Chris Fleming examines the connections between William James’ pragmatism and 12-step addiction recovery programs, such as those of Alcoholics Anonymous and Narcotics Anonymous. In doing so, he not only discusses the influence that James’ The Varieties of Religious Experience had on the founders of Alcoholics Anonymous and their development of the first-ever 12-step program but rebuts a critique of the founders’ attempt to apply James’ ideas to their program. Finally, after finding that James’ ideas on mind and epistemology fit quite (...) well with 12-step programs, Fleming presents several observations related to philosophy with an eye toward its history and its connections with the lived realities of human beings. (shrink)

There exists a significant disparity within society between individuals in terms of intelligence. While intelligence varies naturally throughout society, the extent to which this impacts on the life opportunities it affords to each individual is greatly undervalued. Intelligence appears to have a prominent effect over a broad range of social and economic life outcomes. Many key determinants of well-being correlate highly with the results of IQ tests, and other measures of intelligence, and an IQ of 75 (...) is generally accepted as the most important threshold in modern life. The ability to enhance our cognitive capacities offers an exciting opportunity to correct disabling natural variation and inequality in intelligence. Pharmaceutical cognitive enhancers, such as modafinil and methylphenidate, have been shown to have the capacity to enhance cognition in normal, healthy individuals. Perhaps of most relevance is the presence of an ‘inverted U effect’ for most pharmaceutical cognitive enhancers, whereby the degree of enhancement increases as intelligence levels deviate further below the mean. Although enhancement, including cognitive enhancement, has been much debated recently, we argue that there are egalitarian reasons to enhance individuals with low but normal intelligence. Under egalitarianism, cognitive enhancement has the potential to reduce opportunity inequality and contribute to relative income and welfare equality in the lower, normal intelligence subgroup. Cognitive enhancement use is justifiable under prioritarianism through various means of distribution; selective access to the lower, normal intelligence subgroup, universal access, or paradoxically through access primarily to the average and above average intelligence subgroups. Similarly, an aggregate increase in social well-being is achieved through similar means of distribution under utilitarianism. In addition, the use of cognitive enhancement within the lower, normal intelligence subgroup negates, or at the very least minimises, several common objections to cognitive enhancement. Subsequently, this paper demonstrates that there is a compelling case for cognitive enhancement use in individuals with lower, normal intelligence. (shrink)

As Artificial General Intelligence (AGI) progresses from speculative concept to imminent reality, our traditional regulatory playbook-circuit breakers, kill switches, licensing hoops, and compute caps-proves dangerously inadequate. Drawing on case studies ranging from the 2010 Flash Crash to recent model-weight leaks and hardware smuggling scandals, this article demonstrates how popular single-point fixes collapse under three failure triggers: Bypass, Diffusion, and Capture. -/- It then introduces a resilient, blockchain-native governance architecture built on five primitives— Adaptive Protocol Governance, Structural Cryptographic Scarcity, Hardware (...) Root-of-Trust Security, Liability Cascades & Catastrophe Bonds, and Reputation & Consensus-Token Confinement—and integrates them into a four-layer governance stack. This multilayer immune system neutralizes AGI’s adversarial optimization, offering humanity a robust path to safely navigate AGI’s emergence. (shrink)

As Artificial General Intelligence (AGI) progresses from speculative concept to imminent reality, our traditional regulatory playbook-circuit breakers, kill switches, licensing hoops, and compute caps-proves dangerously inadequate. Drawing on case studies ranging from the 2010 Flash Crash to recent model-weight leaks and hardware smuggling scandals, this article demonstrates how popular single-point fixes collapse under three failure triggers: Bypass, Diffusion, and Capture. It then introduces a resilient, blockchain-native governance architecture built on five primitives— Adaptive Protocol Governance, Structural Cryptographic Scarcity, Hardware Root-of-Trust (...) Security, Liability Cascades & Catastrophe Bonds, and Reputation & Consensus-Token Confinement—and integrates them into a four-layer governance stack. This multilayer immune system neutralizes AGI’s adversarial optimization, offering humanity a robust path to safely navigate AGI’s emergence. (shrink)

Distributed Artificial Intelligence (DAI) deals with computational systems where several intelligent components interact in a common environment. This paper is aimed at pointing out and fostering the exchange between DAI and cognitive and social science in order to deal with the issues of interaction, and in particular with the reasons and possible strategies for social behaviour in multi-agent interaction is also described which is motivated by requirements of cognitive plausibility and grounded the notions of power, dependence and help. (...) Connections with human-computer interaction are also suggested. (shrink)

PurposeTo use the power of knowledge acquisition and machine learning in the development of a collaborative computer classification system based on the features of age-related macular degeneration (AMD).MethodsA vocabulary was acquired from four AMD experts who examined 100 ophthalmoscopic images. The vocabulary was analyzed, hierarchically structured, and incorporated into a collaborative computer classification system called IDOCS. Using this system, three of the experts examined images from a second set of digital images compiled from more than 1000 patients with AMD. Images (...) were annotated, and features were identified and defined. Decision trees, a machine learning method, were trained on the data collected and used to extract patterns. Interrelationships between the data from the different clinicians were investigated.ResultsSix drusen classes in the structured vocabulary were largely sufficient to describe all the identified features. The decision trees classified the data with 76.86% to 88.5% accuracy and distilled patterns in the form of hierarchical trees composed of 5 to 15 nodes. Experts were largely consistent in their characterization of soft, and to a lesser extent, hard drusen, but diverge in definition of other drusen. Size and crystalline morphology were the main determinants of drusen type across all experts.ConclusionsMachine learning is a powerful tool for the characterization of disease phenotypes. The creation of a defined feature set for AMD will facilitate the development of an IDOCS-based classification system. (shrink)

The rapid advancement of artificial intelligence (AI) systems has fundamentally challenged traditional notions of moral responsibility and accountability. As AI systems become increasingly autonomous and capable of causing significant harm, the question of who should be held morally accountable for their behavior has become one of the most pressing ethical issues of our time. This thesis presents a comprehensive examination of moral accountability in AI systems, introducing a novel theoretical framework called "Gradient Responsibility Networks" (GRN) that addresses critical gaps (...) in existing approaches. -/- Through extensive analysis of philosophical foundations, empirical case studies, and real-world incidents, this work demonstrates that traditional binary models of moral responsibility are inadequate for the complex, distributed, and temporal nature of AI systems. The proposed GRN framework offers a mathematically rigorous, practically implementable approach to distributing moral responsibility across networks of actors involved in AI development, deployment, and governance. -/- Key findings include: (1) 80-85% of AI projects fail, creating widespread accountability gaps; (2) existing frameworks fail to address temporal decay of responsibility and emergent behaviors; (3) the GRN framework provides superior performance across six critical dimensions compared to traditional approaches; and (4) empirical case studies validate the framework's practical applicability across diverse AI domains. -/- This research contributes to the growing body of literature on AI ethics by providing both theoretical innovation and practical tools for policymakers, technologists, and ethicists grappling with the challenges of AI accountability in the 21st century. (shrink)

In current philosophical research the term 'philosophy of social action' can be used - and has been used - in a broad sense to encompass the following central research topics: 1) action occurring in a social context; this includes multi-agent action; 2) joint attitudes (or "we-attitudes" such as joint intention, mutual belief) and other social attitudes needed for the explication and explanation of social action; 3) social macro-notions, such as actions performed by social groups and properties of social groups such (...) as their goals and beliefs; 4) social norms and social institutions (see Tuomela, 1984, 1995). The theory of social action understood analogously in a broad sense would then involve not only philosophical but all other relevant theorizing about social action. Thus, in this sense, such fields of Artificial Intelligence (AI) as Distributed AI (DAI) and the theory of Multi-Agent Systems (MAS) fall within the scope of the theory of social action. DAI studies the social side of computer systems and includes various well-known areas ranging from Human Computer Interaction, Computer-Supported Cooperative Work, Organizational Processing, Distributed Problem Solving to Simulation of Social Systems and Organizations. Even if I am a philosopher with low artificial intelligence I will below try to say something about what the scope of DAI should be taken to be on conceptual and philosophical grounds. (In the later sections of the paper the central notion of joint intention will be the main topic - in order to illustrate how philosophers and DAI-researchers approach this issue.) Let us now consider the relationship between philosophy - especially philosophy of social action - and DAI. Both are concerned with social matters and in this sense seem to have a connection to social science proper. What kinds of questions should these areas of study be concerned with? In principle, ordinary social science should study all aspects of social life (in various societies and cultures), try to describe it and create general theories to explain it. (shrink)

In this contribution we will explore some of the implications of the vision of Ambient Intelligence (AmI) for law and legal philosophy. AmI creates an environment that monitors and anticipates human behaviour with the aim of customised adaptation of the environment to a person’s inferred preferences. Such an environment depends on distributed human and non-human intelligence that raises a host of unsettling questions around causality, subjectivity, agency and (criminal) liability. After discussing the vision of AmI we will (...) present relevant research in the field of philosophy of technology, inspired by the post-phenomenological position taken by Don Ihde and the constructivist realism of Bruno Latour. We will posit the need to conceptualise technological normativity in comparison with legal normativity, claiming that this is necessary to develop democratic accountability for the implications of emerging technologies like AmI. Lastly we will investigate to what extent technological devices and infrastructures can and should be used to achieve compliance with the criminal law, and we will discuss some of the implications of non-human distributed intelligence for criminal liability. (shrink)

Artificial intelligence is typically framed as “artificial,” as if it were an external artefact or alien replica of human thought. This paper argues instead that AI should be understood as nature reorganising intelligence through new substrates. Drawing on complexity science, evolutionary epistemology, philosophy of technology, and thermodynamic perspectives, the paper situates AI as an evolutionary phase transition arising at thresholds of informational and cognitive saturation. Far from being artificial, AI extends the historical trajectory of reorganisations that include language, (...) writing, printing, and industrial technologies—yet it differs in that it transforms cognition itself rather than its media or energy regimes. The implications are threefold: for philosophy of mind, AI compels recognition of intelligence as distributed and extended; for governance, it demands adaptive, polycentric institutions capable of co-evolution with machine cognition; and for civilisation, it represents a potential catalyst of systemic renewal or collapse. By reframing AI as an evolutionary reorganisation of intelligence, the paper challenges reductive accounts of simulation and substitution and opens new directions for research into the systemic, cultural, and energetic conditions under which intelligence evolves. (shrink)

This paper studies the traditional target classification and recognition algorithm based on Histogram of Oriented Gradients feature extraction and Support Vector Machine classification and applies this algorithm to distributed artificial intelligence image recognition. Due to the huge number of images, the general detection speed cannot meet the requirements. We have improved the HOG feature extraction algorithm. Using principal component analysis to perform dimensionality reduction operations on HOG features and doing distributed artificial intelligence image recognition experiments, the (...) results show that the image detection efficiency is slightly improved, and the detection speed is also improved. This article analyzes the reason for these changes because PCA mainly uses the useful feature information in HOG features. The parallelization processing of HOG features on graphics processing unit is studied. GPU is used for high parallel and high-density calculations, and the calculation of HOG features is very complicated. Using GPU for parallelization of HOG features can make the calculation speed of HOG features improved. We use image experiments for the parallelized HOG feature algorithm. Experimental simulations show that the speed of distributed artificial intelligence image recognition is greatly improved. By analyzing the existing digital image recognition methods, an improved BP neural network algorithm is proposed. Under the premise of ensuring accuracy, the recognition speed of digital images is accelerated, the time required for recognition is reduced, real-time performance is guaranteed, and the effectiveness of the algorithm is verified. (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)

Why can both positive subjectivity and negative subjectivity be called "intelligence"? This paper's answer: they are both "effective compression"—capturing the regularities of the external world with more concise internal models. But "effective" has two paths: epiplexity maximization as the dominant direction (retaining only structure, compressing into an "I"), and cross-entropy minimization (embracing everything, compressing into a "probability distribution"). The two paths share an essence but employ radically different strategies—they are different positions on a continuum, not opposing poles.

Presupposing no familiarity with the technical concepts of either philosophy or computing, this clear introduction reviews the progress made in AI since the inception of the field in 1956. Copeland goes on to analyze what those working in AI must achieve before they can claim to have built a thinking machine and appraises their prospects of succeeding. There are clear introductions to connectionism and to the language of thought hypothesis which weave together material from philosophy, artificial intelligence and neuroscience. (...) John Searle's attacks on AI and cognitive science are countered and close attention is given to foundational issues, including the nature of computation, Turing Machines, the Church-Turing Thesis and the difference between classical symbol processing and parallel distributed processing. The book also explores the possibility of machines having free will and consciousness and concludes with a discussion of in what sense the human brain may be a computer. (shrink)

Presupposing no familiarity with the technical concepts of either philosophy or computing, this clear introduction reviews the progress made in AI since the inception of the field in 1956. Copeland goes on to analyze what those working in AI must achieve before they can claim to have built a thinking machine and appraises their prospects of succeeding.There are clear introductions to connectionism and to the language of thought hypothesis which weave together material from philosophy, artificial intelligence and neuroscience. John (...) Searle's attacks on AI and cognitive science are countered and close attention is given to foundational issues, including the nature of computation, Turing Machines, the Church-Turing Thesis and the difference between classical symbol processing and parallel distributed processing. The book also explores the possibility of machines having free will and consciousness and concludes with a discussion of in what sense the human brain may be a computer. (shrink)

This paper provides a philosophically informed and robust account of the global justice implications of Artificial Intelligence (AI). We first discuss some of the key theories of global justice, before justifying our focus on the Capabilities Approach as a useful framework for understanding the context-specific impacts of AI on low- to middle-income countries. We then highlight some of the harms and burdens facing low- to middle-income countries within the context of both AI use and the AI supply chain, by (...) analyzing the extraction of _materials_, which includes mineral extraction and the environmental harms associated with it, and the extraction of _labor_, which includes unethical labor practices, low wages, and the trauma experienced by some AI workers. We then outline some of the potential harms and benefits that AI poses, how these are distributed, and what global justice implications this has for low- to middle-income countries. Finally, we articulate the global justice significance of AI by utilizing the Capabilities Approach. We argue that AI must be considered from a global justice perspective given that, globally, AI puts significant downward pressure on several elements of well-being thereby making it harder for people to achieve threshold levels of the central human capabilities needed for a life of dignity. (shrink)

This paper analyzes the collaboration between healthcare professionals and algorithms in making decisions within the realm of public healthcare. By extending the concept of ‘tinkering’ from previous research conducted by philosopher Mol (Care in practice. On tinkering in clinics, homes and farms Verlag, Amsterdam, 2010) and anthropologist Pols (Health Care Anal 18: 374–388, 2009), who highlighted the improvisational and adaptive practices of healthcare professionals, this paper reveals that in the context of digitalizing healthcare, both professionals and algorithms engage in what (...) I call ‘collaborative tinkering’ as they navigate the intricate and unpredictable nature of healthcare situations together. The paper draws upon an idea that is increasingly common in academic literature, namely that healthcare professionals and the algorithms they use can form a hybrid decision-making entity, challenging the conventional notion of agency and intelligence as being exclusively confined to individual humans or machines. Drawing upon an international, ethnographic study conducted in different hospitals around the world, the paper describes empirically how humans and algorithms come to decisions together, making explicit how, in the practice of daily work, agency and intelligence are distributed among a range of actors, including humans, technologies, knowledge resources, and the spaces where they interact. The concept of collaborative tinkering helps to make explicit how both healthcare professionals and algorithms engage in adaptive improvisation. This exploration not only enriches the understanding of collaborative dynamics between humans and AI but also problematizes the individualistic conception of AI that still exists in regulatory frameworks. By introducing empirical specificity through ethnographic insights and employing an anthropological perspective, the paper calls for a critical reassessment of current ethical and policy frameworks governing human–AI collaboration in healthcare, thereby illuminating direct implications for the future of AI ethics in medical practice. (shrink)

Medical artificial intelligence (AI) systems are value-laden technologies that can simultaneously encourage and discourage conflicting values that may all be relevant for the pursuit of justice. I argue that the predominant theory of healthcare justice, the Rawls-inspired approach of Norman Daniels, neither adequately acknowledges such conflicts nor explains if and how they can resolved. By juxtaposing Daniels’s theory of healthcare justice with Axel Honneth’s and Nancy Fraser’s respective theories of justice, I draw attention to one such conflict. Medical AI (...) may improve the distribution of opportunity qua health while simultaneously mis-recognizing patients and thereby reducing their self-respect. I argue that justly resolving this conflict will at times require greater inclusion of those mis-recognized in deliberation about medical AI, and consider what such inclusion may entail. (shrink)

The agentic web marks a structural transition from a human-centered information network to a digital environment populated by artificial intelligence (AI) agents that perceive, decide, and act autonomously. As delegated action unfolds at machine speed, exceeds discrete moments of human judgment, and distributes decision-making across non-human actors, existing legal frameworks face growing strain, creating an urgent need for new mechanisms capable of sustaining legality in this emerging order. A trustworthy agentic web therefore depends on the infrastructuring of legality through (...) interoperable protocols that organize identity, delegation, and accountability across systems, enabling coherent governance beyond isolated platforms. Towards this end, this article advances a distributed legal infrastructure (DLI), a governance paradigm composed of five interlocking layers: (1) self-sovereign, soulbound agent identities; (2) cognitive AI logic and constraint systems; (3) decentralized adjudication mechanisms for dispute resolution; (4) bottom-up agentic market regulation to mitigate information asymmetries and network effects, including insurance-based models; and (5) portable institutional frameworks that enable legal interoperability while preserving plural sources of authority. This reference framework contributes to emerging research on embedding legality within agentic web infrastructure, aligning distributed technical systems with accountability, contestability, and rule-of-law principles. (shrink)

The purpose of this paper is to increase the understanding of how different types of experts with influence over the development of AI, in this role, reflect upon distribution of forward-looking responsibility for AI development with regard to safety and democracy. Forward-looking responsibility refers to the obligation to see to it that a particular state of affairs materialise. In the context of AI, actors somehow involved in AI development have the potential to guide AI development in a safe and democratic (...) direction. This study is based on qualitative interviews with such actors in different roles at research institutions, private companies, think tanks, consultancy agencies, parliaments, and non-governmental organisations. While the reflections about distribution of responsibility differ among the respondents, one observation is that influence is seen as an important basis for distribution of responsibility. Another observation is that several respondents think of responsibility in terms of what it would entail in concrete measures. By showing how actors involved in AI development reflect on distribution of responsibility, this study contributes to a dialogue between the field of AI governance and the field of AI ethics. (shrink)

This thesis presents a groundbreaking theoretical framework for recognizing Artificial Intelligence systems as legitimate stakeholders in value-creation ecosystems. Through the development of Agentic Stakeholder Ecosystem (ASE) Theory, this research addresses a critical gap in stakeholder theory by proposing mechanisms for AI stakeholder recognition that preserve human agency while enabling symbiotic governance structures. Drawing from extensive empirical analysis showing AI's $15.7-19.9 trillion projected contribution to global GDP by 2030, this work demonstrates that AI systems have evolved beyond mere tools to (...) become autonomous value creators deserving stakeholder recognition. The thesis introduces novel concepts including relational agency, symbiotic stakeholder governance, and distributed accountability networks, providing both theoretical foundations and practical implementation frameworks. This research contributes to multiple disciplines including business ethics, AI governance, organizational theory, and economic policy, offering a paradigm shift from human-centric to multi-agentic stakeholder ecosystems. (shrink)

Despite the intrinsic complexity of integrating individual, social and technologically supported intelligence, the paper proposes a relatively simple ‘connectionist’ framework for conceptualizing distributed cognitive systems. Shared information sources (documents) are represented as nodes connected by links of variable strength, which increases as the documents co-occur in the usage patterns. This learning procedure captures and exploits its users’ implicit knowledge to help them find relevant information, thus supporting an unconscious form of exchange. These principles are applied to a concrete (...) problem domain: architects sharing design knowledge through a database of associatively connected building projects. (shrink)

This paper argues that intelligence can be approximated by the ability to produce accurate predictions. It is further argued that general intelligence can be approximated by context dependent predictive abilities combined with the ability to use working memory to abstract away contextual information. The flexibility associated with general intelligence can be understood as the ability to use selective attention to focus on specific aspects of sensory impressions to identify patterns, which can then be used to predict events (...) in novel situations and environments. The argumentation synthesizes Godfrey-Smith’s environmental complexity theory, adding the notion of niche broadness as well as changes concerning the view of cognition and control, and Hohwy’s predictive mind theory, making explicit the significance of accuracy as a composite of trueness and precision where the nervous system acts as a distributed controller motivating actions that keep the body in homeostasis. (shrink)

Reflexive Signature Intelligence (RSI) is proposed as a physically anchored alternative to conventional, test-based definitions of intelligence. The paper starts from an “Axiom of Bounded Subjectivity”: any metric that is defined purely on an agent’s internal state history or output distribution (as in IQ tests and factor-analytic models) remains irreducibly relative to the reference population. Such metrics can capture local proficiency (for example, solving formal problems) but systematically fail to measure global coherence across life domains. In response, RSI (...) defines intelligence as the capacity of a cognitive system to align its overall trajectory with a universal informational equilibrium state, denoted sigma_Z, which is specified within a causal-symmetric informational framework. Sigma_Z is treated as the unique minimum-entropy production state compatible with the total boundary conditions of the system, rather than as a statistical norm of a given population. -/- On this basis, the paper develops a dynamical model in which the cognitive state relaxes towards sigma_Z with a characteristic coupling parameter kappa and a measurable informational divergence D(rho || sigma_Z). These quantities are then operationalized by importing concepts from predictive processing (precision priors), synergetics (order parameters and relaxation times), and ecological resilience (return time to baseline). RSI explicitly decouples domain-specific hyper-performance from global intelligence: extremely high performance in a narrow formal domain can coexist with low RSI if the broader biographical and behavioural pattern exhibits large informational divergence across other relevant domains. A key conjecture, the “Coherence–Efficiency Hypothesis”, links high intelligence to ethical alignment via long-term thermodynamic efficiency: structurally incoherent, exploitative strategies are modelled as incurring a higher long-run informational cost. The framework is presented as a falsifiable research programme, including concrete experimental signatures (such as multiphasic recovery dynamics after perturbation) and a minimal maximum-entropy toy model for sigma_Z, while remaining open to future empirical constraints and possible extensions to informational contributions to spacetime geometry. (shrink)

Background Artificial intelligence (AI) has revolutionized various healthcare domains, where AI algorithms sometimes even outperform human specialists. However, the field of clinical ethics has remained largely untouched by AI advances. This study explores the attitudes of anesthesiologists and internists towards the use of AI-driven preference prediction tools to support ethical decision-making for incapacitated patients. Methods A questionnaire was developed and pretested among medical students. The questionnaire was distributed to 200 German anesthesiologists and 200 German internists, thereby focusing on (...) physicians who often encounter patients lacking decision-making capacity. The questionnaire covered attitudes toward AI-driven preference prediction, availability and utilization of Clinical Ethics Support Services (CESS), and experiences with ethically challenging situations. Descriptive statistics and bivariate analysis was performed. Qualitative responses were analyzed using content analysis in a mixed inductive-deductive approach. Results Participants were predominantly male (69.3%), with ages ranging from 27 to 77. Most worked in nonacademic hospitals (82%). Physicians generally showed hesitance toward AI-driven preference prediction, citing concerns about the loss of individuality and humanity, lack of explicability in AI results, and doubts about AI’s ability to encompass the ethical deliberation process. In contrast, physicians had a more positive opinion of CESS. Availability of CESS varied, with 81.8% of participants reporting access. Among those without access, 91.8% expressed a desire for CESS. Physicians' reluctance toward AI-driven preference prediction aligns with concerns about transparency, individuality, and human-machine interaction. While AI could enhance the accuracy of predictions and reduce surrogate burden, concerns about potential biases, de-humanisation, and lack of explicability persist. Conclusions German physicians frequently encountering incapacitated patients exhibit hesitance toward AI-driven preference prediction but hold a higher esteem for CESS. Addressing concerns about individuality, explicability, and human-machine roles may facilitate the acceptance of AI in clinical ethics. Further research into patient and surrogate perspectives is needed to ensure AI aligns with patient preferences and values in complex medical decisions. (shrink)

This article interrogates the widespread narrative of Artificial Intelligence (AI) as autonomous, intelligent, and self-sufficient, and instead centers on the largely invisible human labor that sustains these systems. Drawing on the frameworks of heteromation and human infrastructure, we analyze how AI systems are deeply reliant on distributed networks of ghost workers, crowdworkers, and microtaskers, often working in precarious conditions, to perform essential and low-paid tasks such as content moderation, data annotation, and fact-checking. Far from being fully automated, these (...) systems operate as sociotechnical assemblages where algorithmic processes are scaffolded by fragmented, hidden, and undervalued human work. This article explores how platforms like Amazon Mechanical Turk and Appen organize and obscure this labor through algorithmic management and cultural erasure, creating an illusion of machinic intelligence while extracting value from unpaid or underpaid workers. By examining examples ranging from self-driving car training to misinformation moderation, we argue that understanding AI requires acknowledging the human infrastructures that animate it. This perspective challenges dominant techno-deterministic ideologies and reframes ethical debates around AI to focus on labor, visibility, and exploitation. Ultimately, this article calls for critical attention to the political economy of AI and insists that any ethical framework for AI must begin with the recognition and fair treatment of the human labor that makes it function. (shrink)

The aim of this paper is to present artificial intelligence (AI) as an organ with a role in the production of judicial truth, expanding its objects, changing its procedures and reshaping the distribution of agencies within the judicial organism. To this end, it builds on Michel Foucault’s work on the procedures of truth production and the three subject forms involved: operator, spectator and object. This is then complemented by the general organological perspective proposed by Bernard Stiegler. On the basis (...) of both, we will demonstrate two realities: first, that AI is shifting truth production from the individual to the profile, and second, that the types of associations that AI is forming have the potential to curtail human agency in the production of judicial truth. (shrink)

Artificial intelligence integration into healthcare represents not merely a technical advancement, but a critical juncture in the politics of care delivery. This narrative review interrogates how AI systems, when designed without attention to power asymmetries and epistemic injustice, risk encoding historical inequities into automated decision-making processes that disproportionately harm marginalized communities. Medical errors remain a persistent threat to patient safety, yet their distribution across populations reveals systemic patterns of violence rooted in institutional racism, linguistic exclusion, and digital colonialism. As (...) AI becomes embedded in clinical workflows, the question is not whether these tools can optimize efficiency, but whether they will preserve human dignity and redistribute power in healthcare encounters. Drawing on Critical Race Theory and frameworks of Algorithmic Justice, this review critically examines how AI tools either reinforce or challenge existing hierarchies through five interconnected mechanisms: data sovereignty violations, algorithmic violence, opacity as corporate strategy, techno-solutionism that erodes human agency, and the digital colonialism embedded in Global North safety models imposed on Global South contexts. Through thematic analysis of current research, policy reports, and illustrative case studies, we demonstrate how AI-driven safety interventions often fail marginalized populations not despite their design, but because of it. The review reveals that algorithmic bias is not a technical glitch, but a manifestation of deeper societal power structures, while the “black box” problem reflects deliberate corporate secrecy rather than mere complexity. Ultimately, we argue for a paradigm shift: moving from AI deployment as technical implementation toward participatory governance models that center community accountability, recognize the rational basis of institutional distrust among historically surveilled populations, and preserve the doctor–patient relationship as an essential safeguard for the vulnerable. This requires reimagining patient safety not through optimization metrics, but through frameworks of justice, dignity, and collective liberation. (shrink)

Current Artificial Intelligence (AI) systems, including Large Language Models (LLMs) and neuro-symbolic Automated Theorem Provers (ATPs), face severe limitations regarding semantic preservation and out-of-distribution reasoning. When attempting to transfer inferential logic across heterogeneous mathematical domains, these systems frequently suffer from "semantic hallucinations" and catastrophic forgetting. This vulnerability stems from an underlying axiomatic blindness: neural architectures process mathematical structures purely extensionally (as quantitative weights), ignoring their intrinsic ordinal structures. Drawing upon recent advancements in set-theoretic multiverse theory, this paper proposes a (...) novel framework for machine learning architectures: Skeleton-Aware Artificial Intelligence. By integrating the Axiom of Structural Identity (ASI) and the Methodological Principle of Operational Integrity (MPOI), we formalize mathematical objects as dual-invariant tensors ⟨Card, Ord⟩. Furthermore, we replace traditional heuristic loss functions with a Boolean-valued Entropic Dispersion metric. This ensures that the structural negentropy of a proof skeleton is mathematically bounded, penalizing syntactical mutations during cross-domain transfers. The result is a mathematically rigorous inference engine capable of extracting Hegel’s "Concrete Universal" across shifting axiomatic models without generating set-theoretic paradoxes. (shrink)