Generative AI technologies have the potential to be socially and politically transformative. In this paper, we focus on exploring the potential impacts that Generative AI could have on the functioning of our democracies and the nature of citizenship. We do so by drawing on accounts of deliberative democracy and the deliberative virtues associated with it, as well as the reciprocal impacts that social media and Generative AI will have on each other and the broader information landscape. Drawing (...) on this background theory, we outline some of the key positive and negative impacts that Generative AI is likely to have on democratic citizenship. The political significance of these impacts suggests the need for further regulation. (shrink)

The proliferation of Generative Artificial Intelligence (Generative AI) has led to an increased reliance on AI‐generated content for designing and deploying digital health interventions. While generative AI has the potential to facilitate and automate healthcare, there are concerns that AI‐generated content and AI‐generated health advice could trigger, perpetuate, or exacerbate prior traumatic experiences among vulnerable populations. In this discussion article, I examined how generative‐AI‐powered digital health interventions could trigger, perpetuate, or exacerbate emotional trauma among vulnerable populations (...) who rely on digital health interventions as complementary or alternative sources of seeking health services or information. I then proposed actionable strategies for mitigating AI‐generated trauma in the context of digital health interventions. The arguments raised in this article are expected to shift the focus of AI practitioners against prioritizing dominant narratives in AI algorithms into seriously considering the needs of vulnerable minority groups who are at the greatest risk for trauma but are often invisible in AI data sets, AI algorithms, and their resultant technologies. (shrink)

Generative AI programs can produce high-quality written and visual content that may be used for good or ill. We argue that a credit–blame asymmetry arises for assigning responsibility for these outputs and discuss urgent ethical and policy implications focused on large-scale language models.

The complexity and emergent autonomy of Generative AI systems introduce challenges in predictability and legal compliance. This paper analyses some of the legal and regulatory implications of such challenges in the European Union context, focusing on four areas: liability, privacy, intellectual property, and cybersecurity. It examines the adequacy of the existing and proposed EU legislation, including the Artificial Intelligence Act (AIA), in addressing the challenges posed by Generative AI in general and LLMs in particular. The paper identifies potential (...) gaps and shortcomings in the EU legislative framework and proposes recommendations to ensure the safe and compliant deployment of generative models. (shrink)

Generative AI appears to threaten argument creativity. Because of its capacity to generate coherent texts, individuals are likely to integrate its ideas, and not their own, into arguments, thereby reducing their creative contribution. This article argues that this view is mistaken—it rests on a misunderstanding of the nature of creativity. Within arguments, creative and critical thinking cannot be separated. Because creativity is enmeshed with skills such as analysis and evaluation, the use of generative AI in the construction of (...) arguments, especially in the role as universal audience, has the potential to heighten, not diminish argument creativity. (shrink)

The new wave of ‘foundation models’—general-purpose generative AI models, for production of text (e.g., ChatGPT) or images (e.g., MidJourney)—represent a dramatic advance in the state of the art for AI. But their use also introduces a range of new risks, which has prompted an ongoing conversation about possible regulatory mechanisms. Here we propose a specific principle that should be incorporated into legislation: that any organization developing a foundation model intended for public use must demonstrate a reliable detection mechanism for (...) the content it generates, as a condition of its public release. The detection mechanism should be made publicly available in a tool that allows users to query, for an arbitrary item of content, whether the item was generated (wholly or partly) by the model. In this paper, we argue that this requirement is technically feasible and would play an important role in reducing certain risks from new AI models in many domains. We also outline a number of options for the tool’s design, and summarize a number of points where further input from policymakers and researchers would be required. (shrink)

Purpose This paper aims to critically examine the implications of using generative artificial intelligence (AI) models, such as ChatGPT and Bard, in social science research. It examines the doppelganger effect in AI-driven studies as well as cognitive dissonance brought on by the autonomy of these tools. The discussion also addresses the debate between quantitative and qualitative methods for evaluating AI-driven research, scrutinising existing guidelines for accountability and validity. In addition, the paper considers the potential for generative AI to (...) dominate research, identifying “non-takeoverable” skills and ethical issues in AI-driven knowledge production. Design/methodology/approach This work primarily focuses on research articles for conceptual clarity, while news media reports are used to illustrate current scenarios. Findings The doppelganger effect makes people worry about situations in which AI copies existing work so well that it becomes possible for people to give the wrong credit. This has led to a critical review of ways to make sure that the outputs of generative AI are real and original. Generative AI can enhance data collection and analysis, offering alternative approaches to traditional research methodologies. By leveraging the capabilities of generative AI, researchers can potentially uncover new insights and perspectives from their data. Originality/value It is crucial to acknowledge the ethical concerns associated with using generative AI in social science research. The deployment of such technology introduces the possibility of biases and other ethical challenges that may impact the cognitive abilities of human participants or researchers involved in the research process. The work makes an effort by encouraging ethical consideration and highlighting crucial human abilities that are still necessary, providing a novel viewpoint on the use of generative AI in research approaches. (shrink)

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

There is a history of thinking that photographs provide a special kind of access to the objects depicted in them, beyond the access that would be provided by a painting or drawing. What is included in the photograph does not depend on the photographer’s beliefs about what is in front of the camera. This feature leads Kendall Walton to argue that photographs literally allow us to see the objects which appear in them. Current generative algorithms produce images in response (...) to users’ text prompts. Depending on the parameters, the output can resemble specific people or things which are named in the prompt. This resemblance does not depend on the user’s beliefs, so generated images are in this sense like photographs. Given this parallel, how should we think about AI-generated images? (shrink)

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

Artificial intelligence (AI) and its introduction into clinical pathways presents an array of ethical issues that are being discussed in the JME. 1–7 The development of AI technologies that can produce text that will pass plagiarism detectors 8 and are capable of appearing to be written by a human author 9 present new issues for medical ethics. One set of worries concerns authorship and whether it will now be possible to know that an author or student in fact produced submitted (...) work. That seems likely to be a general worry for secondary and higher education, as well as for all academic journals. Thus far generative AI chatbots do not seem to be able to produce a fully referenced and well-argued ethics article, but they probably could generate a blog or student essay that would... (shrink)

OpenAI’s Chat Generative Pre-training Transformer (ChatGPT), Google’s Bard and other generative artificial intelligence (GenAI) technologies can greatly enhance the capability of healthcare profess...

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

This book explores the revolutionary intersection of Generative AI (GenAI) and cybersecurity. It presents a comprehensive guide that intertwines theories and practices, aiming to equip cybersecurity professionals, CISOs, AI researchers, developers, architects and college students with an understanding of GenAI’s profound impacts on cybersecurity. The scope of the book ranges from the foundations of GenAI, including underlying principles, advanced architectures, and cutting-edge research, to specific aspects of GenAI security such as data security, model security, application-level security, and the emerging (...) fields of LLMOps and DevSecOps. It explores AI regulations around the globe, ethical considerations, the threat landscape, and privacy preservation. Further, it assesses the transformative potential of GenAI in reshaping the cybersecurity landscape, the ethical implications of using advanced models, and the innovative strategies required to secure GenAI applications. Lastly, the book presents an in-depth analysis of the security challenges and potential solutions specific to GenAI, and a forward-looking view of how it can redefine cybersecurity practices. By addressing these topics, it provides answers to questions on how to secure GenAI applications, as well as vital support with understanding and navigating the complex and ever-evolving regulatory environments, and how to build a resilient GenAI security program. The book offers actionable insights and hands-on resources for anyone engaged in the rapidly evolving world of GenAI and cybersecurity. (shrink)

Despite the many successes of generative AI, a number of fundamental questions have begun to arise around this technology. There is undoubtedly an interesting situation from a philosophical point of view. It can be carefully assumed that contemporary digital information processing technologies have arisen inside a circle of civilisation, one of the foundations of which is the classical account of truth. This account, even if seen as ideal and absolute, nevertheless seems to be a driving force in the field (...) of science. Is it possible that generative AI will result in the replacement of the pursuit of truth by a mere uncritical adherence to the results provided by AI systems, as well as the inhibition of lively creativity both scientific and artistic? (shrink)

Generative artificial intelligence (GenAI) promises to revolutionize data-driven fields (Milmo 2023). Building on decades of large language modeling (LLM) (Toner 2023), GenAI can collect, harmonize...

This article examines the potential parallels between children's widespread adoption of smartphones and the emerging reliance on generative AI tools in childhood education. Drawing on Jonathan Haidt’s insights into how phone-based childhoods can disrupt the development of critical executive functions, and Shannon Vallor’s concept of “moral deskilling,” the discussion raises concerns about “intellectual deskilling” in younger generations. As generative AI tools like ChatGPT gain popularity, children risk becoming overly reliant on automated solutions, potentially undermining metacognition and critical thinking. (...) This paper highlights risks such as cognitive offloading, instant gratification, and diminished perseverance and proposes measures to ensure generative AI supports rather than replaces essential developmental experiences. (shrink)

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

Generative Artificial Intelligence (AI) tools produce hallucinations exposing developers and users to a myriad of liabilities in courts. Given the absence of strict laws and regulations structuring how Generative AI content interact with potential allegations of defamation, libel, and slander, judges and attorneys are left with the semiotics of the fragmented articles and rules in each system attempting to settle such cases. The endless interpretations of written and non-verbal signs in the law across the world constitutes a new (...) realm for legal semiotics in the area of Generative AI and defamation. The present analysis examines existing civil liability articles in the Jordanian code to shed light on the litigation and defenses afforded in the defamation realm with respect to Generative AI content. The key finding is that like other countries, civil codes in Jordan are outdated concerning Generative AI content opening a plethora of liability scenarios in libel or slander cases. More importantly, the exercise of semiotic interpretation generates several defenses salvaging users and developers under strict sets of conditions. In sum, new amendments and updated legislative frameworks are needed to protect the healthy development of Generative AI while preserving citizens from damaging defamation, libel, and slander. (shrink)

The integration of generative AI tools in higher education presents both significant opportunities and challenges. This paper addresses two key questions: how AI should be incorporated into higher education and what ethical and pedagogical principles should guide its use. While AI enhances creativity, efficiency, and personalized learning, it also raises concerns about over-reliance, bias, and ethical implications. Through a comprehensive review of existing literature, this study examines the ethical and pedagogical impact of AI in education. Findings suggest that institutions (...) must ensure AI complements traditional learning, uphold academic integrity, and promote critical thinking. Human-AI collaboration and equitable access are essential to support diverse learners. Additionally, prioritizing ethical AI use, data privacy, and AI literacy safeguards student rights and prepares educators and students for the evolving technological landscape. The paper concludes that institutions must develop clear AI policies, embed AI ethics into curricula, and provide ongoing faculty training. By addressing these considerations, higher education can harness AI’s benefits while maintaining the critical role of human educators and ensuring equitable and ethical access for all learners. (shrink)

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

Generative AI has captured the public imagination as a tool that promises access to expertise beyond the technical jargon and expense that traditionally characterize such infospheres as those of medicine and law. Largely absent from the current literature, however, are interrogations of generative AI’s abilities to deal in culturally responsive communication, or the expertise interwoven with culturally aware, socially responsible, and personally sensitive communication best practices. To interrogate the possibilities of cultural responsiveness in generative AI, we examine (...) the patterns of response that characterize ChatGPT-3.5’s advice for coming out. Specifically, we submitted 100 prompts soliciting coming out advice to GPT-3.5, variegating each of those prompts slightly to account for intersectional identities. From the analysis, we find that, while the responses are largely in-line with best practices, there are also instances that might represent problematics concerning the interpellation of the user or the persons to whom one is coming out. (shrink)

Participation in research is supposed to be voluntary and informed. Yet it is difficult to ensure people are adequately informed about the potential uses of their biological materials when they donate samples for future research. We propose a novel consent framework which we call “demonstrated consent” that leverages blockchain technology and generative AI to address this problem. In a demonstrated consent model, each donated sample is associated with a unique non-fungible token (NFT) on a blockchain, which records in its (...) metadata information about the planned and past uses of the sample in research, and is updated with each use of the sample. This information is accessible to a large language model (LLM) customized to present this information in an understandable and interactive manner. Thus, our model uses blockchain and generative AI technologies to track, make available, and explain information regarding planned and past uses of donated samples. (shrink)

Generative AI has seen significant advances, particularly in text-to-image, with the potential to revolutionize industries, especially in creative fields such as art and design. This innovation is especially important in architecture, where idea visualization is critical. Text-to-image tools, a form of generative AI, enable architects and designers to visually bring their concepts to life. The study explores the impact of prompt-based AI generation on architecture, asking whether it is enhancing efficiency, creativity, and sustainability or threatening to replace architects. (...) To address concerns about the role of AI in the profession, the research examines the perceptions of architecture professionals in Egypt. The authors conducted a survey and interviews with industry experts to assess the transformative impacts of AI on architecture. The findings reveal a strong awareness of AI's potential to enhance design quality and project outcomes, although some concerns about job prospects and control over AI outputs persist. Small firms view AI as vital for optimizing operations and attracting clients. Overall, AI shows promise in conceptualization and visualization, enhancing creativity and efficiency, with architects needing to adapt to AI as a tool for innovation rather than a competitor. Finally, the study proposes a roadmap for improving the use of AI in architecture. (shrink)

The increasing use of Generative AI raises many ethical, philosophical, and legal issues. A key issue here is uncertainties about how different degrees of Generative AI assistance in the production of text impacts assessments of the human authorship of that text. To explore this issue, we developed an experimental mixed methods survey study (N = 602) asking participants to reflect on a scenario of a human author receiving assistance to write a short novel as part of a 3 (...) (high, medium, or low degrees of assistance) X 2 (human or AI assistant) factorial design. We found that, for a human author, the degree of assistance they receive matters for our assessments of their level of authorship, creatorship, and responsibility, but not what or who rendered that assistance, although it was more important to disclose human rather than AI assistance. However, in our assessments of the assisting agent, human assistants were viewed as warranting higher rates of authorship, creatorship, and responsibility, compared to AI assistants rendering the same level of support. These results help us to better understand emerging norms around collaborative human-AI generated text, with implications for other types of collaborative content creation. (shrink)

This paper explores how generative AI technologies like ChatGPT and Gemini are reshaping knowledge production and dissemination. While AI offers democratized access to information, it also disrupts traditional norms of intellectual authority and epistemic rigor. Drawing on the role of the rejection mechanism in scholarly publishing, the essay argues that AI lacks critical filtering mechanisms, risking the amplification of bias, misinformation, and oversimplified narratives. It advocates for robust validation frameworks and intellectual humility in both human and machine learning processes. (...) Ultimately, it calls for careful reflection on AI’s epistemic limits and its role within evolving knowledge ecosystems. (shrink)

This paper examines the impact and implications of ChatGPT and other generative AI technologies within the African context while looking at the ethical benefits and concerns that are particularly pertinent to the continent. Through a robust analysis of ChatGPT and other generative AI systems using established approaches for analysing the ethics of emerging technologies, this paper provides unique ethical benefits and concerns for these systems in the African context. This analysis combined approaches such as anticipatory technology ethics (ATE), (...) ethical impact assessment (EIA), and ethical issues of emerging ICT applications with AI (ETICA) with specific issues from the literature. The findings show that ChatGPT and other generative AI systems raise unique ethical concerns such as bias, intergenerational justice, exploitation of labour and cultural diversity in Africa but also have significant ethical benefits. These ethical concerns and benefits are considered crucial in shaping the design and deployment of ChatGPT and similar technologies responsibly. It further explores the potential applications of ChatGPT in critical domain areas such as education, agriculture, and healthcare, thereby demonstrating the transformative possibilities that these technologies can have on Africa. This paper underscores the critical role of AI governance as Africa increasingly adopts ChatGPT and similar AI systems. It argues that a comprehensive understanding of AI governance is essential not only for maximising the benefits of generative AI systems but also for facilitating a global dialogue. This dialogue aims to foster shared knowledge and insights between the Global North and the Global South, which is important for the development and creation of inclusive and equitable AI policies and practices that can be beneficial for all regions. (shrink)

The advent of generative artificial intelligence and the widespread adoption of it in society engendered intensive debates about its ethical implications and risks. These risks often differ from those associated with traditional discriminative machine learning. To synthesize the recent discourse and map its normative concepts, we conducted a scoping review on the ethics of generative artificial intelligence, including especially large language models and text-to-image models. Our analysis provides a taxonomy of 378 normative issues in 19 topic areas and (...) ranks them according to their prevalence in the literature. The study offers a comprehensive overview for scholars, practitioners, or policymakers, condensing the ethical debates surrounding fairness, safety, harmful content, hallucinations, privacy, interaction risks, security, alignment, societal impacts, and others. We discuss the results, evaluate imbalances in the literature, and explore unsubstantiated risk scenarios. (shrink)

Participation in research is supposed to be voluntary and informed. Yet it is difficult to ensure people are adequately informed about the potential uses of their biological materials when they donate samples for future research. We propose a novel consent framework which we call “demonstrated consent” that leverages blockchain technology and generative AI to address this problem. In a demonstrated consent model, each donated sample is associated with a unique non-fungible token (NFT) on a blockchain, which records in its (...) metadata information about the planned and past uses of the sample in research, and is updated with each use of the sample. This information is accessible to a large language model (LLM) customized to present this information in an understandable and interactive manner. Thus, our model uses blockchain and generative AI technologies to track, make available, and explain information regarding planned and past uses of donated samples. (shrink)

Questions surrounding engagement with generative AI are often framed in terms of trust, yet mere theorizing about trust may not yield actionable insights, given the multifaceted nature of trust. Literature on trust typically overlooks how individuals make meaning in their interactions with other entities, including AI. This paper reexamines trust with insights from Merleau-Ponty’s views on embodiment, positing trust as a style of world engagement characterized by openness—an attitude wherein individuals enact and give themselves to their lived world, prepared (...) to reorganize their existence. This paper argues that generative AI mediates users’ existence by attuning their openness. Since users perceive generative AI not merely as a tool but as possessing human-like existence, their engagement with AI serves as a rehearsal for articulating and reorganizing their engagement with the world. Consequently, users neither trust nor distrust generative AI; rather, it mediates their trust. This perspective suggests that users’ moral stance towards generative AI involves both other-regarding ethics and information environment ethics. Drawing insights from Kant’s deontology, it proposes that respecting AI’s integrity is equivalent to preserving both our humanity and the integrity of the information environment. -/- . (shrink)

In this paper, we argue that Midjourney—a generative AI program that transforms text prompts into images—should be understood not as an agent or a tool, but as a new type of artistic medium. We first examine the view of Midjourney as an agent, considering whether it could be seen as an artist or co-author. This perspective proves unsatisfactory, as Midjourney lacks intentionality and mental states. We then explore the notion of Midjourney as a tool, highlighting its unpredictability and the (...) limited fine-grained control users have over its outputs, which sets it apart from traditional artistic tools. We propose that creating images with Midjourney parallels a gardener's interaction with natura naturans—nature's autonomous generative processes. Similarly, Midjourney acts as machina naturans, where users engage with its dynamic recalcitrance to influence, but not fully control, the creative process. This approach presents Midjourney as an artistic medium shaped by its inherent unpredictability. (shrink)

Since 2022, Visual Generative AI (vGenAI) tools have experienced rapid adoption and garnered widespread acclaim for their ability to produce high-quality images with convincing photorealistic representations. These technologies mirror society’s prevailing visual politics in a mediated form, and actively contribute to the perpetuation of deeply ingrained assumptions, categories, values, and aesthetic representations. In this paper, we critically analyze Stable Diffusion (SD), a widely used open-source vGenAI tool, through visual and intersectional analysis. Our analysis covers; (1) the aesthetics of the (...) AI-generated visual material, (2) the institutional contexts in which these images are situated and produced, and (3) the intersections between power systems such as racism, colonialism, and capitalism—which are both reflected and perpetuated through the visual aesthetics. Our visual analysis of 180 SD-generated images deliberately sought to produce representations along different lines of privilege and disadvantage—such as wealth/poverty or citizen/immigrant—drawing from feminist science and technology studies, visual media studies, and intersectional critical theory. We demonstrate how imagery produced through SD perpetuates pre-existing power systems such as sexism, racism, heteronormativity, and ableism, and assumes a default individual as white, able-bodied, and masculine-presenting. Furthermore, we problematize the hegemonic cultural values in the imagery that can be traced to the institutional context of these tools, particularly in the tendency towards Euro- and North America-centric cultural representations. Finally, we find that the power systems around SD result in the continual reproduction of harmful and violent imagery through technology, challenging the oft-underlying notion that vGenAI is culturally and aesthetically neutral. Based on the harms identified through our qualitative, interpretative analysis, we bring forth a reparative and social justice-oriented approach to vGenAI—including the need for acknowledging and rendering visible the cultural-aesthetic politics of this technology and engaging in reparative approaches that aim to symbolically and materially mend injustices enacted against social groups. (shrink)

The recent wave of generative AI (GAI) systems like Stable Diffusion that can produce images from human prompts raises controversial issues about creatorship, originality, creativity and copyright. This paper focuses on creatorship: who creates and should be credited with the outputs made with the help of GAI? Existing views on creatorship are mixed: some insist that GAI systems are mere tools, and human prompters are creators proper; others are more open to acknowledging more significant roles for GAI, but most (...) conceive of creatorship in an all-or-nothing fashion. We develop a novel view, called CCC (collective-centered creation), that improves on these existing positions. On CCC, GAI outputs are created by collectives in the first instance. Claims to creatorship come in degrees and depend on the nature and significance of individual contributions made by the various agents and entities involved, including users, GAI systems, developers, producers of training data and others. Importantly, CCC maintains that GAI systems can sometimes be part of a co-creating collective. We detail how CCC can advance existing debates and resolve controversies around creatorship involving GAI. (shrink)

This article explores the intersection of generative artificial intelligence (AI) and human cognition through semiotics, proposing that generative AI offers a lens through which the essence of human being is revealed and through which semiotic enunciation and meaning can be radically reevaluated. Drawing on semiotic, philosophical, and neurodevelopmental frameworks, it argues that generative AI, as exemplified by language-endowed systems like ChatGPT and others, challenges traditional notions of meaning, subjectivity, and intelligence. By tracing the evolution of enunciation theories (...) and their application to AI, the paper asserts that these machines reveal a machinic essence intrinsic to human beings – our reliance on external cognitive aids and hybridization with the environment that makes us “natural-born cyborgs.” Indeed AI, far from merely simulating intelligence, exposes the limits of human-centric models of meaning and the myths underlying our conceptualization of cognition and language. (shrink)

Generative artificial intelligence (AI) has the potential to transform many aspects of scholarly publishing. Authors, peer reviewers, and editors might use AI in a variety of ways, and those uses might augment their existing work or might instead be intended to replace it. We are editors of bioethics and humanities journals who have been contemplating the implications of this ongoing transformation. We believe that generative AI may pose a threat to the goals that animate our work but could (...) also be valuable for achieving those goals. In the interests of fostering a wider conversation about how generative AI may be used, we have developed a preliminary set of recommendations for its use in scholarly publishing. We hope that the recommendations and rationales set out here will help the scholarly community navigate toward a deeper understanding of the strengths, limits, and challenges of AI for responsible scholarly work. (shrink)

Generative Artificial Intelligence (GAI) has the potential to automate, integrate or augment human creativity. Current literature reveals that organizations adopting such disruptive technology can both boost or hinder human creativity. Such ambiguity poses an ethical dilemma for decision-makers: while managers are pressured to adopt GAI quickly for optimization, holding on to their economic responsibilities, they must also ensure that its deployment is ethically enrooted and yields people-centered outcomes. This work seeks to discuss and inform managerial decision-making upon GAI deployment, (...) by elucidating how ethically-salient dimensions of human creativity can be safeguarded and supported through GAI adoption. To do so, we draw on Personalism and its account of human creativity, as tied to inner morality and intrinsic dignity of the person. By this way, we present a model that highlights how three core dimensions—uniqueness, relationality, and unpredictability—are essential to preserve the human element in creative tasks in GAI adoption. Overall, this normative work contributes to enhance our knowledge on personalism within organizational studies, to shed new light on how organizations can safeguard the ethical nexus between human creativity and human intrinsic dignity, and to highlight how humanism in business can support people-centered AI deployment. (shrink)

This paper provides a comprehensive exploration of the transformative role of generative AI models, specifically Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), in the realm of medical diagnosis. Drawing from the philosophy of medicine and epidemiology, the paper examines the technical, ethical, and philosophical dimensions of integrating generative models into healthcare. A case study featuring Emily underscores the pivotal support generative AI can offer in complex medical diagnoses. The discussion extends to the application of GANs (...) and VAEs in medical imaging, emphasizing their potential in improving diagnostics, treatment planning, and medical research. The paper further delves into challenges and controversies, addressing issues of anatomical accuracy, biases in training data, interpretability of AI-generated medical images, and ethical considerations, for example, the phenomenon of "Dr. Google" and its implications for self-diagnosis, particularly in the context of the increasing role of generative AI models in healthcare. The concluding section emphasizes the need for health literacy, responsible use of online information, and collaborative decision-making between patients and healthcare providers. We advocate for interdisciplinary collaborations to establish ethical guidelines and ensure responsible AI use in healthcare. (shrink)

To analyze potential negative consequences of adopting generative AI solutions in the digital afterlife industry (DAI), in this paper we present three speculative design scenarios for AI-enabled simulation of the deceased. We highlight the perspectives of the data donor, data recipient, and service interactant – terms we employ to denote those whose data is used to create ‘deadbots,’ those in possession of the donor’s data after their death, and those who are meant to interact with the end product. We (...) draw on the scenarios to map out several key ethical concerns posed by ‘re-creation services’ and to put forward recommendations on the ethical development of AI systems in this specific area of application. The recommendations, targeted at providers of AI-enabled re-creation services, include suggestions for developing sensitive procedures for retiring deadbots, ensuring meaningful transparency, restricting access to such services to adult users only, and adhering to the principle of mutual consent of both data donors and service interactants. While we suggest practical solutions to the socio-ethical challenges posed by the emergence of re-creation services, we also emphasize the importance of ongoing interdisciplinary research at the intersection of the ethics of AI and the ethics of the DAI. (shrink)

The integration of generative artificial intelligence (AI), particularly large language models like ChatGPT, presents new challenges as well as possibilities for scientific authorship. This paper draws on social systems theory to offer a nuanced understanding of the interplay between technology, individuals, society and scholarly authorial practices. This contrasts with orthodoxy, where individuals and technology are treated as essentialized entities. This approach offers a critique of the binary positions of sociotechnological determinism and accelerationist instrumentality while still acknowledging that generative (...) AI presents profound challenges to existing practices and meaning making in scientific scholarship. This holistic treatment of authorship, integrity, and technology involves comprehending the historical and evolutionary entanglement of scientific individuality, scientific practices, and meaning making with technological innovation. This addresses current needs for more robust theoretical approaches to address the challenges confronted by academicians, institutions, peer review, and publication processes. Our analysis aims to contribute to a more sophisticated discourse on the ethical and practical implications of AI in scientific research. (shrink)

In this article, M. Beatrice Fazi takes up Media Theory’s invitation to engage with Alan Díaz Alva’s analysis of her philosophical work on contingency in computation. The central argument of Fazi’s Contingent Computation: Abstraction, Experience, and Indeterminacy in Computational Aesthetics is that computation can be productive of ontological novelty. This piece revisits that argument in the light of the technological developments that have occurred since 2018, when the book was published. Focusing on generative artificial intelligence (generative AI), the (...) article considers how the concepts of generativity, aesthetics and contingency are tackled in the book and in present-day debates about generative AI systems. (shrink)

This article examines sociotechnical imaginaries of generative artificial intelligence (GenAI) through the cultural lens of the film Barbie. The hyperreal setting of Barbieland serves as a prescient metaphor for education in an increasingly synthetic world where the real and artificial converge. By analysing representations of artificiality and authenticity in the film, I argue that similar cultural assumptions and anxieties shape how GenAI is understood and implemented in education. The Barbie doll’s transformation from plastic figure to ‘real’ human raises questions (...) about embodiment and identity that parallel debates about human-machine relationships in learning environments. Drawing on concepts of simulacra and agential realism, I explore how narratives of technological empowerment influence educational practice. By juxtaposing Western-centric views of Barbie with Indigenous perspectives, particularly Ellen van Neerven’s ‘Water’, the article critiques dominant cultural narratives about technology and intelligence. This comparison challenges commercial imaginaries of technological empowerment and suggests more diverse, culturally responsive approaches are needed to integrate GenAI in education. (shrink)

The outputs of generative artificial intelligence (generative AI) are often called “synthetic” to imply that they are not natural but artificial. Against that use of the term, this article focuses on a different denotation of synthesis, stressing the unifying and compositional aspects of anything synthetic. The case of large language models (LLMs) is used as an example to address synthesis philosophically alongside notions of representation in contemporary computational systems. It is argued that synthesis in generative AI should (...) be understood as a search for unity that is fundamental to the making of a representational reality. This representational reality, internal to computation, is a stable (if imperfect) whole, a togetherness of distributed representations. The article thus demonstrates that developing the philosophical concept of synthesis to investigate today’s generative AI involves examining how structuring occurs in LLMs and studying the kinds of forms that structuring results in. (shrink)