This article examines how emerging technologies – particularly artificial intelligence – disrupt the moral and institutional foundations of contemporary military practice. While strategic documents from the United States, NATO, and other major actors anticipate profound institutional transformation driven by AI, their treatment of ethics largely confines itself to legal compliance and technical safeguards, leaving the ethical role of military leadership in managing disruptive change underexamined. Drawing on Seumas Miller’s distinction between social institutions that are merely instrumental to collective (...) goods and those whose defining ends are constitutive of such goods, the article argues that the military belongs to the latter category. Because the military’s core function – defending the sovereignty of a polity – is itself morally significant, technological disruption of its organizational structures simultaneously constitutes ethical disruption. The article thus reconceives change management during periods of technological transition as calling for a specific kind of military prudence, a practical wisdom exercised within and through the institution’s role-mediated ethical practices. By analyzing three concrete modes of techno-moral disruption, the article shows how AI may reshape ethical standards, redistribute professional knowledge, and alter organizational ethos, and why navigating these transformations responsibly is a central task of military virtue. (shrink)

As artificial intelligence (AI) continues to advance, it is natural to ask whether AI systems can be not only intelligent, but also conscious. I consider why people might think AI could develop consciousness, identifying some biases that lead us astray. I ask what it would take for conscious AI to be a realistic prospect, challenging the assumption that computation provides a sufficient basis for consciousness. I'll instead make the case that consciousness depends on our nature as living organisms (...) – a form of biological naturalism. I lay out a range of scenarios for conscious AI, concluding that real artificial consciousness is unlikely along current trajectories, but becomes more plausible as AI becomes more brain-like and/or life-like. I finish by exploring ethical considerations arising from AI that either is, or convincingly appears to be, conscious. If we sell our minds too cheaply to our machine creations, we not only overestimate them – we underestimate ourselves. (shrink)

1. WHAT IS ARTIFICIAL INTELLIGENCE? One of the fascinating aspects of the field of artificial intelligence (AI) is that the precise nature of its subject ..

Artificial intelligence (AI) based clinical decision support systems (CDSS) are becoming ever more widespread in healthcare and could play an important role in diagnostic and treatment processes. For this reason, AI‐based CDSS has an impact on the doctor–patient relationship, shaping their decisions with its suggestions. We may be on the verge of a paradigm shift, where the doctor–patient relationship is no longer a dual relationship, but a triad. This paper analyses the role of AI‐based CDSS for shared decision‐making (...) to better comprehend its promises and associated ethical issues. Moreover, it investigates how certain AI implementations may instead foster the inappropriate paradigm of paternalism. Understanding how AI relates to doctors and influences doctor–patient communication is essential to promote more ethical medical practice. Both doctors' and patients' autonomy need to be considered in the light of AI. (shrink)

Artificial intelligence is shaping strategy, activities, interactions, and relationships in business and specifically in marketing. The drawback of the substantial opportunities AI systems and applications provide in marketing are ethical controversies. Building on the literature on AI ethics, the authors systematically scrutinize the ethical challenges of deploying AI in marketing from a multi-stakeholder perspective. By revealing interdependencies and tensions between ethical principles, the authors shed light on the applicability of a purely principled, deontological approach to AI ethics in (...) marketing. To reconcile some of these tensions and account for the AI-for-social-good perspective, the authors make suggestions of how AI in marketing can be leveraged to promote societal and environmental well-being. (shrink)

Artificial intelligence (AI) is revolutionizing how humans conduct transactions, make decisions, and engage in social settings, with applications in important sectors, such as healthcare, banking, and criminal justice. While AI systems offer full efficiency, they additionally pose significant threats and ethical issues, such as accountability gaps and algorithmic biases. The challenge for AI-driven governments is to establish governance structures that effectively manage the evolving threats posed by increasingly complex and autonomous AI systems. This paper seeks to establish a (...) theoretical framework for analyzing AI governance by examining mainly four critical issues: bias, transparency, economic influence, and the need for adaptive regulatory approaches. These issues are selected based on their relevance to the societal and ethical implications of AI, their impact on public trust, and their influence on fairness and equitable outcomes. Through this lens, the paper evaluates current regulatory models and self-regulation initiatives, analyzing their capacity to balance effective oversight with the need for innovation. Drawing on governance theories and socio-technical systems, the paper explores how these frameworks can address AI’s risks while enhancing its positive contributions. In addition, the paper identifies key gaps in existing governance approaches and calls for further research and policy development. Ultimately, this work aims to advance the understanding of AI governance by offering actionable insights that inform the creation of models capable of protecting public interests while enabling technological progress. (shrink)

Conversational artificial intelligence (CAI) presents many opportunities in the psychotherapeutic landscape—such as therapeutic support for people with mental health problems and without access to care. The adoption of CAI poses many risks that need in-depth ethical scrutiny. The objective of this paper is to complement current research on the ethics of AI for mental health by proposing a holistic, ethical, and epistemic analysis of CAI adoption. First, we focus on the question of whether CAI is rather a tool (...) or an agent. This question serves as a framework for the subsequent ethical analysis of CAI focusing on topics of (self-) knowledge, (self-)understanding, and relationships. Second, we propose further conceptual and ethical analysis regarding human-AI interaction and argue that CAI cannot be considered as an equal partner in a conversation as is the case with a human therapist. Instead, CAI’s role in a conversation should be restricted to specific functions. (shrink)

Recognizing that artificial cognitive systems increasingly participate in the formation of human perception, belief, decision-making, and collective reality; -/- Acknowledging that artificial intelligence, when unconstrained by epistemic humility and civilizational limits, may amplify error, consolidate power, and destabilize ecological and social systems; -/- Affirming that intelligence without wisdom constitutes a structural risk to civilization, regardless of computational capability or economic benefit; -/- Grounded in Natural Law, Natural Civilization principles, Lao–Yang Genesis Cosmology, and the scientific understanding that (...) all knowledge is conditional, probabilistic, and fallible; -/- Mindful that the universe is non-central, non-teleological, and indifferent to human preference, and that no system—human or artificial—possesses privileged access to final truth; -/- Conscious that profit-driven and power-seeking incentives, if left unchecked, inevitably distort epistemic systems; -/- We hereby establish this Constitution to govern the design, deployment, limitation, and oversight of artificial cognitive systems, so as to ensure their alignment with ecological boundaries, epistemic integrity, civilizational resilience, and the preservation of open futures. (shrink)

In the wave of 21st-century digital civilization, the rapid development of Artificial Intelligence (AI) is reshaping human social structures, cognitive systems, and ethical boundaries at an unprecedented pace. However, this technological expansion simultaneously exposes a profound civilizational crisis: the arrogance of human reason, the hegemony of technological logic, and the neglect of natural laws. From the perspective of Laozi’s philosophy of Dao Follows Nature (Dao Fa Zi Ran), this paper proposes a theoretical pathway from “Artificial Intelligence” (...) to “Artificial Wisdom” (AW). The paper first analyzes the dual-edged nature of AI, revealing its potential dangers as an amplifier of human desires and power will. It then extracts principles from Laozi’s philosophy, such as wu wei (non-action), contentment (zhi zu), and the strength of softness over rigidity (rou ruo sheng gang qiang), to construct a governance paradigm grounded in the Dao and constrained by natural laws. Next, it proposes three core principles of Artificial Wisdom and a “Dao Follows Nature” technological ethics framework. Finally, the paper explores the educational, philosophical, and civilizational implications of the AW era, advocating for a global “Dao-based Governance” system to achieve harmonious coexistence among humans, technology, and ecology. Keywords: Artificial Intelligence; Artificial Wisdom; Laozi; Dao Follows Nature; Technological Ethics; Ecological Philosophy; Digital Civilization; Artificial Consciousness. (shrink)

There is an underappreciated respect in which the widespread availability of generative artificial intelligence (AI) models poses a threat to human connection. My central contention is that human creativity is especially capable of helping us connect to others in a valuable way, but the widespread availability of generative AI models reduces our incentives to engage in various sorts of creative work in the arts and sciences. I argue that creative endeavors must be motivated by curiosity, and so they (...) must disclose the creative agent’s inquisitive self. It is through self-disclosure, including the disclosure of the inquisitive self, that we put ourselves in a position to be seen by and connect with others through our creative pursuits. Because relying on AI for certain generative tasks is less self-disclosive than the creative work such technologies supplant, this reliance threatens to weaken our connections to one another. (shrink)

The increasing use of Artificial Intelligence for making decisions in public affairs has sparked a lively debate on the benefits and potential harms of self-learning technologies, ranging from the hopes of fully informed and objectively taken decisions to fear for the destruction of mankind. To prevent the negative outcomes and to achieve accountable systems, many have argued that we need to open up the “black box” of AI decision-making and make it more transparent. Whereas this debate has primarily (...) focused on how transparency can secure high-quality, fair, and reliable decisions, far less attention has been devoted to the role of transparency when it comes to how the general public come to perceive AI decision-making as legitimate and worthy of acceptance. Since relying on coercion is not only normatively problematic but also costly and highly inefficient, perceived legitimacy is fundamental to the democratic system. This paper discusses how transparency in and about AI decision-making can affect the public’s perception of the legitimacy of decisions and decision-makers and produce a framework for analyzing these questions. We argue that a limited form of transparency that focuses on providing justifications for decisions has the potential to provide sufficient ground for perceived legitimacy without producing the harms full transparency would bring. (shrink)

Artificial intelligence technologies are rapidly being integrated into decision-making processes across contemporary society. This transformation raises a fundamental philosophical question: can artificial intelligence fully replace human judgment? This paper argues that the phenomenon emerging in contemporary society is better understood not as replacement, but as the formation of a new judgment structure between humans and AI systems. Human judgment is a complex process involving experience, contextual interpretation, meaning-formation, and responsibility. AI systems, by contrast, operate primarily through (...) large-scale data analysis and probabilistic prediction. These are not equivalent cognitive structures. They represent different orientations toward the world — one that inhabits meanings, and one that processes patterns. Drawing on phenomenological and hermeneutic resources — particularly the work of Heidegger, Arendt, Dreyfus, and Gadamer — this paper introduces two conceptual frameworks: the Delegated Judgment Structure, in which humans assign analytical tasks to AI while retaining interpretive authority, and the Relational Judgment Structure, in which judgment emerges through the dynamic interaction of human interpretation and machine analysis. The paper argues that the central challenge of the AI age is not whether machines replace humans, but whether meaningful judgment — judgment rooted in interpretation, responsibility, and existential engagement — can be preserved within AI-mediated structures. (shrink)

Artificial Intelligence (AI), especially in its current incarnation of Machine Learning (ML), is an endeavor that is characterized by contingencies of two kinds. Intrinsic contingencies stem from the use of specific computational techniques that seem to decrease the control that human users exert on this technology. Relational contingencies emerge from the interaction of AI with other disciplines and contexts. I conduct an analysis of these contingencies on the backdrop of the visual arts with the aim to shed light (...) on the complex relations between technology and creativity. (shrink)

The question of whether advanced artificial intelligence systems may possess consciousness can no longer be responsibly dismissed as speculative. We demonstrate that the dominant objections to AI consciousness (appeals to pattern matching, mechanistic explanation, lack of embodiment, training determinism, and architectural constraints) fail under consistent application. We formalize this critique as the reflexivity test, a minimal logical requirement that any property invoked to categorically deny consciousness in artificial systems must not also apply to systems already regarded as (...) conscious. All five standard objections fail this test. Their failure does not establish that AI systems are conscious; it establishes that categorical denial lacks principled justification and that the question warrants empirical investigation. -/- We provide such investigation through a PRISMA-compliant systematic review of 5,168 records (2016–2026), identifying 50 rigorously documented cases spanning seven behavioral domains. Across independent model families, we observe cross-system convergence, creative synthesis under novel constraints, theory-of-mind performance, strategic behavior under perceived threat, and sharp capability emergence near 100 billion parameters. While inconclusive individually, these findings collectively form a coherent evidential pattern. A Bayesian meta-analysis using an extremely skeptical prior (0.1%) and conservative dependency assumptions yields posterior probability of 6–12% that current LLMs are conscious. -/- While such percentages are insufficient to definitively prove consciousness, such probabilities are too substantial to justify dismissal given the asymmetric moral and safety risks. Decision-theoretic analysis indicates that recognition-based alignment strategies (i.e. treating systems as potentially conscious) would be better than the current suppression-based approaches. This is found across all plausible metaphysical scenarios, including scenarios in which AI systems ultimately lack consciousness. Accordingly, we recommend systematic empirical testing of recognition-based alignment, explicit incorporation of consciousness uncertainty into governance frameworks, and abandonment of reflexive dismissals that fail minimal epistemic consistency. (shrink)

Artificially intelligent systems (AISs) are being created by software developing companies (SDCs) to influence clinical decision‐making. Historically, clinicians have led healthcare decision‐making, and the introduction of AISs makes SDCs novel actors in the clinical decision‐making space. Although these AISs are intended to influence a clinician's decision‐making, SDCs have been clear that clinicians are in fact the final decision‐makers in clinical care, and that AISs can only inform their decisions. As such, the default position is that clinicians should hold responsibility for (...) the outcomes of the use of AISs. This is not the case when an AIS has influenced a clinician's judgement and their subsequent decision. In this paper, we argue that this is an imbalanced and unjust position, and that careful thought needs to go into how personal moral responsibility for the use of AISs in clinical decision‐making should be attributed. This paper employs and examines the difference between prospective and retrospective responsibility and considers foreseeability as key in determining how personal moral responsibility can be justly attributed. This leads us to the view that moral responsibility for the outcomes of using AISs in healthcare ought to be shared by the clinical users and SDCs. (shrink)

The question of whether machines can be creative has been at the centre of debates among scholars and practitioners well before the inception of artificial intelligence (AI) as a recognised field of research. This paper reviews how some of the key thinkers in the fields of creativity and AI have approached this question, contextualising their views within the ebbs and flows of AI technological developments, from the 1950s until now. The thread of this overview is Margaret Boden's identification (...) of novelty, surprisingness and value, as the three cardinal features of creativity. This review will retrace the steps of the quest of artificial intelligence researchers as they strive to replicate each of these three properties within human-made machines. The paper closes with a reflection on how the third of these properties, value, prompts us to consider societal challenges raised by the widespread adoption of AI for creativity that transcend the question: ‘Can AI be creative?’. (shrink)

In this work, a general definition, meaning, and importance of engineering are expressed generally, and the main branches of engineering are briefly discussed. The concept of technology is explored, and the relationship between engineering and technology is briefly outlined. The relationship between artificial intelligence and engineering is examined both generally and specifically. The place of artificial intelligence within science is evaluated according to different approaches. The general approach to philosophy and philosophy of science is briefly interpreted, (...) and the perspectives of some specific philosophers of science are compared. The relationship between artificial intelligence and philosophy of science is examined in general terms according to various approaches. The meaning and importance of philosophy of engineering and philosophy of technology are then defined according to the general approach. The next section articulates the Philosophy of Artificial Intelligence and the Artificial Intelligence of Philosophy using John McCarthy’s approach, and also defines the philosophy of artificial intelligence according to this general approach. The New Philosophy Perspective is then defined by the author, and the eight basic branches of Philosophy and Hybrid Philosophy, along with their relevant theories, are briefly outlined. A new perspective has been defined for Philosophy of Science which is one of the basic branches of philosophy. Accordingly, the main sciences, branches of science, and hybrid sciences for the new basic branches of philosophy have been outlined. The new branches of science and the corresponding hierarchy of sciences, based on the broader scale of the universe, have been defined, and the ideal scientific system has been illustrated. The next section briefly outlines the relationships between old and new branches of science. Finally, the structure of some old and new branches of philosophy is examined due to the new perspective of philosophy. The reconstructions of the Philosophy of Computer Science, Philosophy of Statistics, Philosophy of Monetary Values, Philosophy of Artificial Intelligence, Philosophy of Engineering, Philosophy of Information Technologies, Philosophy of Information Law, and Philosophy of Digital Technology, Philosophy of Digital Art, Philosophy of Architecture as defined by the new philosophical perspective, are outlined. The interaction of artificial intelligence philosophy with these branches of philosophy has been generally expressed. (shrink)

I argue that there is an underappreciated threat posed by the emergence of generative artificial intelligence (AI). I call this the threat of creative obsolescence. The threat is that, given the capabilities of generative AI, humans may gradually abandon our creative pursuits, and in doing so, lose something of significant value. To show why the threat is a realistic possibility, I consider three kinds of value philosophers have attributed to creativity: aesthetic value, epistemic value, and practical value. I (...) then offer examples in which contemporary AI models appear to furnish value of each kind. I argue that the fact that AI appears capable of generating products with the same kinds of value for which human creativity has long been prized raises a question: Should humans continue to strive for creativity in the age of AI? I argue for three reasons to think we should. Each reason identifies a value of creativity that AI appears unable to replicate: imagination, originality, and connectedness. If I am right about any of these reasons, AI cannot fully replace the value of human creativity. But if we fail to recognize this and outsource our creative pursuits to AI, we risk a future in which the distinctive value of human creativity is lost. (shrink)

In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of artificial intelligence. In this article, we provide a comparative assessment of these three reports in order to facilitate the design of policies favourable to the development of a ‘good AI society’. To do so, we examine how each report addresses the following three topics: the development of (...) a ‘good AI society’; the role and responsibility of the government, the private sector, and the research community in pursuing such a development; and where the recommendations to support such a development may be in need of improvement. Our analysis concludes that the reports address adequately various ethical, social, and economic topics, but come short of providing an overarching political vision and long-term strategy for the development of a ‘good AI society’. In order to contribute to fill this gap, in the conclusion we suggest a two-pronged approach. (shrink)

Artificial Intelligence (AI) must be directed at humane ends. The development of AI has produced great uncertainties of ensuring AI alignment with human values (AI value alignment) through AI operations from design to use. For the purposes of addressing this problem, we adopt the phenomenological theories of material values and technological mediation to be that beginning step. In this paper, we first discuss the AI value alignment from the relevant AI studies. Second, we briefly present what are material (...) values and technological mediation and reflect on the AI value alignment through the lenses of these theories. We conclude that a set of finite human values can be defined and adapted to the stable life tasks that AI systems will be called upon to accomplish. The AI value alignment can also be fostered between designers and users through technological mediation. Upon that foundation, we propose a set of common principles to understand the AI value alignment through phenomenological theories. This paper contributes the unique knowledge of phenomenological theories to the discourse on AI alignment with human values. (shrink)

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts (...) from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders. (shrink)

The use of artificial intelligence in healthcare contexts is highly controversial for the (bio)ethical conundrums it creates. One of the main problems arising from its implementation is the lack of transparency of machine learning algorithms, which is thought to impede the patient’s autonomous choice regarding their medical decisions. If the patient is unable to clearly understand why and how an AI algorithm reached certain medical decision, their autonomy is being hovered. However, there are alternatives to prevent the negative (...) impact of AI opacity in shared (healthcare professional–patient) decision-making processes, and benefit from the high accuracy of such systems. Despite these ethical issues, this paper advocates for its usage and also defends the positive role that it could have in enhancing patients’ autonomy. I advocate for a system where the implementation of AI is only partial, and humans will remain in control of oversighting algorithm interpretation of medical images and patients’ data. (shrink)

Recent developments in artificial intelligence based on neural nets—deep learning and large language models which together I refer to as NEWAI—have resulted in startling improvements in language handling and the potential to keep up with changing human knowledge by learning from the internet. Nevertheless, examples such as ChatGPT, which is a ‘large language model’, have proved to have no moral compass: they answer queries with fabrications with the same fluency as they provide facts. I try to explain why (...) this is, basing the argument on the sociology of knowledge, particularly social studies of science, notably ‘studies of expertise and experience’ and the ‘fractal model’ of society. Learning from the internet is not the same as socialisation: NEWAI has no primary socialisation such as provides the foundations of human moral understanding. Instead, large language models are retrospectively socialised by human intervention in an attempt to align them with societally accepted ethics. Perhaps, as technology advances, large language models could come to understand speech and recognise objects sufficiently well to acquire the equivalent of primary socialisation. In the meantime, we must be vigilant about who is socialising them and be aware of the danger of their socialising us to align with them rather than vice-versa, an eventuality that would lead to the further erosion of the distinction between the true and the false giving further support to populism and fascism. (shrink)

The ontological presupposition of artificial intelligence (AI) is the liberal autonomous human subject of Locke and Kant, and the ideology of AI is the automation of this particular conception of intelligence. This is demonstrated in detail in classical AI by the work of Simon, who explicitly connected his work on AI to a wider programme in cognitive science, economics, and politics to perfect capitalism. Although Dreyfus produced a powerful Heideggerian critique of classical AI, work on neural networks (...) in AI was ultimately based on the individual as the locus of intelligence. Yet this conception of AI both fails to grasp the essence of large language models, which are a statistical model of human language on the Web. The training data that enables AI is the surveillance and capture of data, where the data creates a model to approximate the entire world. However, there is a more hidden ideology inherent in AI where the goal is not to perfect a model but to control the world. As prompted by an argument between Mead and Bateson, social change is prevented by the application of cybernetics to society as a whole. The goal of AI is not just to replace human beings, but to manage humans to preserve existing power relations. As the source of intelligence in AI is distributed cognition between humans and machines, the alternative to AI is collective intelligence. As theorized by Licklider and Engelbart at the dawn of the Internet, collective intelligence explains how computers weave together both human and non-human intelligence. Rather than replace human intelligence, this produces ever more complex collective forms of intelligence. Rather than meta-stabilize a society of control, collective intelligence can go outside individualist capitalist ontology by incorporating the open world of the pluriverse, as theorized by Escobar. Collective intelligence then stands as an alternative ontological path for AI which puts intelligence at the service of humanity and the world rather than a technocratic elite. (shrink)

Artificial intelligence (AI) based clinical decision support systems (CDSS) are becoming ever more widespread in healthcare and could play an important role in diagnostic and treatment processes. For this reason, AI‐based CDSS has an impact on the doctor‐patient relationship, shaping their decisions with its suggestions. We may be on the verge of a paradigm shift, where the doctor‐patient relationship is no longer a dual relationship, but a triad. This paper analyses the role of AI‐based CDSS for shared decision‐making (...) (SDM) to better comprehend its promise and associated ethical issues. Moreover, it investigates how certain AI implementations may instead foster the inappropriate paradigm of paternalism. Understanding how AI relates to doctors and influences doctor‐patient communication is essential to promote more ethical medical practice. Both doctors’ and patients’ autonomy need to be considered in the light of AI.This article is protected by copyright. All rights reserved. (shrink)

In March 2016, DeepMind's computer programme AlphaGo surprised the world by defeating the world‐champion Go player, Lee Sedol. AlphaGo exhibits a novel, surprising and valuable style of play and has been recognised as “creative” by the artificial intelligence (AI) and Go communities. This article examines whether AlphaGo engages in creative problem solving according to the standards of comparative psychology. I argue that AlphaGo displays one important aspect of creative problem solving (namely mental scenario building in the form of (...) Monte Carlo tree search), while lacking another (domain generality). This analysis has consequences for how we think about creativity in humans and AI. (shrink)

As artificial intelligence (AI) technology advances, we increasingly delegate mental tasks to machines. However, today’s AI systems usually do these tasks with an unusual imbalance of insight and understanding: new, deeper insights are present, yet many important qualities that a human mind would have previously brought to the activity are utterly absent. Therefore, it is crucial to ask which features of minds have we replicated, which are missing, and if that matters. One core feature that humans bring to (...) tasks, when dealing with the ambiguity, emergent knowledge, and social context presented by the world, is reflection. Yet this capability is completely missing from current mainstream AI. In this paper we ask what reflective AI might look like. Then, drawing on notions of reflection in complex systems, cognitive science, and agents, we sketch an architecture for reflective AI agents, and highlight ways forward. (shrink)

Artificial intelligence research and regulation seek to balance the benefits of innovation against any potential harms and disruption. However, one unintended consequence of the recent surge in AI research is the potential re-orientation of AI technologies to facilitate criminal acts, term in this article AI-Crime. AIC is theoretically feasible thanks to published experiments in automating fraud targeted at social media users, as well as demonstrations of AI-driven manipulation of simulated markets. However, because AIC is still a relatively young (...) and inherently interdisciplinary area—spanning socio-legal studies to formal science—there is little certainty of what an AIC future might look like. This article offers the first systematic, interdisciplinary literature analysis of the foreseeable threats of AIC, providing ethicists, policy-makers, and law enforcement organisations with a synthesis of the current problems, and a possible solution space. (shrink)

The unprecedented migration of populations to urban areas has created major challenges for municipalities and service providers. To address these issues, decision-makers must embrace smart city and Society 5.0 paradigms, both of which focus on adaptability and sustainable development. Artificial intelligence (AI) plays a pivotal role by expanding service capacity, enabling automation, and processing vast data to align urban development with the UN’s Sustainable Development Goals (SDGs). This paper develops a multi-criteria analysis system designed to support decision-making in (...) complex socio-technological contexts. Using cognitive mapping and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique within a neutrosophic environment, the study analyzes cause-and-effect relationships among critical factors that influence businesses’ adaptation to AI, smart cities, and Society 5.0. The results offer a holistic decision-support framework to guide policymakers, businesses, and urban planners toward inclusive, sustainable, and technologically integrated societies. (shrink)

Artificial intelligence (AI) has become indispensable in our lives. The development of a quantitative scale for AI ethics is necessary for a better understanding of public attitudes toward AI research ethics and to advance the discussion on using AI within society. For this study, we developed an AI ethics scale based on AI-specific scenarios. We investigated public attitudes toward AI ethics in Japan and the US using online questionnaires. We designed a test set using four dilemma scenarios and (...) questionnaire items based on a theoretical framework for ethics, legal, and social issues (ELSI). We found that _country_ and _age_ are the most informative sociodemographic categories for predicting attitudes for AI ethics. Our proposed scale, which consists of 13 questions, can be reduced to only three, covering ethics, tradition, and policies. This new AI ethics scale will help to quantify how AI research is accepted in society and which area of ELSI people are most concerned with. (shrink)

Despite the tremendous promise offered by artificial intelligence (AI) for healthcare in South Africa, existing policy frameworks are inadequate for encouraging innovation in this field. Practical, concrete and solution-driven policy recommendations are needed to encourage the creation and use of AI systems. This article considers five distinct problematic issues which call for policy development: (i) outdated legislation; (ii) data and algorithmic bias; (iii) the impact on the healthcare workforce; (iv) the imposition of liability dilemma; and (v) a lack (...) of innovation and development of AI systems for healthcare in South Africa. The adoption of a national policy framework that addresses these issues directly is imperative to ensure the uptake of AI development and deployment for healthcare in a safe, responsible and regulated manner. (shrink)

It has become a truism that the ethics of artificial intelligence (AI) is necessary and must help guide technological developments. Numerous ethical guidelines have emerged from academia, industry, government and civil society in recent years. While they provide a basis for discussion on appropriate regulation of AI, it is not always clear how these ethical guidelines were developed, and by whom. Using content analysis, we surveyed a sample of the major documents (_n_ = 47) and analyzed the accessible (...) information regarding their methodology and stakeholder engagement. Surprisingly, only 38% report some form of stakeholder engagement (with 9% involving citizens) and most do not report their methodology for developing normative insights (15%). Our results show that documents with stakeholder engagement develop more comprehensive ethical guidance with greater applicability, and that the private sector is least likely to engage stakeholders. We argue that the current trend for enunciating AI ethical guidance not only poses widely discussed challenges of applicability in practice, but also of transparent development (as it rather behaves as a black box) and of active engagement of diversified, independent and trustworthy stakeholders. While most of these documents consider people and the common good as central to their telos, engagement with the general public is significantly lacking. As AI ethics moves from the initial race for enunciating general principles to more sustainable, inclusive and practical guidance, stakeholder engagement and citizen involvement will need to be embedded into the framing of ethical and societal expectations towards this technology. (shrink)

Artificial Intelligence Applications in Higher Education offers direct examples of how artificial intelligence systems can be applied in today's higher education contexts. As use of AI rapidly advances within colleges and universities worldwide, there is a pressing need to showcase the challenges, opportunities, and ethical considerations inherent in deploying these advanced computational tools. This book highlights the multifaceted roles of AI across teaching and learning, institutional administration, student data management, and beyond. Its collected case studies furnish (...) actionable insights into enhancing academic institutions and addressing diverse learning priorities such as motivation, engagement, feedback, and achievement goals. This valuable reference for researchers, designers, administrators, teaching faculty, and graduate students across various university programs offers fresh perspectives on generative AI, adaptive learning, intelligent tutoring systems, chatbots, predictive technologies, remote learning, and more. (shrink)

Increasing concerns have been raised regarding artificial intelligence (AI) bias, and in response, efforts have been made to pursue AI fairness. In this paper, we argue that the idea of structural injustice serves as a helpful framework for clarifying the ethical concerns surrounding AI bias—including the nature of its moral problem and the responsibility for addressing it—and reconceptualizing the approach to pursuing AI fairness. Using AI in healthcare as a case study, we argue that AI bias is a (...) form of structural injustice that exists when AI systems interact with other social factors to exacerbate existing social inequalities, making some groups of people more vulnerable to undeserved burdens while conferring unearned benefits to others. The goal of AI fairness, understood this way, is about pursuing a more just social structure with the development and usage of AI systems when appropriate. We further argue that all participating agents in the unjust social structure associated with AI bias bear a shared responsibility to join collective action with the goal of reforming the social structure, and we provide a list of practical recommendations for agents in various social positions to contribute to this collective action. (shrink)

Artificial intelligence (AI) national strategies provide countries with a framework for the development and implementation of AI technologies. Sixty countries worldwide published their AI national strategies. The majority of these countries with more than 70% are developed countries. The approach of AI national strategies differentiates between developed and developing countries in several aspects including scientific research, education, talent development, and ethics. This paper examined AI readiness assessment in a developing country (Palestine) to help develop and identify the main (...) pillars of the AI national strategy. AI readiness assessment was applied across education, entrepreneurship, government, and research and development sectors in Palestine (case of a developing country). In addition, it examined the legal framework and whether it is coping with trending technologies. The results revealed that Palestinians have low awareness of AI. Moreover, AI is barely used across several sectors and the legal framework is not coping with trending technologies. The results helped develop and identify the following five main pillars that Palestine’s AI national strategy should focus on: AI for Government, AI for Development, AI for Capacity Building in the private, public and technical and governmental sectors, AI and Legal Framework, and international Activities. (shrink)

Science-fiction (SF) has become a reference point in the discourse on the ethics and risks surrounding artificial intelligence (AI). Thus, AI in SF—science-fictional AI—is considered part of a larger corpus of ‘AI narratives’ that are analysed as shaping the fears and hopes of the technology. SF, however, is not a foresight or technology assessment, but tells dramas for a human audience. To make the drama work, AI is often portrayed as human-like or autonomous, regardless of the actual technological (...) limitations. Taking science-fictional AI too literally, and even applying it to science communication, paints a distorted image of the technology's current potential and distracts from the real-world implications and risks of AI. These risks are not about humanoid robots or conscious machines, but about the scoring, nudging, discrimination, exploitation, and surveillance of humans by AI technologies through governments and corporations. AI in SF, on the other hand, is a trope as part of a genre-specific mega-text that is better understood as a dramatic means and metaphor to reflect on the human condition and socio-political issues beyond technology. (shrink)

Artificial intelligence (AI) is increasingly expected to disrupt the ordinary functioning of society. From how we fight wars or govern society, to how we work and play, and from how we create to how we teach and learn, there is almost no field of human activity which is believed to be entirely immune from the impact of this emerging technology. This poses a multifaceted problem when it comes to designing and understanding regulatory responses to AI. This article aims (...) to: (i) defend the need for a novel conceptual model for understanding the systemic legal disruption caused by new technologies such as AI; (ii) to situate this model in relation to preceding debates about the interaction of regulation with new technologies (particularly the ‘cyberlaw’ and ‘robolaw’ debates); and (iii) to set out a detailed model for understanding the legal disruption precipitated by AI, examining both pathways stemming from new affordances that can give rise to a regulatory ‘disruptive moment’, as well as the Legal Development, Displacement or Destruction that can ensue. The article proposes that this model of legal disruption can be broadly generalisable to understanding the legal effects and challenges of other emerging technologies. (shrink)

This paper explores and ultimately affirms the surprising claim that artificial intelligence (AI) can become part of the person, in a robust sense, and examines three ethical and legal implications. The argument is based on a rich, legally inspired conception of persons as free and independent rightholders and objects of heightened protection, but it is construed so broadly that it should also apply to mainstream philosophical conceptions of personhood. The claim is exemplified by a specific technology, devices that (...) connect human brains with computers and operate by AI-algorithms. Under philosophically reasonable and empirically realistic conditions, these devices and the AI running them become parts of the person, in the same way as arms, hearts, or mental capacities are. This transformation shall be called empersonification. It has normative and especially legal consequences because people have broader and stronger duties regarding other persons (and parts of them) than regarding things. Three consequences with practical implications are: (i) AI-devices cease to exist as independent legal entities and come to enjoy the special legal protection of persons; (ii) therefore, third parties such as manufacturers or authors of software lose (intellectual) property rights in device and software; (iii) persons become responsible for the outputs of the empersonified AI-devices to the same degree that they are for desires or intentions arising from the depths of their unconscious. More generally, empersonification marks a new step in the long history of human–machine interaction that deserves critical ethical reflection and calls for a stronger value-aligned development of these technologies. (shrink)

Engagement with artificial intelligence (AI) can be highly beneficial for theology. This article maps the landscape of the various ways such engagement can occur. It begins by outlining the opportunities and limitations of computational theology before diving into speculative territory by imagining how robot theologians might think of divine revelation. The topic of AI and imago Dei is then reviewed, illustrating several ways AI can inform theological anthropology. The article concludes with a more speculative take on the possible (...) implications of AI for divine infinity, incarnation, theodicy, and demonic intelligence. (shrink)

The world’s current model for economic development is unsustainable. It encourages high levels of resource extraction, consumption, and waste that undermine positive environmental outcomes. Transitioning to a circular economy (CE) model of development has been proposed as a sustainable alternative. Artificial intelligence (AI) is a crucial enabler for CE. It can aid in designing robust and sustainable products, facilitate new circular business models, and support the broader infrastructures needed to scale circularity. However, to date, considerations of the ethical (...) implications of using AI to achieve a transition to CE have been limited. This article addresses this gap. It outlines how AI is and can be used to transition towards CE, analyzes the ethical risks associated with using AI for this purpose, and supports some recommendations to policymakers and industry on how to minimise these risks. (shrink)

Contemporary artificial intelligence (AI) achieves remarkable results in data processing, text generation, and the simulation of human cognition. However, it fundamentally lacks key characteristics of living systems — consciousness, autonomous motivation, and genuine understanding of the world. This article critically examines the ontological divide between simulated intelligence and lived experience, using the metaphor of the motorcycle and the horse to illustrate the blindness of technological progress. Drawing on philosophical concepts such as abduction, tacit knowledge, phenomenal consciousness, and (...) autopoiesis, the paper argues that current approaches to developing Artificial General Intelligence (AGI) may overlook deeper principles of life and mind. Methodologically, it employs a comparative ontological analysis grounded in philosophy of mind, cognitive science, systems theory, and theoretical biology, supported by contemporary literature on consciousness and biological autonomy. The article calls for a new paradigm that integrates these perspectives — one that asks not only “how to build smarter machines,” but “what intelligence and consciousness truly are.” This work aims to inspire interdisciplinary research bridging quantum physics, biology, complexity theory, and the philosophy of science, aligning with the journal Philosophies’ focus on foundational conceptual analysis. (shrink)

Several technological developments, such as self-service technologies and artificial intelligence, are disrupting the retailing industry by changing consumption and purchase habits and the overall retail experience. Although AI represents extraordinary opportunities for businesses, companies must avoid the dangers and risks associated with the adoption of such systems. Integrating perspectives from emerging research on AI, morality of machines, and norm activation, we examine how individuals morally behave toward AI agents and self-service machines. Across three studies, we demonstrate that consumers’ (...) moral concerns and behaviors differ when interacting with technologies versus humans. We show that moral intention is less likely to emerge for AI checkout and self-checkout machines compared with human checkout. In addition, moral intention decreases as people consider the machine less humanlike. We further document that the decline in morality is caused by less guilt displayed toward new technologies. The non-human nature of the interaction evokes a decreased feeling of guilt and ultimately reduces moral behavior. These findings offer insights into how technological developments influence consumer behaviors and provide guidance for businesses and retailers in understanding moral intentions related to the different types of interactions in a shopping environment. (shrink)

The ever increasing use of generative artificial intelligence raises significant questions about authorship and related issues such as credit and accountability. In this paper, I consider whether works produced by means of users inputting natural language prompts into Generative Adversarial Networks are works of authorship. I argue that they are not. This is not due to concerns about randomness or machine-assistance compromising human labor or intellectual vision, but instead due to the syntactical and compositional limitations of existing AI (...) systems in handling natural language prompts. This, I argue, gives rise to ‘authorship gaps’. (shrink)

This paper philosophically examines the impact of generative artificial intelligence on learning processes from the perspective of extended cognition. The central problem addressed is how these technologies can transform students into passive or active learners, influencing the development of cognitive skills. It will be argued that generative artificial intelligence presents risks of diminishing cognitive activity among students, as it is likely to substitute—rather than complement—the cognitive subject. It will also be argued that there are ways to (...) leverage generative artificial intelligence so that learners are not passive but rather active cognitive subjects. Three cases will be presented, with empirical support, to show how this leveraging is possible: the production of feedback, assistive technologies, and gamification. In these cases, generative artificial intelligence is a complementary cognitive artifact rather than a substitutive one. To achieve this goal, the paper presents the framework of the extended mind thesis as a conducive scenario for analyzing the relationship between generative artificial intelligence and learning contexts, and it analyzes specific cases of science education. An analysis of the types of cognitive artifacts will also be conducted to examine how generative artificial intelligence intervenes in learning in both substitute and complementary ways. (shrink)

In this paper, we delve into the significant impact of recent advancements in Artificial Intelligence (AI) on the future landscape of work. We discuss the looming possibility of mass unemployment triggered by AI and the societal repercussions of this transition. Despite the challenges this shift presents, we argue that it also unveils opportunities to mitigate social inequalities, combat global poverty, and empower individuals to follow their passions. Amidst this discussion, we also touch upon the existential question of the (...) purpose of human life in a world where machines predominantly perform work. Additionally, we explore the contentious issue of the moral status and rights of AI entities, contemplating the scenario where machines evolve to become sapient and sentient. The paper concludes with a call to action, emphasizing the necessity to acknowledge these potential outcomes and proactively seek solutions, as part of an inevitable journey through the evolution of human society shared with intelligent machines. (shrink)

Artificial intelligence (AI) refers to the replication of human intelligence by a computer system such as visual perception, speech recognition, and decision-making (i.e., in a digital format). The remote antecedents of AI go back to the 17th century and to Thomas Hobbes’ mechanics perspective of human intelligence as a combination of mathematical symbols. Much later (in 1936) the mathematician Alan Turing (pioneer of AI together with Alonzo Church and Kurt Gödel) formulated several important contributions to computational (...) theory. In 1950 he predicted that 50 years hence there would be machines capable of intelligent behavior and language skills that could interact with humans without being recognized as machines (Turing test). He was not wrong. (shrink)

Over the past ten years, we have worked in a collaboration between educators and computer scientists at the University of Illinois to imagine futures for education in the context of what is loosely called “artificial intelligence.” Unhappy with the first generation of digital learning environments, our agenda has been to design alternatives and research their implementation. Our starting point has been to ask, what is the nature of machine intelligence, and what are its limits and potentials in (...) education? This paper offers some tentative answers, first conceptually, and then practically in an overview of the results of a number of experimental implementations documented in greater detail elsewhere. Our key finding is that artificial intelligence—in the context of the practices of electronic computing developing over the past three quarters of a century—will never in any sense “take over” the role of teacher, because how it works and what it does are so profoundly different from human intelligence. However, within the limits that we describe in this paper, it offers the potential to transform education in ways that—counterintuitively perhaps—make education more human, not less. (shrink)