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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]

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]

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 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]

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]

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]

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]

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]

AI research is growing rapidly raising various ethical issues related to safety, risks, and other effects widely discussed in the literature. We believe that in order to adequately address those issues and engage in a productive normative discussion it is necessary to examine key concepts and categories. One such category is anthropomorphism. It is a well-known fact that AI's functionalities and innovations are often anthropomorphized (i.e., described and conceived as characterized by human traits). The general public's anthropomorphic attitudes and some of their ethical consequences (particularly in the context of social robots and their interaction with humans) have been widely discussed in the literature. However, how anthropomorphism permeates AI research itself (i.e., in the very language of computer scientists, designers, and programmers), and what the epistemological and ethical consequences of this might be have received less attention. In this paper we explore this issue. We first set the methodological/theoretical stage, making a distinction between a normative and a conceptual approach to the issues. Next, after a brief analysis of anthropomorphism and its manifestations in the public, we explore its presence within AI research with a particular focus on brain-inspired AI. Finally, on the basis of our analysis, we identify some potential epistemological and ethical consequences of the use of anthropomorphic language and discourse within the AI research community, thus reinforcing the need of complementing the practical with a conceptual analysis. [ABSTRACT FROM AUTHOR]

Any space program involving long-term human missions will have to cope with serious risks to human health and life. Because currently available countermeasures are insufficient in the long term, there is a need for new, more radical solutions. One possibility is a program of human enhancement for future deep space mission astronauts. This paper discusses the challenges for long-term human missions of a space environment, opening the possibility of serious consideration of human enhancement and a fully automated space exploration, based on highly advanced AI. The author argues that for such projects, there are strong reasons to consider human enhancement, including gene editing of germ line and somatic cells, as a moral duty. [ABSTRACT FROM AUTHOR]

Trust is believed to be a foundational cornerstone for artificial intelligence (AI). In April 2019 the European Commission High Level Expert Group on AI adopted the Ethics Guidelines for Trustworthy AI, stressing that human beings will only be able to confidently and fully reap the benefits of AI if they can trust the technology. Trustworthy AI is defined as ethical, lawful and robust AI. Three things strike me about the EC Guidelines. Firstly, though building trust in AI seems to be a shared aim, it is not explicated what trust is, and how it can be built and maintained. Secondly, the Guidelines ignore the widespread distinction made in philosophical literature between trust and reliance. Thirdly, it is not clear how the values have been selected with which AI has to align and what would happen if they came into conflict. In this paper, I shall provide a conceptual analysis of trust in contrast to reliance and ask when it is warranted to talk about trust in AI and trustworthy AI. I shall show how trust and risk are related and what benefits and risks are associated with narrow and general AI. Also, I shall point out that metaphorical talk about ethically aligned AI ignores the real disagreements we have about ethical values. [ABSTRACT FROM AUTHOR]

As highly intelligent autonomous robots are gradually introduced into the home and workplace, ensuring public safety becomes extremely important. Given that such machines will learn from interactions with their environment, standard safety engineering methodologies may not be applicable. Instead, we need to ensure that the machines themselves know right from wrong; we need moral mechanisms. Morality, however, has traditionally been considered a defining characteristic, indeed the sole realm of human beings; that which separates us from animals. But if only humans can be moral, can we build safe robots? If computationalism-roughly the thesis that cognition, including human cognition, is fundamentally computational-is correct, then morality cannot be restricted to human beings (since equivalent cognitive systems can be implemented in any medium). On the other hand, perhaps there is something special about our biological makeup that gives rise to morality, and so computationalism is effectively falsified. This paper examines these issues by looking at the nature of morals and the influence of biology. It concludes that moral behaviour is concerned solely with social well-being, independent of the nature of the individual agents that comprise the group. While our biological makeup is the root of our concept of morals and clearly affects human moral reasoning, there is no basis for believing that it will restrict the development of artificial moral agents. The consequences of such sophisticated artificial mechanisms living alongside natural human ones are also explored. [ABSTRACT FROM AUTHOR]