the coupling between cognition and world, the paper shows why determination of moral status is only sensible in terms of the whole being, rather than mental sophistication alone, and why failure to do this leads to an impoverished account of our obligations to such NSBs. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is not new, but recent years have seen a growing concern about the technology's political, economic and social impact, including debates about its governance. This paper describes how an analysis of the technology's governance should build on the understanding of AI as the creation of artificial agents, and that the challenge that governance seeks to address is best understood as one of material agency. It describes how relevant rules can be systematically analyzed across different applications by considering the fundamental properties of artificial agents. The paper concludes by describing how the framework can be applied for further governance studies, and as a means to bridge insights across social science and technical perspectives on AI. [ABSTRACT FROM AUTHOR]

The rapid growth of AI systems has implications on a wide variety of fields. It can prove to be a boon to disparate fields such as healthcare, education, global logistics and transportation, to name a few. However, these systems will also bring forth far-reaching changes in employment, economy and security. As AI systems gain acceptance and become more commonplace, certain critical questions arise: What are the legal and security ramifications of the use of these new technologies? Who can use them, and under what circumstances? What is the safety of these systems? Should their commercialization be regulated? What are the privacy issues associated with the use of these technologies? What are the ethical considerations? Who has responsibility for the large amounts of data that is collected and manipulated by these systems? Could these systems fail? What is the recourse if there is a system failure? These questions are but a small subset of possible questions in this key emerging field. In this paper, we focus primarily on the legal questions that relate to the security, privacy, ethical, and policy considerations that emerge from one of these types of technologies, namely social robots. We begin with a history of the field, then go deeper into legal issues, the associated issues of security, privacy and ethics, and consider some solutions to these issues. Finally, we conclude with a look at the future as well as a modest proposal for future research addressing some of the challenges listed. [ABSTRACT FROM AUTHOR]

Trust management has become a fundamental requirement for Social Internet of Things (SIoT) to enable a trustworthy social network of smart objects necessary for enhancing the security and reliability of cyber-physical systems. To increase the credibility scores in trust management, AI (Artificial Intelligence) has been adopted. However, the current need for digital acceleration has brought ethical concerns related to the smartness and social consciousness of autonomous objects, which leads to a question whether AI-based trust management is ready to deal with these concerns. In this paper, we consider 11 ethical dimensions within the context of trust management in SIoT. Then, we examine the existing AI-based trust models in the context of SIoT and its related application domains to assess their maturity in terms of the 11 ethical dimensions. The evaluation results show how trust management can be improved by AI ethical principles in vehicular networks and underwater acoustic sensor networks. [ABSTRACT FROM AUTHOR]

In the era of generative AI and specifically large language models (LLMs), exemplified by ChatGPT, the intersection of artificial intelligence and human reasoning has become a focal point of global attention. Unlike conventional search engines, LLMs go beyond mere information retrieval, entering into the realm of discourse culture. Their outputs mimic well-considered, independent opinions or statements of facts, presenting a pretense of wisdom. This paper explores the potential transformative impact of LLMs on democratic societies. It delves into the concerns regarding the difficulty in distinguishing ChatGPT-generated texts from human output. The discussion emphasizes the essence of authorship, rooted in the unique human capacity for reason—a quality indispensable for democratic discourse and successful collaboration within free societies. Highlighting the potential threats to democracy, this paper presents three arguments: the Substitution argument, the Authenticity argument, and the Facts argument. These arguments highlight the potential risks that are associated with an overreliance on LLMs. The central thesis posits that widespread deployment of LLMs may adversely affect the fabric of a democracy if not comprehended and addressed proactively and properly. In proposing a solution, we advocate for an emphasis on education as a means to mitigate risks. We suggest cultivating thinking skills in children, fostering coherent thought formulation, and distinguishing between machine-generated output and genuine, i.e., human, reasoning. The focus should be on the responsible development and usage of LLMs, with the goal of augmenting human capacities in thinking, deliberating and decision-making rather than substituting them.