The advent of Generative AI, particularly through Large Language Models (LLMs) like ChatGPT and its successors, marks a paradigm shift in the AI landscape. Advanced LLMs exhibit multimodality, handling diverse data formats, thereby broadening their application scope. However, the complexity and emergent autonomy of these models introduce challenges in predictability and legal compliance. This paper analyses the legal and regulatory implications of Generative AI and LLMs in the European Union context, focusing on 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 revolution of artificial intelligence (AI), particularly generative AI, and its implications for human–robot interaction (HRI) opened up the debate on crucial regulatory, business, societal, and ethical considerations. This paper explores essential issues from the anthropomorphic perspective, examining the complex interplay between humans and AI models in societal and corporate contexts. We provided a comprehensive review of existing literature on HRI, with a special emphasis on the impact of generative models such as ChatGPT. The scientometric study posits that (...) due to their advanced linguistic capabilities and ability to mimic human-like behavior, generative AIs like ChatGPT will continue to grow in popularity in pair with human rational empathy, tendency for personification and their advanced linguistic capabilities and ability to mimic human-like behavior. As they blur the boundaries between humans and robots, these models introduce fresh moral and philosophical dilemmas. Our research aims to extrapolate key trends and unique factors in HRI and to elucidate the technical aspects of generative AI that enhance its effectiveness in this field compared to traditional rule-based AI systems. We further discuss the challenges and limitations of applying generative AI in HRI, providing a future research agenda for AI optimization in diverse applications, including education, entertainment, and healthcare. (shrink)

This 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)

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.

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...

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)

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)

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)

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 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...

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...

A leading critic of the disruptive force of technology in education, Bernard Stiegler saw the counter-effects of artificial intelligence in undermining human agency, autonomy and individuality, rendering the role of education ever more critical. Stiegler believes that our goal is not to abandon technology but to focus our attention on its power and direction in a hypercapitalist economy. While he did not foresee the emergence of generative artificial intelligence (GAI), its rapid acceleration raises important issues for his notion of (...) digital writing as a pharmakon. In this essay, we examine Stiegler’s view on intelligence, digital writing, technological memory, and neuropower, focusing specifically on his Shanghai lecture and Nanjing Lectures. We compare Stiegler with Catherine Malabou, who examines the nature of intelligence primarily through the lens of neuroscience. We argue that Malabou’s view of neuroplasticity in human and artificial brains, and her critique of techno-narcissism in the control over technology, add an important dimension to Stiegler’s political critique of education. Finally, we examine how best to construe AI as part of a hyper-materialist epistemology that Stiegler espouses in the conclusion of his Nanjing Lectures, to bring about neganthropic knowledge through a therapeutics of care. (shrink)

The recent emergence of generative AI software as viable tools for use in the cultural and creative industries has sparked debates about the potential for “creativity” to be automated and “augmented” by algorithmic machines. Such discussions, however, begin from an ontological position, attempting to define creativity by either falling prey to universalism (i.e. “creativity is X”) or reductionism (i.e. “only humans can be truly creative” or “human creativity will be fully replaced by creative machines”). Furthermore, such an approach evades (...) addressing the real and material impacts of AI on creative labour in these industries. This article thus offers more expansive methodological and conceptual approaches to the recent hype on generative AI. By combining (Csikszentmihalyi, The systems model of creativity, Springer, Dordrecht, 2014) systems view of creativity, in which we emphasise the shift from “what” to “where” is creativity, with (Lievrouw, Media technologies, The MIT Press, 2014) relational-materialist theory of “mediation”, we argue that the study of “creativity” in the context of generative AI must be attentive to the interactions between technologies, practices, and social arrangements. When exploring the relational space between these elements, three core concepts become pertinent: creative labour, automation, and distributed agency. Critiquing “creativity” through these conceptual lenses allows us to re-situate the use of generative AI within discourses of labour in post-industrial capitalism and brings us to a conceptualisation of creativity that privileges neither the human user nor machine algorithm but instead emphasises a relational and distributed form of agency. (shrink)

Spoken communication between social robotic devices, powered by generative AI tools such as ChatGPT, and the senior population offers great potential for researching social interaction and robot identity perceptions as well as exploring the potential opportunities and challenges when implementing this human-machine interactions in real life situations and health care. In this paper we explore people’s perceptions of the social robot Furhat when administering verbal tasks similar to those used in screening for Alzheimer’s disease. We describe the Slovak system (...) mounted on the robot that includes a speech recognizer, the scenarios powered by generative large language model ChatGPT, and a speech synthesizer. We tested the functionality of the proposed approach with two groups of participants: attendees of a large science fair and a scientific conference. The observations from 87 collected questionnaires suggest good potential and applicability of such an approach for the given task and more positive attitudes of older subjects compared to younger ones. (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)

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 paper discusses some societal implications of the most recent and publicly discussed application of advanced machine learning techniques: generative AI models, such as ChatGPT (text generation) and DALL-E (text-to-image generation). The aim is to shift attention away from conceptual disputes, e.g. regarding their level of intelligence and similarities/differences with human performance, to focus instead on practical problems, pertaining the impact that these technologies might have (and already have) on human societies. After a preliminary clarification of how generative (...) AI works (Sect. 1), the paper discusses what kind of transparency ought to be required for such technologies and for the business model behind their commercial exploitation (Sect. 2), what is the role of user-generated data in determining their performance and how it should inform the redistribution of the resulting benefits (Sect. 3), the best way of integrating generative AI systems in the creative job market and how to properly negotiate their role in it (Sect. 4), and what kind of “cognitive extension” offered by these technologies we ought to embrace, and what type we should instead resist and monitor (Sect. 5). The last part of the paper summarizes the main conclusions of this analysis, also marking its distance from other, more apocalyptic approaches to the dangers of AI for human society. (shrink)

Until now, the mass spread of fake news and its negative consequences have implied mainly textual content towards a loss of citizens' trust in institutions. Recently, a new type of machine learning framework has arisen, Generative Adversarial Networks (GANs) – a class of deep neural network models capable of creating multimedia content (photos, videos, audio) that simulate accurate content with extreme precision. While there are several areas of worthwhile application of GANs – e.g., in the field of audio-visual production, (...) human-computer interactions, satire, and artistic creativity – their deceptive uses, at least as currently foreseeable, are just as numerous and worrying. The main concern is linked to the so-called “deepfakes”, fake images or videos that simulate real events with extreme precision. When trained on a human face, GANs can make the face assume hyper-realistic movements, expressions and (verbal and non-verbal) communication abilities. This technology poses an urgent threat to the governance of democratic processes concerning the production of public opinions and political discourses, with significant potential for reality-altering and disinformation. After a short introduction of their current technical state-of-the-art, in this paper we want to enquire the GANs` socio-technical system alongside different and intertwined philosophical accounts. Firstly, we will argue about the conditions that make perceived a GANs-generated content as trustworthy, arguing also about the general effects GANs might have on the perceived trustworthiness of individuals. Thereafter, we will discuss about the inadequacy to approach GANs only as perception-altering technology. Against this backdrop, we will propose a theoretical turn that considers the human-machine relationships of trustworthiness as elements of a broader hybrid socio-technical systems. This turn come up with political repercussions that we will discuss in the last part of the paper. (shrink)

“Throw away anything that sounds too complicated. Only keep what is simple to grasp...If the information appears fuzzy and causes the brain to implode after two sentences, toss it away and stop listening. Doing so will make the news as orderly and simple to understand as the truth.” - In “GHG emissions,” The Kingfisher Story Collection, (Vuong 2022a).

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)

This study explores the capability of academic staff assisted by the Turnitin Artificial Intelligence (AI) detection tool to identify the use of AI-generated content in university assessments. 22 different experimental submissions were produced using Open AI’s ChatGPT tool, with prompting techniques used to reduce the likelihood of AI detectors identifying AI-generated content. These submissions were marked by 15 academic staff members alongside genuine student submissions. Although the AI detection tool identified 91% of the experimental submissions as containing AI-generated content, only (...) 54.8% of the content was identified as AI-generated, underscoring the challenges of detecting AI content when advanced prompting techniques are used. When academic staff members marked the experimental submissions, only 54.5% were reported to the academic misconduct process, emphasising the need for greater awareness of how the results of AI detectors may be interpreted. Similar performance in grades was obtained between student submissions and AI-generated content (AI mean grade: 52.3, Student mean grade: 54.4), showing the capabilities of AI tools in producing human-like responses in real-life assessment situations. Recommendations include adjusting the overall strategies for assessing university students in light of the availability of new Generative AI tools. This may include reducing the overall reliance on assessments where AI tools may be used to mimic human writing, or by using AI-inclusive assessments. Comprehensive training must be provided for both academic staff and students so that academic integrity may be preserved. (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)

In this article I discuss the ethical ramifications for medical ethics training of the availability of large language models (LLMs) for medical students. My focus is on the practical ethical consequences for what we should expect of medical students in terms of medical professionalism and ethical reasoning, and how this can be tested in a context where LLMs are relatively easy available. If we continue to expect ethical competences of medical professionalism of future physicians, how much – if at all (...) – should we worry that such generative AI may compromise adequate testing of medical students’ abilities in this regard? I mainly focus on assessment methods based on written assignments of the ‘student paper’ type and consider whether LLMs make it unfeasible for assessors to gauge whether output is student-generated or ‘machine-generated’ and, if so, whether this is a problem. My take on this research question unfolds in three interwoven arguments, claiming that the advent of LLMs may offer a momentum (i) to reaffirm the importance of context-sensitive interpretation and specification of ethical principles in medical ethics training, (ii) to provide more supportive circumstances to assessors to allow them to meet scoring demands entailed by the importance that is placed on medical professionalism, and (iii) to complement written assignments with verbal (group) discussion to train and test students’ skills to habitually recognize that ‘moral solutions’ can be normatively questioned and specified from various perspectives. (shrink)

Large Language Models (LLMs) “assign probabilities to sequences of text. When given some initial text, they use these probabilities to generate new text. Large language models are language models u...

Cohen’s article (2023) on the significance of ChatGPT for bioethics suggests that little is known about the development of generative AI (“GAI”) in China and other national markets. It warns about...

The new generative artificial intelligence (AI) tools, and especially the large language models (LLMs) of which ChatGPT is the most prominent example, have the potential to transform many aspects o...

The new generative artificial intelligence (AI) tools, and especially the large language models (LLMs) of which ChatGPT is the most prominent example, have the potential to transform many aspects o...

Since the launch of applications such as DALL-E, Midjourney, and Stable Diffusion, generative artificial intelligence has been controversial as a tool for creating artwork. While some have presented longtermist worries about these technologies as harbingers of fully automated futures to come, more pressing is the impact of generative AI on creative labour in the present. Already, business leaders have begun replacing human artistic labour with AI-generated images. In response, the artistic community has launched a protest movement, which argues (...) that AI image generation is a kind of theft. This paper analyzes, substantiates, and critiques these arguments, concluding that AI image generators involve an unethical kind of labour theft. If correct, many other AI applications also rely upon theft. (shrink)

This research delves into the potential of implementing artificial intelligence in architecture. It specifically provides a critical assessment of AI-enabled workflows, encompassing creative ideation, representation, materiality, and critical thinking, facilitated by prompt-based generative processes. In this context, the paper provides an examination of the concept of hybrid human–machine intelligence. In an era characterized by pervasive data bias and engineered injustices, the concept of hybrid intelligence emerges as a critical tool, enabling the transcendence of preconceived stereotypes, clichés, and linguistic prejudices. (...) This paper not only explores the applied and generative capacities of AI-enabled workflows but also suggests fundamental approaches that can enhance the creative process and confront the embedded biases and injustices within data-driven systems. (shrink)

As the use of AI systems continues to increase, so do concerns over their lack of fairness, legitimacy and accountability. Such harmful automated decision-making can be guarded against by ensuring AI systems are contestable by design: responsive to human intervention throughout the system lifecycle. Contestable AI by design is a small but growing field of research. However, most available knowledge requires a significant amount of translation to be applicable in practice. A proven way of conveying intermediate-level, generative design knowledge (...) is in the form of frameworks. In this article we use qualitative-interpretative methods and visual mapping techniques to extract from the literature sociotechnical features and practices that contribute to contestable AI, and synthesize these into a design framework. (shrink)

The ability to interact in a natural language profoundly changes devices’ interfaces and potential applications of speaking technologies. Concurrently, this phenomenon challenges our mainstream theories of knowledge, such as how to analyze linguistic outputs of devices under existing anthropocentric theoretical assumptions. In section 1, I present the topic of machines that speak, connecting between Descartes and Generative AI. In section 2, I argue that accepted testimonial theories of knowledge and justification commonly reject the possibility that a speaking technological artifact (...) can give testimony. In section 3, I identify three assumptions underlying the view that rejects conversational AIs – AI-based technologies that converse, as testifiers: conversational AIs (1) lack intentions, (2) cannot be normatively assessed, and (3) cannot constitute an object in trust relations, while humans can. In section 4, I propose the concept ‘AI-testimony’ for analyzing outputs of conversational AIs, suggesting three conditions for technologies to deliver AI-testimony: (1) content is propositional, (2) generated and delivered with no other human directly involved, (3) the output is perceived as phenomenologically similar to that of a human. I conclude that this concept overcomes the limitations of the anthropocentric concept of testimony, opening future directions of research without associating conversational AIs with human-like agency. (shrink)

One objection to conventional AI ethics is that it slows innovation. This presentation responds by reconfiguring ethics as an innovation accelerator. The critical elements develop from a contrast between Stability AI’s Diffusion and OpenAI’s Dall-E. By analyzing the divergent values underlying their opposed strategies for development and deployment, five conceptions are identified as common to acceleration ethics. Uncertainty is understood as positive and encouraging, rather than discouraging. Innovation is conceived as intrinsically valuable, instead of worthwhile only as mediated by social (...) effects. AI problems are solved by more AI, not less. Permissions and restrictions governing AI emerge from a decentralized process, instead of a unified authority. The work of ethics is embedded in AI development and application, instead of functioning from outside. Together, these attitudes and practices remake ethics as provoking rather than restraining artificial intelligence. (shrink)

Abstract:Here I look at sundry aspects of the current controversy about Generative AI and, in particular, the implications of this new and rapidly evolving technology for poetry, the arts, and human creativity in general. My essay looks at earlier episodes in the history of thought, from Descartes on, that I take to have prefigured this latest debate around 'the human' in relation to its various physical, 'artificial,' or (presumptively) prosthetic means of extension and refinement. I also discuss its bearing (...) on literary-critical issues concerning the formal or structural aspects of poetry, the role (if any) of authorial intent, and the extent to which criticism enters into– and might lay claim to a stake in–what counts as human creativity. A central point of reference is William Empson's 1930 book, Seven Types of Ambiguity, where these issues are raised in a strikingly prescient way through his practice of close-focused textual analysis allied to great philosophical acuity. I conclude that a priori attitudinizing, pro or con, is not much use in this context, but that poets, critics, philosophers, cognitive scientists, and AI researchers need to cooperate in the reexamination of many long-standing but now highly contestable distinctions. (shrink)

Historically, the dynamic between philosophy of artificial intelligence and its practical application has been essential for the development of both, and thus the encounter between theory of AI and architectural/urban theory should be a site of considerable productivity. However, in many ways, it is not. This is due to two primary factors, one arising from each side of this encounter. First, legacies of overly-anthropomorphic models of AI permeate design discourses, where issues of how well AI can be constrained to social (...) issues of philosophy of mind exclude more foundational and cross-cultural questions about artificial intelligence as material process in the physical world that could inform new philosophical insights. Concurrently, the mobilization of investment in “Smart Cities” discourses dominates the space of application of AI at urban scale in ways that prematurely fixes solutions in the skeuomorphic image of architectural and urban conventions. To break this impasse, we held a series of think-tanks, workshops, and design charettes that brought together leading figures in philosophy of artificial intelligence, urban design, and commercial AI platforms. The goal was to re-think from first principles how alternative philosophical models of AI as a distributed, discontinuous, landscape-scale technology in a direct encounter with the applications in contexts of ecological sensing, automation’s impact on urban form, and issues of algorithmic governance. The design brief for this research establishes a generative framework for conceiving how embedded machine sensing and intelligence is already changing urban form and could change it in the future. The framework focuses on issues of urban zoning and architectural programming, data modeling and governance, platform cognition and design, and how these inform shifts in dynamics of public and private institutions. (shrink)

In this paper, we sketch a programme for AI-driven social theory. We begin by defining what we mean by artificial intelligence (AI) in this context. We then lay out our specification for how AI-based models can draw on the growing availability of digital data to help test the validity of different social theories based on their predictive power. In doing so, we use the work of Randall Collins and his state breakdown model to exemplify that, already today, AI-based models can (...) help synthesise knowledge from a variety of sources, reason about the world, and apply what is known across a wide range of problems in a systematic way. However, we also find that AI-driven social theory remains subject to a range of practical, technical, and epistemological limitations. Most critically, existing AI-systems lack three essential capabilities needed to advance social theory in ways that are cumulative, holistic, open-ended, and purposeful. These are (1) semanticisation, i.e., the ability to develop and operationalize verbal concepts to represent machine-manipulable knowledge; (2) transferability, i.e., the ability to transfer what has been learned in one context to another; and (3) generativity, i.e., the ability to independently create and improve on concepts and models. We argue that if the gaps identified here are addressed by further research, there is no reason why, in the future, the most advanced programme in social theory should not be led by AI-driven cumulative advances. (shrink)

The growing capabilities of artificial intelligence (AI) word processing models have demonstrated exceptional potential to impact language related tasks and functions. Their fast pace of adoption and probable effect has also given rise to controversy within certain fields. Models, such as GPT-3, are a particular concern for professionals engaged in writing, particularly as their engagement with these technologies is limited due to lack of ability to control their output. Most efforts to maximize and control output rely on a process known (...) as prompt engineering, the construction and modification of the inputted prompt with expectation for certain outputted or desired text. Consequently, prompt engineering has emerged as an important consideration for research and practice. Previous conceptions of prompt engineering have largely focused on technical and logistic modifications to the back-end processing, remaining inaccessible and, still, limited for most users. In this paper, we look to the technical communication field and its methods of text generation—the rhetorical situation—to conceptualize prompt engineering in a more comprehensible way for its users by considering the context and rhetoric. We introduce a framework, consisting of a formula, to prompt engineering, which demands all components of the rhetorical situation be present in the inputted prompt. We present discussions on the future of AI writing models and their use in both professional and educational settings. Ultimately, this discussion and its findings aim to provide a means of integrating agency and writer-centric methods to AI writing tools to advance a more human-in-the-loop approach. As the use of generative AI and especially NLP-based technologies become common across societal functions, the use of prompt engineering will play a crucial role not just in adoption of the technology, but also its productive and responsible use. (shrink)

In the piece, “What Should ChatGPT Mean for Bioethics?” Professor Cohen proposes that the introduction of AI generally, and generative AI specifically, requires that patients be informed of, and co...