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)

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 ..

BackgroundArtificial intelligence (AI) has been described as the “fourth industrial revolution” with transformative and global implications, including in healthcare, public health, and global health. AI approaches hold promise for improving health systems worldwide, as well as individual and population health outcomes. While AI may have potential for advancing health equity within and between countries, we must consider the ethical implications of its deployment in order to mitigate its potential harms, particularly for the most vulnerable. This scoping review addresses the following (...) question: What ethical issues have been identified in relation to AI in the field of health, including from a global health perspective?MethodsEight electronic databases were searched for peer reviewed and grey literature published before April 2018 using the concepts of health, ethics, and AI, and their related terms. Records were independently screened by two reviewers and were included if they reported on AI in relation to health and ethics and were written in the English language. Data was charted on a piloted data charting form, and a descriptive and thematic analysis was performed.ResultsUpon reviewing 12,722 articles, 103 met the predetermined inclusion criteria. The literature was primarily focused on the ethics of AI in health care, particularly on carer robots, diagnostics, and precision medicine, but was largely silent on ethics of AI in public and population health. The literature highlighted a number of common ethical concerns related to privacy, trust, accountability and responsibility, and bias. Largely missing from the literature was the ethics of AI in global health, particularly in the context of low- and middle-income countries (LMICs).ConclusionsThe ethical issues surrounding AI in the field of health are both vast and complex. While AI holds the potential to improve health and health systems, our analysis suggests that its introduction should be approached with cautious optimism. The dearth of literature on the ethics of AI within LMICs, as well as in public health, also points to a critical need for further research into the ethical implications of AI within both global and public health, to ensure that its development and implementation is ethical for everyone, everywhere. (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 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 (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)

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)

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)

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)

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)

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)

Mandevillian intelligence is a specific form of collective intelligence in which individual cognitive shortcomings, limitations, and biases play a positive functional role in yielding various forms of collective cognitive success. When this idea is transposed to the epistemological domain, mandevillian intelligence emerges as the idea that individual forms of intellectual vice may, on occasion, support the epistemic performance of some form of multi-agent ensemble, such as a socio-epistemic system, a collective doxastic agent, or an epistemic group agent. As a specific (...) form of collective intelligence, mandevillian intelligence is relevant to a number of debates in social epistemology, especially those that seek to understand how group (or collective) knowledge arises from the interactions between a collection of individual epistemic agents. (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 (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)

Artificial intelligence research has foundered on the issue of representation. When intelligence is approached in an incremental manner, with strict reliance on interfacing to the real world through perception and action, reliance on representation disappears. In this paper we outline our approach to incrementally building complete intelligent Creatures. The fundamental decomposition of the intelligent system is not into independent information processing units which must interface with each other via representations. Instead, the intelligent system is decomposed into independent and parallel activity (...) producers which all interface directly to the world through perception and action, rather than interface to each other particularly much. The notions of central and peripheral systems evaporateeverything is both central and peripheral. Based on these principles we have built a very successful series of mobile robots which operate without supervision as Creatures in standard office environments. (shrink)

Mandevillian intelligence is a specific form of collective intelligence in which individual cognitive vices are seen to play a positive functional role in yielding collective forms of cognitive success. The present paper introduces the concept of mandevillian intelligence and reviews a number of strands of empirical research that help to shed light on the phenomenon. The paper also attempts to highlight the value of the concept of mandevillian intelligence from a philosophical, scientific and engineering perspective. Inasmuch as we accept the (...) notion of mandevillian intelligence, then it seems that the cognitive and epistemic value of a specific social or technological intervention will vary according to whether our attention is focused at the individual or collective level of analysis. This has a number of important implications for how we think about the design and evaluation of collective cognitive systems. For example, the notion of mandevillian intelligence forces us to take seriously the idea that the exploitation of individual cognitive shortcomings could, in some situations, provide a productive route to collective forms of cognitive and epistemic success. (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)

Ambient intelligence systems are designed to sense, process, adapt to, and act on information in their environment: motion, light, sound, and other data. In their target article, "A Justice First Approach to Ambient Intelligence in Healthcare", Herington and Cho argue that the ethical implementation of ambient intelligence systems cannot rely on autonomy-focused, individualized clinical or research ethics frameworks. Because ambient intelligence surveillance can passively record and report information beyond its most narrow intended purpose, ambient intelligence systems implicate various groups of (...) people (with potentially conflicting interests). To address these conditions, Herington and Cho propose a justice-first approach grounded in principles of fairness, transparent governance, and accounting for historical, societal, and structural injustice. We agree that the application of traditional bioethics approaches would be inadequate and that this situation requires thinking beyond the clinician-patient or researcher-participant dyad. We extend their argument in two ways: first, by noting that these challenges are not unique to ambient intelligence systems but arise in many health system advances; and second, by operationalizing their call for systematic research to evaluate ethicality before widespread deployment. (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)

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)

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)

We have entered an era when AI technologies are transforming the public time of social coexistence. So far, the general public has demonstrated two opposing attitudes towards AI. Let us call the first attitude, which uncritically believes the promises of big companies that AI will improve human life, technophilia, and the opposite attitude, which denies any involvement with AI in fear of the enslavement of humans to the machine, technophobia. It is time to open up an intermediate path in our (...) research, which we could call technoscepticism, starting from the philosophical position of digital humanism. Digital Humanism means that at the beginning and the end of every digital system, there is the human subject, as creator, user, and signifier. Another philosophical axiom is implied, that technology is not neutral, since it entails human subjects and realizes human ethics. Technology is only understood as neutral if it is not understood as technology - but as nature. Our intelligence is natural. And, although its manifestations are not neutral, it is in itself the primary condition for any judgment and therefore precedes its manifestations as a precondition. But AI is by definition technical. So, in the first step, AI is not intelligence, if we consider intelligence to be natural. In the era of AI, the problem is the intensity and extension of this dominant principle in all fields of social life. The democratic problem is to overthrow the regime of mythinformation and make available to the people the data necessary for making informed political decisions. As always, the ultimate question is political: Who controls the providers of AI? In this book, we follow the thread of AI's metaphysical claims, crossing the veil of dystopian and utopian exaggerations, to reveal the core of the novel philosophical and political problems brought about by the digital ontological revolution. In Chapter One, we examine technoscepticism as a socio-historical critique of technology. In Chapter Two, we investigate the metaphysical foundations of AI and AGI as a scientific program. In Chapter Three, we challenge the ideology of AI as a corporate capitalist venture. In Chapter Four, we envision possible ways of overcoming digital barbarism toward a future of democratic digital humanism. (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)

This paper critically assesses the possibility of moral enhancement with ambient intelligence technologies and artificial intelligence presented in Savulescu and Maslen (2015). The main problem with their proposal is that it is not robust enough to play a normative role in users’ behavior. A more promising approach, and the one presented in the paper, relies on an artifi-cial moral reasoning engine, which is designed to present its users with moral arguments grounded in first-order normative theories, such as Kantianism or utilitarianism, (...) that reason-responsive people can be persuaded by. This proposal can play a normative role and it is also a more promising avenue towards moral enhancement. It is more promising because such a system can be designed to take advantage of the sometimes undue trust that people put in automated technologies. We could therefore expect a well-designed moral reasoner system to be able to persuade people that may not be persuaded by similar arguments from other people. So, all things considered, there is hope in artificial intelli-gence for moral enhancement, but not in artificial intelligence that relies solely on ambient intelligence technologies. (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)

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)

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)

This research investigates the efficacy of business ethics intervention, tests a theoretical model that the love of money is directly or indirectly related to propensity to engage in unethical behavior (PUB), and treats college major (business vs. psychology) and gender (male vs. female) as moderators in multi-group analyses. Results suggested that business students who received business ethics intervention significantly changed their conceptions of unethical behavior and reduced their propensity to engage in theft; while psychology students without intervention had no such (...) changes. Therefore, ethics training had some impacts on business students' learning and education (intelligence). For our theoretical model, results of the whole sample (N = 298) revealed that Machiavellianism (measured at Time 1) was a mediator of the relationship between the love of money (measured at Time 1) and unethical behavior (measured at Time 2) (the Love of Money → Machiavellianism → Unethical Behavior). Further, this mediating effect existed for business students (n = 198) but not for psychology students (n = 100), for male students (n = 165) but not for female students (n = 133), and for male business students (n = 128) but not for female business students (n = 70). Moreover, when examined alone, the direct effect (the Love of Money → Unethical Behavior) existed for business students but not for psychology students. We concluded that a short business ethics intervention may have no impact on the issue of virtue (wisdom). (shrink)

Intelligence is one of the most influential yet contested constructs in psychology. Psychometric research, beginning with early intelligence testing and the proposal of a general factor of intelligence, established influential models that continue to shape scientific and applied work. Yet competing theories, challenging the focus on abstract reasoning, highlight ongoing disputes about the nature of intelligence and the assessment of intellectual differences among individuals. Central debates concern the relationship between measurement and theory and the ontological status of the g factor (...) - particularly whether intelligence is reducible to neurocognitive processes or must be treated as an emergent phenomenon. Contemporary research is marked by ontological and epistemological pluralism, reflecting divergent explanatory aims and practical needs. This pluralism also carries normative implications, as different conceptions of intelligence guide educational practices, inform policy, and shape broader societal understandings of human cognitive abilities. (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)

There is considerable enthusiasm about the prospect that artificial intelligence (AI) will help to improve the safety and efficacy of health services and the efficiency of health systems. To realize this potential, however, AI systems will have to overcome structural problems in the culture and practice of medicine and the organization of health systems that impact the data from which AI models are built, the environments into which they will be deployed, and the practices and incentives that structure their development. (...) This perspective elaborates on some of these structural challenges and provides recommendations to address potential shortcomings. (shrink)

On the plausible assumption that skillful behavior is a visible manifestation of intelligence, a theory of intelligence—whether human or not—should be informed by a theory of skills. More controversial is the question as to whether, in order to theorize about intelligence, we should study certain skills in particular. My target is the view that only a particular class/kind of skill (i.e., ‘theoretical’, or ‘intellectual’ skills, versus ‘practical’, or ‘embodied’ skills) manifests intelligence, or especially does so. I call this view ‘Intelligence (...) Elitism’. The primary goal of this article is to make explicit, for the first time in the philosophical literature, the controversy between elitism and socialism, to sharpen it, and to highlight its significance. At the same time, I also aim at laying the foundations for undermining the hegemony of elitism by mounting an inductive argument on behalf of socialism. (shrink)