Align Generative Artificial Intelligence with Human Preferences: A Novel Large Language Model Fine-Tuning Method for Online Review Management

How Can <i>IJDS</i> Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

Introduction Author Arthur C. Clarke famously argued that in science fiction literature “any sufficiently advanced technology is indistinguishable from magic” (Clarke). On 30 November 2022, technology company OpenAI publicly released their Large Language Model (LLM)-based chatbot ChatGPT (Chat Generative Pre-Trained Transformer), and instantly it was hailed as world-changing. Initial media stories about ChatGPT highlighted the speed with which it generated new material as evidence that this tool might be both genuinely creative and actually intelligent, in both exciting and disturbing ways. Indeed, ChatGPT is part of a larger pool of Generative Artificial Intelligence (AI) tools that can very quickly generate seemingly novel outputs in a variety of media formats based on text prompts written by users. Yet, claims that AI has become sentient, or has even reached a recognisable level of general intelligence, remain in the realm of science fiction, for now at least (Leaver). That has not stopped technology companies, scientists, and others from suggesting that super-smart AI is just around the corner. Exemplifying this, the same people creating generative AI are also vocal signatories of public letters that ostensibly call for a temporary halt in AI development, but these letters are simultaneously feeding the myth that these tools are so powerful that they are the early form of imminent super-intelligent machines. For many people, the combination of AI technologies and media hype means generative AIs are basically magical insomuch as their workings seem impenetrable, and their existence could ostensibly change the world. This article explores how the hype around ChatGPT and generative AI was deployed across the first six months of 2023, and how these technologies were positioned as either utopian or dystopian, always seemingly magical, but never banal. We look at some initial responses to generative AI, ranging from schools in Australia to picket lines in Hollywood. We offer a critique of the utopian/dystopian binary positioning of generative AI, aligning with critics who rightly argue that focussing on these extremes displaces the more grounded and immediate challenges generative AI bring that need urgent answers. Finally, we loop back to the role of schools and educators in repositioning generative AI as something to be tested, examined, scrutinised, and played with both to ground understandings of generative AI, while also preparing today’s students for a future where these tools will be part of their work and cultural landscapes. Hype, Schools, and Hollywood In December 2022, one month after OpenAI launched ChatGPT, Elon Musk tweeted: “ChatGPT is scary good. We are not far from dangerously strong AI”. Musk’s post was retweeted 9400 times, liked 73 thousand times, and presumably seen by most of his 150 million Twitter followers. This type of engagement typified the early hype and language that surrounded the launch of ChatGPT, with reports that “crypto” had been replaced by generative AI as the “hot tech topic” and hopes that it would be “‘transformative’ for business” (Browne). By March 2023, global economic analysts at Goldman Sachs had released a report on the potentially transformative effects of generative AI, saying that it marked the “brink of a rapid acceleration in task automation that will drive labor cost savings and raise productivity” (Hatzius et al.). Further, they concluded that “its ability to generate content that is indistinguishable from human-created output and to break down communication barriers between humans and machines reflects a major advancement with potentially large macroeconomic effects” (Hatzius et al.). Speculation about the potentially transformative power and reach of generative AI technology was reinforced by warnings that it could also lead to “significant disruption” of the labour market, and the potential automation of up to 300 million jobs, with associated job losses for humans (Hatzius et al.). In addition, there was widespread buzz that ChatGPT’s “rationalization process may evidence human-like cognition” (Browne), claims that were supported by the emergent language of ChatGPT. The technology was explained as being “trained” on a “corpus” of datasets, using a “neural network” capable of producing “natural language“” (Dsouza), positioning the technology as human-like, and more than ‘artificial’ intelligence. Incorrect responses or errors produced by the tech were termed “hallucinations”, akin to magical thinking, which OpenAI founder Sam Altman insisted wasn’t a word that he associated with sentience (Intelligencer staff). Indeed, Altman asserts that he rejects moves to “anthropomorphize” (Intelligencer staff) the technology; however, arguably the language, hype, and Altman’s well-publicised misgivings about ChatGPT have had the combined effect of shaping our understanding of this generative AI as alive, vast, fast-moving, and potentially lethal to humanity. Unsurprisingly, the hype around the transformative effects of ChatGPT and its ability to generate ‘human-like’ answers and sophisticated essay-style responses was matched by a concomitant panic throughout educational institutions. The beginning of the 2023 Australian school year was marked by schools and state education ministers meeting to discuss the emerging problem of ChatGPT in the education system (Hiatt). Every state in Australia, bar South Australia, banned the use of the technology in public schools, with a “national expert task force” formed to “guide” schools on how to navigate ChatGPT in the classroom (Hiatt). Globally, schools banned the technology amid fears that students could use it to generate convincing essay responses whose plagiarism would be undetectable with current software (Clarence-Smith). Some schools banned the technology citing concerns that it would have a “negative impact on student learning”, while others cited its “lack of reliable safeguards preventing these tools exposing students to potentially explicit and harmful content” (Cassidy). ChatGPT investor Musk famously tweeted, “It’s a new world. Goodbye homework!”, further fuelling the growing alarm about the freely available technology that could “churn out convincing essays which can't be detected by their existing anti-plagiarism software” (Clarence-Smith). Universities were reported to be moving towards more “in-person supervision and increased paper assessments” (SBS), rather than essay-style assessments, in a bid to out-manoeuvre ChatGPT’s plagiarism potential. Seven months on, concerns about the technology seem to have been dialled back, with educators more curious about the ways the technology can be integrated into the classroom to good effect (Liu et al.); however, the full implications and impacts of the generative AI are still emerging. In May 2023, the Writer’s Guild of America (WGA), the union representing screenwriters across the US creative industries, went on strike, and one of their core issues were “regulations on the use of artificial intelligence in writing” (Porter). Early in the negotiations, Chris Keyser, co-chair of the WGA’s negotiating committee, lamented that “no one knows exactly what AI’s going to be, but the fact that the companies won’t talk about it is the best indication we’ve had that we have a reason to fear it” (Grobar). At the same time, the Screen Actors’ Guild (SAG) warned that members were being asked to agree to contracts that stipulated that an actor’s voice could be re-used in future scenarios without that actor’s additional consent, potentially reducing actors to a dataset to be animated by generative AI technologies (Scheiber and Koblin). In a statement issued by SAG, they made their position clear that the creation or (re)animation of any digital likeness of any part of an actor must be recognised as labour and properly paid, also warning that any attempt to legislate around these rights should be strongly resisted (Screen Actors Guild). Unlike the more sensationalised hype, the WGA and SAG responses to generative AI are grounded in labour relations. These unions quite rightly fear the immediate future where human labour could be augmented, reclassified, and exploited by, and in the name of, algorithmic systems. Screenwriters, for example, might be hired at much lower pay rates to edit scripts first generated by ChatGPT, even if those editors would really be doing most of the creative work to turn something clichéd and predictable into something more appealing. Rather than a dystopian world where machines do all the work, the WGA and SAG protests railed against a world where workers would be paid less because executives could pretend generative AI was doing most of the work (Bender). The Open Letter and Promotion of AI Panic In an open letter that received enormous press and media uptake, many of the leading figures in AI called for a pause in AI development since “advanced AI could represent a profound change in the history of life on Earth”; they warned early 2023 had already seen “an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control” (Future of Life Institute). Further, the open letter signatories called on “all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4”, arguing that “labs and independent experts should use this pause to jointly develop and implement a set of shared safety protocols for advanced AI design and development that are rigorously audited and overseen by independent outside experts” (Future of Life Institute). Notably, many of the signatories work for the very companies involved in the “out-of-control race”. Indeed, while this letter could be read as a moment of ethical clarity for the AI industry, a more cynical reading might just be that in warning that their AIs could effectively destroy the w

This dialogue is from an early scene in the 2014 film Ex Machina, in which Nathan has invited Caleb to determine whether Nathan has succeeded in creating artificial intelligence.1 The achievement of powerful artificial general intelligence has long held a grip on our imagination not only for its exciting as well as worrisome possibilities, but also for its suggestion of a new, uncharted era for humanity. In opening his 2021 BBC Reith Lectures, titled "Living with Artificial Intelligence," Stuart Russell states that "the eventual emergence of general-purpose artificial intelligence [will be] the biggest event in human history."2Over the last decade, a rapid succession of impressive results has brought wider public attention to the possibilities of powerful artificial intelligence. In machine vision, researchers demonstrated systems that could recognize objects as well as, if not better than, humans in some situations. Then came the games. Complex games of strategy have long been associated with superior intelligence, and so when AI systems beat the best human players at chess, Atari games, Go, shogi, StarCraft, and Dota, the world took notice. It was not just that Als beat humans (although that was astounding when it first happened), but the escalating progression of how they did it: initially by learning from expert human play, then from self-play, then by teaching themselves the principles of the games from the ground up, eventually yielding single systems that could learn, play, and win at several structurally different games, hinting at the possibility of generally intelligent systems.3Speech recognition and natural language processing have also seen rapid and headline-grabbing advances. Most impressive has been the emergence recently of large language models capable of generating human-like outputs. Progress in language is of particular significance given the role language has always played in human notions of intelligence, reasoning, and understanding. While the advances mentioned thus far may seem abstract, those in driverless cars and robots have been more tangible given their embodied and often biomorphic forms. Demonstrations of such embodied systems exhibiting increasingly complex and autonomous behaviors in our physical world have captured public attention.Also in the headlines have been results in various branches of science in which AI and its related techniques have been used as tools to advance research from materials and environmental sciences to high energy physics and astronomy.4 A few highlights, such as the spectacular results on the fifty-year-old protein-folding problem by AlphaFold, suggest the possibility that AI could soon help tackle science's hardest problems, such as in health and the life sciences.5While the headlines tend to feature results and demonstrations of a future to come, AI and its associated technologies are already here and pervade our daily lives more than many realize. Examples include recommendation systems, search, language translators - now covering more than one hundred languages - facial recognition, speech to text (and back), digital assistants, chatbots for customer service, fraud detection, decision support systems, energy management systems, and tools for scientific research, to name a few. In all these examples and others, AI-related techniques have become components of other software and hardware systems as methods for learning from and incorporating messy real-world inputs into inferences, predictions, and, in some cases, actions. As director of the Future of Humanity Institute at the University of Oxford, Nick Bostrom noted back in 2006, "A lot of cutting-edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore."6As the scope, use, and usefulness of these systems have grown for individual users, researchers in various fields, companies and other types of organizations, and governments, so too have concerns when the systems have not worked well (such as bias in facial recognition systems), or have been misused (as in deepfakes), or have resulted in harms to some (in predicting crime, for example), or have been associated with accidents (such as fatalities from self-driving cars).7Dædalus last devoted a volume to the topic of artificial intelligence in 1988, with contributions from several of the founders of the field, among others. Much of that issue was concerned with questions of whether research in AI was making progress, of whether AI was at a turning point, and of its foundations, mathematical, technical, and philosophical-with much disagreement. However, in that volume there was also a recognition, or perhaps a rediscovery, of an alternative path toward AI - the connectionist learning approach and the notion of neural nets-and a burgeoning optimism for this approach's potential. Since the 1960s, the learning approach had been relegated to the fringes in favor of the symbolic formalism for representing the world, our knowledge of it, and how machines can reason about it. Yet no essay captured some of the mood at the time better than Hilary Putnam's "Much Ado About Not Very Much." Putnam questioned the Dædalus issue itself: "Why a whole issue of Dædalus? Why don't we wait until AI achieves something and then have an issue?" He concluded:This volume of Dædalus is indeed the first since 1988 to be devoted to artificial intelligence. This volume does not rehash the same debates; much else has happened since, mostly as a result of the success of the machine learning approach that was being rediscovered and reimagined, as discussed in the 1988 volume. This issue aims to capture where we are in AI's development and how its growing uses impact society. The themes and concerns herein are colored by my own involvement with AI. Besides the television, films, and books that I grew up with, my interest in AI began in earnest in 1989 when, as an undergraduate at the University of Zimbabwe, I undertook a research project to model and train a neural network.9 I went on to do research on AI and robotics at Oxford. Over the years, I have been involved with researchers in academia and labs developing AI systems, studying AI's impact on the economy, tracking AI's progress, and working with others in business, policy, and labor grappling with its opportunities and challenges for society.10The authors of the twenty-five essays in this volume range from AI scientists and technologists at the frontier of many of AI's developments to social scientists at the forefront of analyzing AI's impacts on society. The volume is organized into ten sections. Half of the sections are focused on AI's development, the other half on its intersections with various aspects of society. In addition to the diversity in their topics, expertise, and vantage points, the authors bring a range of views on the possibilities, benefits, and concerns for society. I am grateful to the authors for accepting my invitation to write these essays.Before proceeding further, it may be useful to say what we mean by artificial intelligence. The headlines and increasing pervasiveness of AI and its associated technologies have led to some conflation and confusion about what exactly counts as AI. This has not been helped by the current trend-among researchers in science and the humanities, startups, established companies, and even governments-to associate anything involving not only machine learning, but data science, algorithms, robots, and automation of all sorts with AI. This could simply reflect the hype now associated with AI, but it could also be an acknowledgment of the success of the current wave of AI and its related techniques and their wide-ranging use and usefulness. I think both are true; but it has not always been like this. In the period now referred to as the AI winter, during which progress in AI did not live up to expectations, there was a reticence to associate most of what we now call AI with AI.Two types of definitions are typically given for AI. The first are those that suggest that it is the ability to artificially do what intelligent beings, usually human, can do. For example, artificial intelligence is:The human abilities invoked in such definitions include visual perception, speech recognition, the capacity to reason, solve problems, discover meaning, generalize, and learn from experience. Definitions of this type are considered by some to be limiting in their human-centricity as to what counts as intelligence and in the benchmarks for success they set for the development of AI (more on this later). The second type of definitions try to be free of human-centricity and define an intelligent agent or system, whatever its origin, makeup, or method, as:This type of definition also suggests the pursuit of goals, which could be given to the system, self-generated, or learned.13 That both types of definitions are employed throughout this volume yields insights of its own.These definitional distinctions notwithstanding, the term AI, much to the chagrin of some in the field, has come to be what cognitive and computer scientist Marvin Minsky called a "suitcase word."14 It is packed variously, depending on who you ask, with approaches for achieving intelligence, including those based on logic, probability, information and control theory, neural networks, and various other learning, inference, and planning methods, as well as their instantiations in software, hardware, and, in the case of embodied intelligence, systems that can perceive, move, and manipulate objects.Three questions cut through the discussions in this volume: 1) Where are we in AI's development? 2) What opportunities and challenges does AI pose for society? 3) How much about AI is really about us?Notions of intelligent machines date all the way back to antiquity.15 Philosophers, too, among them Hobbes, Leibnitz, and Descartes, have been dreaming about AI for a long time; Daniel Dennett suggests that Descartes may have even anticipated the Turing Test.16 The idea of computation-based machine intelligence traces to Alan Turing's invention of the universal Turing machine in the 1930s, and to the ideas of several of his contemporaries in the mid-twentieth century. But the birth of artificial intelligence as we know it and the use of the term is generally attributed to the now famed Dartmouth summer workshop of 1956. The workshop was the result of a proposal for a two-month summer project by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon whereby "An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves."17In their respective contributions to this volume, "From So Simple a Beginning: Species of Artificial Intelligence" and "If We Succeed," and in different but complementary ways, Nigel Shadbolt and Stuart Russell chart the key ideas and developments in AI, its periods of excitement as well as the aforementioned AI winters. The current AI spring has been underway since the 1990s, with headline-grabbing breakthroughs appearing in rapid succession over the last ten years or so: a period that Jeffrey Dean describes in the title of his essay as a "golden decade," not only for the pace of AI development but also its use in a wide range of sectors of society, as well as areas of scientific research.18 This period is best characterized by the approach to achieve artificial intelligence through learning from experience, and by the success of neural networks, deep learning, and reinforcement learning, together with methods from probability theory, as ways for machines to learn.19A brief history may be useful here: In the 1950s, there were two dominant visions of how to achieve machine intelligence. One vision was to use computers to create a logic and symbolic representation of the world and our knowledge of it and, from there, create systems that could reason about the world, thus exhibiting intelligence akin to the mind. This vision was most espoused by Allen Newell and Hebert Simon, along with Marvin Minsky and others. Closely associated with it was the "heuristic search" approach that supposed intelligence was essentially a problem of exploring a space of possibilities for answers. The second vision was inspired by the brain, rather than the mind, and sought to achieve intelligence by learning. In what became known as the connectionist approach, units called perceptrons were connected in ways inspired by the connection of neurons in the brain. At the time, this approach was most associated with Frank Rosenblatt. While there was initial excitement about both visions, the first came to dominate, and did so for decades, with some successes, including so-called expert systems.Not only did this approach benefit from championing by its advocates and plentiful funding, it came with the suggested weight of a long intellectual tradition-exemplified by Descartes, Boole, Frege, Russell, and Church, among others-that sought to manipulate symbols and to formalize and axiomatize knowledge and reasoning. It was only in the late 1980s that interest began to grow again in the second vision, largely through the work of David Rumelhart, Geoffrey Hinton, James McClelland, and others. The history of these two visions and the associated philosophical ideas are discussed in Hubert Dreyfus and Stuart Dreyfus's 1988 Dædalus essay "Making a Mind Versus Modeling the Brain: Artificial Intelligence Back at a Branchpoint."20 Since then, the approach to intelligence based on learning, the use of statistical methods, back-propagation, and training (supervised and unsupervised) has come to characterize the current dominant approach.Kevin Scott, in his essay "I Do Not Think It Means What You Think It Means: Artificial Intelligence, Cognitive Work & Scale," reminds us of the work of Ray Solomonoff and others linking information and probability theory with the idea of machines that can not only learn, but compress and potentially generalize what they learn, and the emerging realization of this in the systems now being built and those to come. The success of the machine learning approach has benefited from the boon in the availability of data to train the algorithms thanks to the growth in the use of the Internet and other applications and services. In research, the data explosion has been the result of new scientific instruments and observation platforms and data-generating breakthroughs, for example, in astronomy and in genomics. Equally important has been the co-evolution of the software and hardware used, especially chip architectures better suited to the parallel computations involved in data- and compute-intensive neural networks and other machine learning approaches, as Dean discusses.Several authors delve into progress in key subfields of AI.21 In their essay, "Searching for Computer Vision North Stars," Fei-Fei Li and Ranjay Krishna chart developments in machine vision and the creation of standard data sets such as ImageNet that could be used for benchmarking performance. In their respective essays "Human Language Understanding & Reasoning" and "The Curious Case of Commonsense Intelligence," Chris Manning and Yejin Choi discuss different eras and ideas in natural language processing, including the recent emergence of large language models comprising hundreds of billions of parameters and that use transformer architectures and self-supervised learning on vast amounts of data.22 The resulting pretrained models are impressive in their capacity to take natural language prompts for which they have not been trained specifically and generate human-like outputs, not only in natural language, but also images, software code, and more, as Mira Murati discusses and illustrates in "Language & Coding Creativity." Some have started to refer to these large language models as foundational models in that once they are trained, they are adaptable to a wide range of tasks and outputs.23 But despite their unexpected performance, these large language models are still early in their development and have many shortcomings and limitations that are highlighted in this volume and elsewhere, including by some of their developers.24In "The Machines from Our Future," Daniela Rus discusses the progress in robotic systems, including advances in the underlying technologies, as well as in their integrated design that enables them to operate in the physical world. She highlights the limitations in the "industrial" approaches used thus far and suggests new ways of conceptualizing robots that draw on insights from biological systems. In robotics, as in AI more generally, there has always been a tension as to whether to copy or simply draw inspiration from how humans and other biological organisms achieve intelligent behavior. 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In today’s rapidly evolving business landscape, Artificial Intelligence (AI), and specifically Large Language Models (LLMs), are redefining how organizations operate, make decisions, and engage with customers. AI-driven technologies have become indispensable, providing businesses with powerful tools to streamline operations, derive actionable insights from vast data, and foster more meaningful customer interactions. For business leaders, scholars, and practitioners alike, understanding the transformative potential of AI isn’t just advantageous—it’s essential to staying competitive in an increasingly data-driven world.This editorial delves into recent scholarly advancements in LLM applications within business contexts, analyzing studies that explore AI’s potential across various domains, from decision support to creative industries. By introducing a structured framework, this editorial highlights key insights and contributions from recent studies, assessing their value to academia and industry. The following comparative analysis sheds light on how these innovations shape our understanding of AI’s role in business while pointing to future research directions.Puyt and Madsen's (2024) study stands out as a foundational exploration of LLM accuracy, assessing ChatGPT-4's ability to recount the history of the SWOT analysis-a vital business strategy tool. Their findings reveal that, while ChatGPT-4 effectively conveys general concepts, it struggles with detailed historical information, often producing inaccuracies or "hallucinations." This gap underscores the need for LLMs to be trained with verified academic data, particularly for strategic business applications that demand precision. This study not only contributes to the literature by proposing methods to evaluate AI accuracy in historical contexts but also highlights the importance of rigorous information vetting in industry settings where reliability is crucial.In contrast, Raikov et al. (2024) explore a hybrid intelligence model that combines LLM capabilities with explainable AI (XAI) principles to enhance human-machine collaboration. Their approach emphasizes cognitive semantics, improving transparency and decision-making efficiency. The hybrid model's real-time adaptability addresses the needs of complex, regulated industries such as finance and healthcare, where trust in AI decisions is paramount. Academically, this study provides a valuable addition to XAI literature by demonstrating how LLMs can bridge the gap between AI autonomy and human oversight, making it a model for future human-AI interactions in complex business environments.Another significant study by Mariotti and colleagues (2024) examines the integration of LLMs with enterprise knowledge graphs to enhance data-driven decision-making. By enabling organizations to leverage knowledge graphs for more accurate and scalable data retrieval, this research provides a robust framework for businesses seeking efficient knowledge management systems. The academic contribution here lies in advancing the dialogue between LLMs and knowledge graphs, emphasizing ethical data handling and quality standards essential for industry applications. For enterprises, the study offers practical solutions to achieve streamlined data management, balancing automation with privacy and security. 2024) take a different approach, investigating LLMs' role in creative industries, specifically within fashion design. They introduce a hybrid intelligence model that supports creative processes, allowing AI to complement rather than replace human ingenuity. While LLMs in this field demonstrate potential in automating repetitive design tasks and enhancing customer personalization, the study reveals limitations in AI's ability to handle spatial and stylistic nuances. This study's academic contribution lies in promoting human-AI co-creation, inspiring further research into AI applications across diverse creative sectors, including media and marketing.Collectively, these studies not only illuminate LLMs' transformative potential in business but also highlight critical ethical and operational considerations. Ensuring accuracy, transparency, and data privacy are vital to responsibly integrating AI into business workflows. Future research should focus on enhancing LLM accuracy, refining hybrid intelligence models, and exploring creative AI applications, all while maintaining ethical standards. As LLMs evolve, interdisciplinary collaborations will be essential to harness their full potential, making AI an ethical, effective, and innovative force in the business world.

We thank Üçdal et al. for their thoughtful letter [1] advocating for the development and use of domain-specific large language models (LLMs) in healthcare in reference to our recent publication on the use of LLMs for identifying preoperative frailty among older adults using clinical notes [2]. They make interesting and valid points on how to ensure the use of artificial intelligence (AI) in medicine is accurate, applicable, and transparent, just like any clinical tools that are developed and become widely used to evaluate and treat patients. We agree that identifying or building tools that specifically excel in clinical applications will be key in the future of AI as clinical tools. General-purpose LLMs, while powerful, may fall short in the contextual understanding of medical text and handling of unique clinical language used in medicine. These categories of language models are typically trained on broad internet corpora that include only a small fraction of biomedical literature, electronic health records, and guideline-based knowledge. As a result, they may generate fluent but factually incorrect answers (e.g., hallucinations)—a phenomenon that is particularly problematic when applied to high-stakes clinical settings. In contrast, domain-specific models are specifically pre-trained on curated biomedical text, peer-reviewed literature, and structured health data and may potentially reduce hallucination rates, increase the precision of medical terminology, and align more closely with established standards of care. Our study is one example that shows how general-purpose models compared to specialized models tailored to clinical contexts may likely underperform in healthcare-related tasks. However, this is not always the case, as demonstrated by another study that showed similar performances between domain-specific and general-purpose language models for identifying the need for preoperative cardiac evaluations [3]. Furthermore, general-purpose language models may be further fine-tuned with clinical notes or with optimized prompt engineering to improve performance for healthcare-related tasks [4]. Regardless, the concept is the same, in that leveraging LLMs for clinical tasks must take into consideration the knowledge base of its underlying foundation model for developing accurate AI-based tools for medicine. We agree with the authors' point that we need to ensure international relevance when using LLMs as clinical tools. Even within healthcare itself, AI models trained on certain subpopulations may still not be accurate and exhibit bias when used on another patient population [5]. It follows that a model that performs well within one country, trained on one patient population, may not generalize globally, particularly when guidelines, documentation styles, and patient demographics vary. Just as we validate clinical guidelines across populations, so too must we evaluate LLMs to ensure safe, equitable application [6]. In order to properly use models, clinicians need to understand not only what the result is but why. In order to properly develop and use clinical tools, we must understand them—the ethical principle behind explainability. Moving forward, it is important if we use AI models that we maintain transparency, as Ucdal et al. point out, with interpretability mechanisms to understand what models learn and why. We will hold LLMs and other AI tools to the same standard as all clinical tools used in medicine. As with any medical advancement, those developing and implementing the tool have responsibility for clinical validation, usability testing, post-deployment monitoring, and ongoing iteration based on real-world data. As healthcare moves toward an increased demand and utilization, AI technologies such as LLMs have come into play to streamline and improve care in a world of increasing workload and decreasing resources. As we have seen in various aspects of healthcare, including our study using LLMs to identify a difficult-to-quantify state such as frailty, LLMs and other aspects of AI increasingly show great potential in improving our ability to care for patients. Like with any clinical tool we use, it must be proven to improve and not compromise care. Along those lines, it is also critical to apply tools that are relevant and designed to perform well. Careful steps forward to make sure only AI technologies appropriate to the proposed usage, such as domain-specific LLMs, careful testing, validation, and transparency of models will ensure we are improving care and not causing harm to our patients. In this way, clinicians can learn about and lead healthcare toward the best direction forward using a complex but powerful technology. Y.Q.Z. contributed to the concept design and preparation of the manuscript. R.A.G. contributed to the concept design and preparation of the manuscript. The authors have nothing to report. The authors declare no conflicts of interest. This publication is linked to a related Letter to the Editor article by Üçdal et al. To view this article, visit https://doi.org/10.1111/jgs.70171.

Background: Generative artificial intelligence (AI) is rapidly transforming various aspects of healthcare, including critical care nephrology. Large language models (LLMs), a key technology in generative AI, show promise in enhancing patient care, streamlining workflows, and advancing research in this field. Summary: This review analyzes the current applications and future prospects of generative AI in critical care nephrology. Recent studies demonstrate the capabilities of LLMs in diagnostic accuracy, clinical reasoning, and continuous renal replacement therapy (CRRT) alarm troubleshooting. As we enter an era of multiagent models and automation, the integration of generative AI into critical care nephrology holds promise for improving patient care, optimizing clinical processes, and accelerating research. However, careful consideration of ethical implications and continued refinement of these technologies are essential for their responsible implementation in clinical practice. This review explores the current and potential applications of generative AI in nephrology, focusing on clinical decision support, patient education, research, and medical education. Additionally, we examine the challenges and limitations of AI implementation, such as privacy concerns, potential bias, and the necessity for human oversight. Key Messages: (i) LLMs have shown potential in enhancing diagnostic accuracy, clinical reasoning, and CRRT alarm troubleshooting in critical care nephrology. (ii) Generative AI offers promising applications in patient education, literature review, and academic writing within the field of nephrology. (iii) The integration of AI into electronic health records and clinical workflows presents both opportunities and challenges for improving patient care and research. (iv) Addressing ethical concerns, ensuring data privacy, and maintaining human oversight are crucial for the responsible implementation of AI in critical care nephrology.

The emergence of generative artificial intelligence (GenAI) offers the potential to enhance health economics and outcomes research (HEOR) by streamlining traditionally time-consuming and labour-intensive tasks, such as literature reviews, data extraction, and economic modelling. To effectively navigate this evolving landscape, health economists need a foundational understanding of how GenAI can complement their work. This primer aims to introduce health economists to the essentials of using GenAI tools, particularly large language models (LLMs), in HEOR projects. For health economists new to GenAI technologies, chatbot interfaces like ChatGPT offer an accessible way to explore the potential of LLMs. For more complex projects, knowledge of application programming interfaces (APIs), which provide scalability and integration capabilities, and prompt engineering strategies, such as few-shot and chain-of-thought prompting, is necessary to ensure accurate and efficient data analysis, enhance model performance, and tailor outputs to specific HEOR needs. Retrieval-augmented generation (RAG) can further improve LLM performance by incorporating current external information. LLMs have significant potential in many common HEOR tasks, such as summarising medical literature, extracting structured data, drafting report sections, generating statistical code, answering specific questions, and reviewing materials to enhance quality. However, health economists must also be aware of ongoing limitations and challenges, such as the propensity of LLMs to produce inaccurate information ('hallucinate'), security concerns, issues with reproducibility, and the risk of bias. Implementing LLMs in HEOR requires robust security protocols to handle sensitive data in compliance with the European Union's General Data Protection Regulation (GDPR) and the United States' Health Insurance Portability and Accountability Act (HIPAA). Deployment options such as local hosting, secure API use, or cloud-hosted open-source models offer varying levels of control and cost, each with unique trade-offs in security, accessibility, and technical demands. Reproducibility and transparency also pose unique challenges. To ensure the credibility of LLM-generated content, explicit declarations of the model version, prompting techniques, and benchmarks against established standards are recommended. Given the 'black box' nature of LLMs, a clear reporting structure is essential to maintain transparency and validate outputs, enabling stakeholders to assess the reliability and accuracy of LLM-generated HEOR analyses. The ethical implications of using artificial intelligence (AI) in HEOR, including LLMs, are complex and multifaceted, requiring careful assessment of each use case to determine the necessary level of ethical scrutiny and transparency. Health economists must balance the potential benefits of AI adoption against the risks of maintaining current practices, while also considering issues such as accountability, bias, intellectual property, and the broader impact on the healthcare system. As LLMs and AI technologies advance, their potential role in HEOR will become increasingly evident. Key areas of promise include creating dynamic, continuously updated HEOR materials, providing patients with more accessible information, and enhancing analytics for faster access to medicines. To maximise these benefits, health economists must understand and address challenges such as data ownership and bias. The coming years will be critical for establishing best practices for GenAI in HEOR. This primer encourages health economists to adopt GenAI responsibly, balancing innovation with scientific rigor and ethical integrity to improve healthcare insights and decision-making.

This paper focuses on generative AI, a typical representative of contemporary artificial intelligence (AI) and artificial general intelligence (AGI), aiming to delve into the latest research progress in its basic theory. The research method involves a comparative analysis of the differences in underlying logic and formal understanding between traditional AI and Current AI, further exploring the distinctions between the three core viewpoints of traditional AI (symbolism, connectionism, behaviorism) and the three major schools of Current AI (generative AI/AGI based on large language models (LLMs) such as ChatGPT; new quality productive force AGI characterized by small models, such as I3DNA; and twin Turing machines based on dual formal understanding models that are compatible with both large and small models). The research reveals the core components of the basic theory of AI and AGI: bit-list logic, linkage functions, followed by generalized bilingualism or generalized translation based on digital and intelligent text with the three fundamental laws. The significance of this research lies in not only enhancing the interpretability of generative AI/AGI based on LLMs represented by ChatGPT but also providing generalized translations for the new quality productive force AGI characterized by small models and its complex theories of cosmic intelligence and the universal model series. At the same time, it demonstrates the potential of twin Turing machines as inclusive intelligent agents in integrating data, knowledge, computing power, algorithms, and human-computer mutual assistance in the new era of cognitive paradigms, laying the foundation for constructing super intelligent systems.

The rapid advancement of Large Language Models (LLMs) and their potential integration into autonomous driving systems necessitates understanding their moral decision-making capabilities. While our previous study examined four prominent LLMs using the Moral Machine experimental framework, the dynamic landscape of LLM development demands a more comprehensive analysis. Here, we evaluate moral judgments across 52 different LLMs, including multiple versions of proprietary models (GPT, Claude, Gemini) and open-source alternatives (Llama, Gemma), to assess their alignment with human moral preferences in autonomous driving scenarios. Using a conjoint analysis framework, we evaluated how closely LLM responses aligned with human preferences in ethical dilemmas and examined the effects of model size, updates, and architecture. Results showed that proprietary models and open-source models exceeding 10 billion parameters demonstrated relatively close alignment with human judgments, with a significant negative correlation between model size and distance from human judgments in open-source models. However, model updates did not consistently improve alignment with human preferences, and many LLMs showed excessive emphasis on specific ethical principles. These findings suggest that while increasing model size may naturally lead to more human-like moral judgments, practical implementation in autonomous driving systems requires careful consideration of the trade-off between judgment quality and computational efficiency. Our comprehensive analysis provides crucial insights for the ethical design of autonomous systems and highlights the importance of considering cultural contexts in AI moral decision-making.

Problem (Introduction). The rapid integration of generative artificial intelligence (AI) into education has created unprecedented opportunities for personalised learning, yet it has also raised serious concerns about the reliability of AI-generated content. Large language models (LLMs) optimise for plausibility rather than truth, and they can fabricate facts, citations or even legal cases (When AI Gets It Wrong: Addressing AI Hallucinations and Bias - MIT Sloan Teaching &amp; Learning Technologies, n.d.). Such hallucinations threaten academic integrity: students may unknowingly absorb falsehoods, while teachers could inadvertently reproduce inaccuracies in course materials. Empirical studies reveal that AI systems can hallucinate from less than 1 % up to 15–40 % of cases in educational tasks depending on the model and domain (Figure 1), and systematic reviews note that over‑reliance on AI dialogue systems is linked to diminished critical thinking, increased technology dependence and the spread of misinformation (Zhai et al., 2024). Purpose. This article aims to analyse the scope and causes of AI-generated misinformation in education and to develop evidence-based recommendations for mitigating these risks. It combines technical insights on model architecture and training data with pedagogical strategies to foster AI literacy. The goal is to ensure that AI enhances rather than undermines learning. Methods. A systematic literature review of over 80 sources, including scientific articles, policy documents (AI Act, UNESCO guidelines), and empirical studies, provided a theoretical foundation. Comparative analysis of hallucination rates across models (Makhno et al., 2025; Lelièvre et al., 2025) informed the quantitative assessment. The study also modelled mitigation strategies such as Retrieval‑Augmented Generation (RAG) and Chain‑of‑Verification and evaluated pedagogical interventions like lateral reading and AI literacy programmes. Results. The findings show that hallucinations stem from both internal (model architecture and context limitations) and external (biased or incomplete training data) factors. Even top models misinform 1–3% of the time, whereas widely used free systems can err 15–40% of the time when generating bibliographies or research proposals (Balch &amp; Blanck, 2024). Hallucinations manifest in various forms: logical errors, mathematical mistakes, fabricated sources and factual inaccuracies. Their educational consequences include decreased critical thinking, increased plagiarism (“AI‑giarism”) and a risk of spreading disinformation. Regulatory frameworks classify educational AI systems as high‑risk; Annex III of the EU AI Act lists educational AI systems for admissions, assessment and monitoring as high‑risk and sets obligations for accuracy, transparency and human oversight (Nguyen, 2025). Among mitigation strategies, RAG reduces hallucinations, while Chain‑of‑Verification and self‑consistency improve reliability. Pedagogically, teaching students lateral reading, updating academic policies, and redesigning assessments to require reflection and verification are essential. Originality. Unlike purely technical surveys or broad commentaries, this study bridges AI research with educational practice and policy, providing a holistic perspective tailored to Ukrainian higher education. It synthesises international findings with local realities, offers a taxonomy of AI errors, presents original visualisations (Table 1 and Figure 1), and proposes a multi-level framework combining technical, pedagogical and regulatory solutions. The article emphasises that AI hallucinations are not simply technical bugs but systemic challenges requiring cultural change. Conclusion. To harness AI’s benefits in education, stakeholders must recognise and mitigate the problem of misinformation. Improving models (via RAG, verification chains), enhancing AI literacy, and adhering to high-risk regulatory standards will help ensure that AI supports, rather than sabotages, learning. Future research should focus on domain-specific hallucination rates, real-time fact-checkers for Ukrainian-language content, and longitudinal studies on AI’s cognitive impact. Ultimately, balancing technological innovation with human oversight and ethical principles will determine whether AI becomes a trustworthy educational ally or a source of confusion.

Recent developments in AI, especially the spectacular success of Large Language models, have instigated renewed questioning of what remains distinctively human. As AI stands poised to take over more and more human tasks, what is left that distinguishes humans? One way we might identify a humanlike intelligence would be when we detect it telling lies. Yet AIs lack both the intention and the motivation to truly tell lies, instead producing merely bullshit. With neither emotions, embodiment, nor the social awareness that leads to a theory of mind, AIs lack the internal referents on which to judge truth or falsity. When we are deceived by our computers, we need to look for the hidden agent who benefits from the deception. 'Someday a computer will give a wrong answer to spare someone's feelings, and man will have invented artificial intelligence.' -Robert Breault Recent developments in AI, especially the spectacular success of Large Language Models (LLMs) such as ChatGPT, GPT4, and the image generating programs such as Midjourney and DALL-E, have instigated renewed questioning of what remains distinctively human. Dozens of companies are working to make robots as humanlike as possible. As Artificial Intelligence (AI) pioneer Nils Nilsson (2005) notes, 'achieving real human-level AI would necessarily imply that most of the tasks that humans perform for pay could be automated.' Elon Musk has suggested that we might have an Artificial General Intelligence (AGI) comparable to human intelligence by 2030. Google engineer Blake Lemoine, after conversing with Google's chatbot LaMDA, said he already detected sentience (though Google denied LaMDA had any such thing), and Lemoine is not alone. LLMs have reinvigorated the field of AI and, once again, numerous developers and commentators are saying AGI is right around the corner. As computers show themselves increasingly capable of taking * Noreen Herzfeld

As generative artificial intelligence (AI) tools and large language models (LLMs)-powered applications develop rapidly in the era of algorithms, it should be integrated thoughtfully to enhance English as a Foreign Language (EFL) teaching and learning without replacing learners’ critical thinking (CT). This study systematically analyzes the impact of generative AI tools and LLMs on language learners’ CT in EFL education using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to identify, evaluate, and synthesize relevant studies from 2022 to 2025. A thorough review of 15 selected studies focuses on generative AI tools and LLMs’ dual nature, research methods, main focuses, theory and models, limitations and challenges, and future directions in the field based on Web of Science (WoS), SCOPUS, ERIC, ProQuest, and Google Scholar. The findings identified generative AI tools and LLMs possessed both the potential to nurture and the risk of hindering CT in EFL education. 66.67% of studies reported generative AI tools and LLMs’ positive role in CT, while 33.33% of studies reported its negative role in CT. Furthermore, 3 types of research methods, 3 key themes of research focus, and 4 groups of theoretical perspectives were examined. However, 4 kinds of limitations in this field remain, including research scope, user dependency, generative AI reliability, and pedagogical integration. Future research can focus on assessing long-term effects, broadening research scope, promoting responsible AI use, and refining pedagogical strategies. Finally, Limitations, implications and future direction of this study were discussed.

Artificial intelligence (AI) hallucinations occur when large language models, such as chatbots or computer vision systems, generate outputs containing non-existent patterns, leading to inaccurate results. Also known as AI confabulations or delusions, these instances challenge expectations of appropriate responses from AI tools due to unrelated or pattern-lacking outputs, similar to human hallucinations. Addressing such issues with generative AI presents significant challenges despite ongoing efforts to resolve them.[1,2] CAUSES OF AI HALLUCINATIONS Various causes of AI hallucinations have been identified and include: Insufficient or biased training data: An AI model designed to assist anaesthesiologists in administering anaesthesia may be trained predominantly on data from patients of a certain demographic, such as adults of average weight. When faced with a paediatric patient or an obese patient, the AI model may possibly hallucinate dosage recommendations that are inaccurate or unsafe, as it lacks sufficient exposure to diverse patient populations.[3] Model complexity: A highly complex AI system tasked with monitoring vital signs during surgery may exhibit hallucinatory responses when encountering unusual physiological patterns. This complexity underscores the need for simpler models to avoid such hallucinations.[4] Lack of explainability (black box): An AI algorithm designed to predict anaesthesia induction times may produce unexpectedly long or short estimates without providing clear explanations for its predictions. In cases where anaesthesiologists cannot understand or verify the AI system’s reasoning, there is a risk of blindly following its recommendations, potentially leading to errors or patient harm. This highlights the urgent need for explainable AI in anaesthesia.[5] MULTIFACETED THREAT OF AI HALLUCINATIONS IN ANAESTHESIA An AI hallucination occurs when an AI system produces demonstrably incorrect or misleading outputs, appearing confident and plausible despite factually flawed. The possible impacts of AI hallucinations on anaesthesia domains are varied[6-9] [Table 1].Table 1: Examples of AI hallucinations’ possible impact on anaesthesia domainsMisdiagnosis and mistreatment: Hallucinations can misinterpret patient data, resulting in unnecessary interventions or delayed treatments. Medication errors: AI-driven systems may recommend incorrect drug dosages, impacting patient safety. Communication and documentation: Misinterpreted verbal commands or procedure details can hinder accurate documentation and patient safety. Research skewing: AI-driven analysis of anaesthesia data for research could be skewed by hallucinations, leading to misleading conclusions. Legal and ethical concerns: Liability: Who is responsible for the errors caused by AI hallucinations? This remains a complex question with no clear answer. Depending on the specific circumstances, potential targets include the AI developer, healthcare provider or hospital. Informed consent: How can patients be adequately informed about the risks of AI hallucinations in anaesthesia, given the technical complexity involved and the dynamic nature of AI outputs? Striking a balance between transparency and patient anxiety is crucial. Bias: AI algorithms can perpetuate societal biases, leading to discriminatory outcomes in health care. Imagine an AI system trained on biased data; it might recommend different treatments based on a patient’s race or socioeconomic background.[10-12] STRATEGIES TO MITIGATE AI HALLUCINATIONS Various mitigation strategies need to be adhered to for the impact of AI hallucination on health care [Figure 1].Figure 1: Impact of AI hallucination on health care and mitigation strategies. AI = artificial intelligenceHigh-quality, diverse training data: Utilising diverse datasets improves AI model accuracy and reduces hallucination risks. For example, research by Jones et al.[13] demonstrated how incorporating various demographic factors and medical histories in training data significantly improved the accuracy of an AI-driven diagnostic tool for skin cancer detection. Explainable AI: Developing transparent AI models aids in identifying and rectifying hallucinations. For instance, the explainable nature of a deep learning model used in financial fraud detection allowed analysts to trace back erroneous predictions to specific data points, enabling targeted adjustments to the model’s training data and architecture.[14] Human oversight and collaboration: Human involvement reduces hallucination risks, especially in sensitive domains like health care. Collaborative efforts between AI systems and human experts have effectively reduced hallucination risks.[15] Continuous monitoring and evaluation: Regular evaluation detects and addresses hallucinations promptly. Continuous monitoring of its AI-powered recommendation system and real-time user feedback analysis allows for swift identification and correction of hallucinated product suggestions, improving user satisfaction and trust.[16] Algorithmic auditing and regulatory frameworks: Establishing robust auditing mechanisms and regulatory frameworks ensures AI system’s accountability and reliability.[17] To conclude, AI hallucinations in anaesthesia pose risks of misdiagnosis, medication errors and skewed research outcomes. Prioritising diverse training data, embracing explainable AI, maintaining human oversight, continuous monitoring and regulatory frameworks are crucial in mitigating these risks and fostering trust in AI technologies in health care. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.

Advances in artificial intelligence, particularly in natural language processing, offer promising tools for addressing mental health challenges in online contexts, potentially identifying at-risk individuals and informing timely interventions. This study investigates the potential of Large Language Models (LLMs) for automatically triaging social media posts expressing psychological distress. Using a dataset of 425 Italian-language Reddit posts, we compared the triage performance of three state-of-the-art LLMs - ChatGPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro - with trained clinician assessment using an adapted version of the Mental Health Triage Scale (MHTS), a validated instrument used in psychiatric screening services. A zero-shot prompting approach, with and without role assignment (simulating a clinician's perspective), evaluated the models' capability to assess intervention urgency. Results revealed that LLMs consistently overestimated urgency compared to human raters, although correlations with human judgments were moderate to strong, with GPT-4o and Claude 3.5 Sonnet demonstrating higher agreement. GPT-4o achieved the best classification performance, highlighting its potential for this task. Claude 3.5 Sonnet showed high sensitivity but lower precision, indicating a tendency toward false positives, while Gemini 1.5 Pro exhibited more balanced but generally lower performance. These findings suggest that while LLMs show promise for mental health triage in social media, their tendency to overestimate urgency and model-specific variations in performance underscore the need for careful interpretation, and human oversight when applying LLMs in mental health contexts.

This paper introduces a new field of AI research called machine unlearning and examines the challenges and approaches to extend machine unlearning to generative AI (GenAI). Machine unlearning is a model-driven approach to make an existing artificial intelligence (AI) model unlearn a set of data from its learning. Machine unlearning is becoming important for businesses to comply with privacy laws such as General Data Protection Regulation (GDPR) customer’s right to be forgotten, to manage security and to remove bias that AI models learn from their training data, as it is expensive to retrain and deploy the models without the bias or security or privacy compromising data. This paper presents the state of the art in machine unlearning approaches such as exact unlearning, approximate unlearning, zero-shot learning (ZSL) and fast and efficient unlearning. The paper highlights the challenges in applying machine learning to GenAI which is built on a transformer architecture of neural networks and adds more opaqueness to how large language models (LLM) learn in pre-training, fine-turning, transfer learning to more languages and in inference. The paper elaborates on how models retain the learning in a neural network to guide the various machine unlearning approaches for GenAI that the authors hope can be built upon their work. The paper suggests possible futuristic directions of research to create transparency in LLM and particularly looks at hallucinations in LLMs when they are extended to do machine translation for new languages beyond their training with ZSL to shed light on how the model stores its learning of newer languages in its memory and how it draws upon it during inference in GenAI applications. Finally, the paper calls for collaborations for future research in machine unlearning for GenAI, particularly LLMs, to add transparency and inclusivity to language AI.