ARTIFICIAL INTELLIGENCE IN INDONESIAN JUDICIAL DECISIONS: A PANCASILA-BASED NORMATIVE MODEL WITH A COMPARATIVE APPROACH

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. Elsewhere, AI researcher Demis Hassabis and colleagues have explored how neuroscience and AI learn from and inspire each other, although so far more in one than the other, as and have the success of the current approaches to AI, there are still many shortcomings and as well as problems in It is useful to on one such as when AI does not as or or or that can to or when it on or information about the world, or when it has such as of all of which can to a of public shortcomings have captured the attention of the wider public and as well as among there is an on AI and In recent years, there has been a of to principles and approaches to AI, as well as involving and such as the on AI, that to best important has been the of with to and - in the and developing AI in both and as has been well in recent This is an important in its own but also with to the of the resulting AI and, in its intersections with more the other there are limitations and problems associated with the that AI is not capable of if could to more more or more general AI. 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Artificial Intelligence (AI) has become an influential force shaping modern society, raising urgent ethical questions about responsibility, fairness, transparency, and human dignity. The discussion explores the conceptual and philosophical foundations of AI ethics as a framework for ensuring that technological progress aligns with moral and social values. It traces the evolution of ethical thought from classical philosophy to contemporary digital governance and defines the core principles guiding responsible AI, including beneficence, non-maleficence, autonomy, justice, and explicability. Emphasizing a human-centered approach, the section examines the challenges of operationalizing ethics into AI design, deployment, and regulation. By grounding innovation in ethical responsibility, it argues that trust in AI depends not on technical superiority alone but on its moral alignment with the public good and social justice.

The article is devoted to the study of the impact of artificial intelligence (AI) on the formation of adaptive management strategies for enterprises in the context of global turbulence. In today's world, where economic, political and social instability have become the norm, enterprises face the need to respond quickly to changes in the external environment. The use of AI opens up new opportunities for efficient management, resource optimization, and increased competitiveness. The article presents the theoretical foundations of AI implementation in the field of management, including the concepts of big data, machine learning, decision support systems, and forecasting algorithms. Particular attention is paid to the mechanisms for integrating AI into management processes, including real-time data analysis, automation of routine operations, adaptation to environmental changes, and creation of personalized strategies for each level of management. The authors emphasize the challenges that arise when implementing AI, including the digital divide between large corporations and small businesses, high costs of technology integration, lack of qualified personnel, and issues of cybersecurity and ethical responsibility. To successfully overcome these challenges, specific recommendations are offered, including investments in human capital, the creation of an inclusive regulatory framework, and the development of international cooperation in the field of artificial intelligence. The article discusses real-life cases of AI implementation in various industries, such as the financial sector, logistics, manufacturing, and healthcare. The analysis of the experience of international companies that have implemented AI in their business processes shows a significant economic effect, which can reach a cost reduction of 20-30% and an increase in productivity of 15-25%. At the same time, the use of AI in Ukraine remains at an early stage, requiring significant investments in technological infrastructure, educational programs, and government support. The article emphasizes the prospects of using AI to formulate adaptive strategies. In particular, the authors emphasize the importance of integrating AI into decision-making processes, which allows enterprises to anticipate risks, avoid crises, and ensure sustainable development. The authors highlight such areas as the development of personalized strategies for customer interaction, forecasting changes in the market environment, and creating innovative business models. The key aspect of the paper is practical recommendations for enterprises seeking to integrate AI into their business processes. The authors propose a step-by-step approach to AI implementation, which includes analysing the organization's readiness for change, developing pilot projects, training staff, and evaluating efficiency. The article also outlines the prospects for cooperation between business, government, and scientific institutions to create an innovative ecosystem focused on AI implementation. The research results confirm that AI is not only a technological tool but also a strategic asset that changes the rules of the game in management. It allows companies not only to adapt to an unstable environment but also to use its capabilities to achieve competitive advantages. The authors believe that the development of AI technologies is a prerequisite for sustainable economic growth, especially in the context of Ukraine's integration into the global knowledge economy. Thus, the article makes a significant contribution to understanding the role of AI in modern enterprise management. The results of the study are of practical importance for scientists, entrepreneurs, and representatives of government agencies interested in implementing innovative technologies in their activities.

P5245Cost-saving diagnosis approach by artificial intelligence tool in patients with suspected coronary artery disease. The co-operative ARTICA registry database

This study presents a systematic literature review that examines the integration of artificial intelligence (AI) technologies into the disciplines of architecture and urban planning, within the context of sustainable cities. In this review conducted in accordance with the PRISMA protocol, 11 academic publications were evaluated through keywords identified in the Web of Science and Scopus databases. The literature has been examined under five thematic headings: AI applications in urban planning processes, Decision support systems and scenario development, Social participation and governance, Conceptual- speculative approaches, and the Sustainability perspective. Methodologically, the study has focused on both technical applications and social and ethical dimensions with an interdisciplinary approach. The literature shows that AI is effective in areas such as data-intensive decision-making, multi-scenario analysis, and performance optimization in planning and design processes. However, significant gaps still exist in the areas of social engagement, ethical responsibility, and stakeholder diversity. The speculative design approach, on the other hand, has the potential to go beyond current practices by combining possible future scenarios shaped by AI with architectural and urban visions. In this context, the study poses the following fundamental research questions: (1) How are AI technologies integrated into the fields of architecture and urban planning? (2) What themes are prominent at the intersection of AI and speculative design? (3) How are AI applications evaluated in terms of social participation and ethical governance with respect to sustainable city goals? These questions provide an essential framework for forward-looking creative and critical urban design approaches. This review addresses a notable gap in the literature by examining the role of AI in sustainable urban design, providing insights into underexplored areas such as participatory governance and ethical AI practices. The findings contribute to both academia and practice by highlighting how AI-driven approaches can enhance sustainable city planning and by identifying areas where further innovation is needed, thereby informing future design strategies.

Cancel culture is a notable, but not well theorised social phenomenon, widely understood as a way of punishing those in the public eye who are perceived to do or say the wrong thing. This study aimed to elucidate the cancelling process and the role of morality‐ and emotion‐related traits in this process. Adult social media users ( n = 298) undertook an online survey containing cancel culture–related vignettes and scales. As hypothesised, moral outrage was found to positively mediate the relationship between transgression perception and cancel culture engagement. Moral sensitivity also had a positive correlation with transgression perception. Moral sensitivity was not related to cancel culture engagement, however, whilst emotion regulation difficulty and moral identity did not moderate the relationship between moral outrage and cancel culture engagement. These findings suggest that those sensitive to moral problems, who feel strong negative emotions, are more likely to engage in cancel culture. Furthermore, findings indicate that Crockett’s (2017) online moral outrage theory has some explanatory power, and that moral outrage and sensitivity may be important to consider when regulating cancel culture. Features of social media and artificial intelligence can be potential obstacles in this process, however, and this warrants consideration in instances where preventing cancellation is desirable. Future research is needed to corroborate these findings and evaluate other prospective cancel culture theories.

This article examines the integration of Artificial Intelligence (AI) in judicial decision-making through legal pragmatism principles. The study analyses how AI can enhance judicial decisions while addressing barriers to justice, focusing on three key areas: AI's current legal applications, legal pragmatism's theoretical framework, and their practical synthesis in judicial systems. The research first explores AI classification in legal contexts, examining how machine learning and generative AI process legal data, predict outcomes and model scenarios. It then analyses legal pragmatism's core principles – anti-foundationalism, contextualism, and consequentialism and their impact on judicial decision-making. Finally, it demonstrates how AI can enhance pragmatic judicial decisions through precedent analysis, consequence evaluation, context assessment, bias mitigation, and discretion reduction. Using deductive, analytical, and inductive methods, this study shows that AI integration, guided by pragmatic principles, can improve judicial efficiency while promoting transparency and consistency in legal reasoning.

Measuring AI preparedness in health professions education: Evidence from a national survey of medical radiation science students and new graduates.

Indonesia as a democratic country that emphasizes the rule of law as the main foundation, makes Pancasila as the moral and philosophical foundation for the constitution and laws and regulations. This research aims to evaluate the consistency of Government Regulation Number 28 Year 2022 on the Management of State Receivables with the principles of Pancasila, as well as to assess the extent to which its practical implementation reflects the moral and philosophical values of Pancasila. The method used in this research is juridical-normative with a conceptual and statutory approach, accompanied by qualitative descriptive analysis of relevant statutory documents and legal doctrine. The results of the analysis highlight the importance that Government Regulation No. 28 Year 2022 must comply with the existing legal hierarchy, as well as consistently reflect the principles of Pancasila as a moral and philosophical foundation in the regulation of state receivables. Nonetheless, the addition of new rules concerning liability for state receivables raises debates about consistency and social justice. Therefore, it is important for the government to ensure that the implementation of the rules not only considers the legal and financial aspects, but also the social and humanitarian impacts on the individuals involved. Success in regulating state receivables should be measured by the extent to which the principles of Pancasila are respected and social justice is realized in the process, which will help strengthen the foundations of democracy and the rule of law in Indonesia.

Since artificial intelligence has completed the process from the auxiliary tool of human creation to the independent creation completion of works with formal appearance, it has brought many legal issues that have caused widespread controversy. Among them, whether artificial intelligence has the qualification of legal subject and whether the products of artificial intelligence should be protected by law is the focus of the problem. In the legal circle, the involvement of the theme of “non-anthropocentrism” can be traced back to the debate between animal legal personality and non-human ecological rights. The Naruto v. Slater Monkey selfie case and the Pigcasso light people’s debating about animal copyright, and artificial intelligence provides a new research perspective and reinvigorates the research on animal copyright. By means of the analogy research of animals, humans and artificial intelligence, this paper explores the rationality, necessity and feasibility of investing non-human beings with quasi-legal subject qualification in the special subdivision field of law—copyright. Quasi-legal subject qualification means that artificial narrow intelligence and animals are endowed with judicial capacity for copyrights and limited capacity to act. At the same time, the designers of artificial intelligence, animal breeders and the government and so on serve as the quasi-guardian of artificial intelligence and animals. In addition, artificial general intelligence and artificial super general intelligence are endowed with completely independent legal capacity to act, and the quasi-guardian system is terminated. The quasi-guardian system is perfectly compatible with the existing legal framework from the perspective of development. It protects the ownerless intellectual property from the free lift, thereby helping avoid the tragedy of the commons. Furthermore, it solves the problem that animals and artificial narrow intelligence cannot independently safeguard their rights and provides a forward-looking theoretical model for the system construction of non-human copyright.

Purpose. The study aims to understand artificial intelligence as a socio-cultural phenomenon and its impact on education, where the spiritual sphere of humanity, moral norms, values, and human cognitive abilities are preserved, transferred as well as reproduced. A new discourse on the interaction of artificial and authentic human intelligence becomes inevitable, which has led to a situation of uncertainty. Changes in the socio-cultural environment under the influence of artificial intelligence increase potential threats to the educational space, which stimulates to find the ways to eliminate them. Theoretical basis. Various approaches of classical and postmodern philosophical heritage were taken as a theoretical basis for the research. The originality of the study is in the interpretation of artificial intelligence as a modern form of alienation of essential human characteristics in the socio-cultural context of information technology. The expansion of artificial intelligence raises awareness of the existential threat to the basic socio-cultural, moral and ethical principles of humanism. It is proved that various forms of alienation in the current existing socio-cultural space are typical of our reality, which changes the system of values, moral principles, and social organization of the community. Conclusions. In conclusion, it is proved that AI is a natural stage of scientific and technological progress, which reflects its secondary, derivative nature from human (authentic) intelligence. Human intelligence will always have advantages over AI due to its ability to create, communicate socially and culturally, and be emotional. The dilemma of the counterbalance between human and artificial intelligence is perceived mainly at the emotional level of people. The millennial understanding of the primacy of the creator over his creation can traditionally overcome this contradiction. The universality of human thinking is an undeniable advantage of human intelligence and a guarantee of its, i.e. our, priority.

Artificial intelligence (AI) is a rapidly developing technology that has the potential to create previously unimaginable chances for our societies. Still, the public’s opinion of AI remains mixed. Since AI has been integrated into many facets of daily life, it is critical to understand how people perceive these systems. The present work investigated the perceived social risk and social value of AI. In a preliminary study, AI’s social risk and social value were first operationalized and explored by adopting a correlational approach. Results highlighted that perceived social value and social risk represent two significant and antagonistic dimensions driving the perception of AI: the higher the perceived risk, the lower the social value attributed to AI. The main study considered pretested AI applications in different domains to develop a classification of AI applications based on perceived social risk and social value. A cluster analysis revealed that in the two-dimensional social risk × social value space, the considered AI technologies grouped into six clusters, with the AI applications related to medical care (e.g., assisted surgery) unexpectedly perceived as the riskiest ones. Understanding people’s perceptions of AI can guide researchers, developers, and policymakers in adopting an anthropocentric approach when designing future AI technologies to prioritize human well-being and ensure AI’s responsible and ethical development in the years to come.

AI technologies (e.g., systems, applications, platforms, tools), from voice assistants to personalized learning environments, are becoming increasingly pervasive in the daily lives and educational experiences of young children. In light of this trend, the question of how we should design, deploy, and regulate AI for young children takes on heightened urgency and significance. With this question in mind, I offer a reflective and conceptual perspective with a threefold goal. First, I examine how certain current AI tools used by young children (ages 3-8)—often shaped by the assumptions, priorities, and even biases of adult designers—were not developed with consideration of child users, thereby failing to incorporate the perspectives and developmental needs of young children. Second, I further scrutinize ethical concerns, including data privacy, bias, agency, trust, and—above all—young children’s safety and well-being in AI use. Third, I advocate for a child-centered approach to AI design, deployment, and regulation, one that respects young children’s rights, prioritizes their safety and well-being, and empowers them as AI users. Furthermore, drawing on research evidence and practical examples, I call on key stakeholders, especially AI developers, policymakers, researchers, school leaders, educators, and families, to rethink (through critical reflection), reimagine (through a visionary lens), and reshape (through transformative action) AI use for, with, and by children. To guide these efforts, I propose a three-pronged conceptual framework: (1) developmental appropriateness, (2) ethical responsibility, and (3) child-centeredness. Keywords: Artificial intelligence (AI); AI tools; children; developmental appropriateness, ethical responsibility, child-centeredness.

The need of more sophisticated decision making tools in modern businesses is largely influenced by the pace and intricacy of new business markets. The use of Artificial Intelligence (AI) is transforming Decision Support Systems (DSS), inforamtion technology, and maagement strategies. This paper seeks to analyze how AI technology is changing the business decision making processes with and emphasis on its use in DSS. The application of AI, machine learning (ML), natural language processing (NLP), and predictive analysis have radically transformed the efficiency and effectiveness of decision making processes. These changes enable timely and accurate relavant decisions which improves overall efficiency and responsiveness to market dynamics. AI empowered DSS are more valuable in retail, manufactury, and finance industries where complex decisions are accompanied by time sensitive data. AI application in these fields not only enhanced the decision making accuracy, but also greatly minimized adverse human factors, dwindling resources, and time wastage. This research was carried out using a mixed-method approach of qualitative and quantitative techniques. The qualitative case study method consists of multi-industry studies which have implemented AI based DSS systems, aiding in understanding the processes, complications, and results of such systems. Moreover, additional data was collected through Industry expert and decision-maker surveys and interviews to evaluate the effects of AI on business functions and processes. The work furthers comprehension regarding the influence of AI automation on DSS integration by explaining the value added from more accurate, scalable, and responsive decision-making from AI technologies. Automated decision systems of AI driven DSS do not only facilitate decisions but also forecast critical insights to help organizations strategically plan, avert threats, and succeed. This underscores what is increasingly becoming a central concern in business strategy and policy formulation - the application of AI in operational and strategic decision-making processes of firms.

This study aims to uncover the underlying psychological mechanism through which individuals attribute ethical responsibility to conversational artificial intelligence (AI). Furthermore, this study delves into the implications of AI’s unethical behavior on consumer evaluation. In Study 1, the results showed that participants in the high (vs. low) anthropomorphic AI condition judged greater responsibility for unethical behavior by AI, while lessening the AI developer’s ethical responsibility. In addition, the effect of anthropomorphism on ethical responsibility was mediated by perceived freewill. In Study 2, a significant interaction effect between perceived freewill and communication strategy is found, suggesting that when a high degree of AI freewill is perceived, the accommodative (vs. defensive) communication strategy is more effective in reducing the perception of the unethical behavior of AI. Conversely, the defensive strategy was more effective when perceived freewill was low. This study reveals the psychological mechanism through which individuals expect ethical responsibility from conversational AI, which has theoretical implications for broadening the understanding of human–AI interaction, and discusses the practical implications of proposing an AI communication strategy.