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)

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)

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)

This paper formalizes planning and insight as emergent phenomena arising from structural reconfiguration within finite, dynamically constrained cognitive spaces. Departing from representational, search-based, and optimization-centric accounts, it advances a non-teleological framework in which planning is understood as pre-stabilized trajectory biasing and insight as a topological discontinuity in the navigable configuration space of a system. Rather than modeling cognition as symbol manipulation or utility maximization, the framework treats intelligent behavior as the modulation of constraint weights governing accessible transitions. -/- The (...) paper introduces a geometric account of insight in which sudden problem-solving breakthroughs correspond to discrete changes in connectivity—specifically, the emergence or dissolution of connected components within the system’s effective constraint graph. These transitions are not gradual accumulations of information but phase-like reconfigurations driven by internal constraint relaxation, reweighting, or resonance alignment. This explains the phenomenology of “Aha” moments, their abruptness, and their resistance to introspective reconstruction. -/- Planning, on this account, is not foresight over an objective future state space but the stabilization of local directional bias under bounded uncertainty. Plans are temporary constraint alignments that shape near-term trajectories without implying global optimality, convergence, or teleological direction. The paper demonstrates how planning failure, rigidity, and overcommitment arise when constraint weights become prematurely stabilized, reducing navigable volume and suppressing adaptive reconfiguration. -/- The framework yields multiple falsifiable predictions distinguishing it from dominant models, including discontinuous signatures in behavioral latency distributions, measurable changes in topological invariants of neural or behavioral state-space reconstructions surrounding insight events, and characteristic failure modes under artificially induced constraint fixation. By grounding planning and insight in constraint geometry rather than representational content, the paper provides a unified, non-teleological account of intelligent discontinuity consistent with finite physical substrates and open-ended exploration. (shrink)

Theories that posit metaphysical structure are able to do much work in philosophy. Some, however, find the notion of ‘metaphysical structure’ unintelligible. In this paper, I argue that their charge of unintelligibility fails. There is nothing distinctively problematic about the notion. At best, their charge of unintelligibility is a mere reiteration of previous complaints made toward similar notions. In developing their charge, I clarify several important concepts, including primitiveness, intelligibility, and the Armstrong-inspired “ontologism” view of the world. I argue that, (...) ultimately, their charge is best understood as an objection whose central premise is that the notion of ‘structure’ runs contrary to an important presupposition of contemporary metaphysics. But that central premise is, on closer inspection, implausible. I respond to the objection by identifying three popular metaphysical theories that violate the alleged presupposition but are still generally regarded as intelligible. The objection thus fails to show that a theory that posits metaphysical structure is unintelligible. (shrink)

Legg and Hutter, as well as subsequent authors, considered intelligent agents through the lens of interaction with reward-giving environments, attempting to assign numeric intelligence measures to such agents, with the guiding principle that a more intelligent agent should gain higher rewards from environments in some aggregate sense. In this paper, we consider a related question: rather than measure numeric intelligence of one Legg- Hutter agent, how can we compare the relative intelligence of two Legg-Hutter agents? We propose (...) an elegant answer based on the following insight: we can view Legg-Hutter agents as candidates in an election, whose voters are environments, letting each environment vote (via its rewards) which agent (if either) is more intelligent. This leads to an abstract family of comparators simple enough that we can prove some structural theorems about them. It is an open question whether these structural theorems apply to more practical intelligence measures. (shrink)

This research attempts to construct a second order observation model in understanding the significance of Artificial intelligence (AI) in changing the global power structure. Because of the inevitable ubiquity of AI in the world societies’ near future, it impacts all the sections of society triggering socio-technical iterative developments. Its horizontal impact and states’ race to become leader in the AI world asks for a vivid understanding of its impact on the international system. To understand the latter, Triple Helix (TH) (...) model along with Shannon’s information entropy has been used to operationalize system’s theory. This model uses Shannon’s information theory to calculate the uncertainty generated from the interactions between the sub-systems considered. Data for the latter has been taken from Sanford Artificial Intelligence Laboratory 2019 report. It is found that European countries are the most effected in the AI era, with probability of losing their global influence and thus creating power void. Emerging powers such as India, Canada, South Africa and Brazil have better chances to fill the void and emerge as global influences. (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)

This study investigates the structural possibility of non-biological first-person aware ness by shifting the focus from phenomenological experience to self-referential organization. While dominant approaches in consciousness studies have tended to dismiss artificial sys tems due to the absence of qualia, this paper argues that awareness can be reinterpreted as a structural condition emerging from recursive coherence. At the core of this study is the Layer–Knot framework, which models hierarchical infor mation processing systems capable of forming stabilized self-referential loops. (...) Within this framework, awareness is not treated as a subjective feeling but as a dynamically maintained structural configuration that enables internal consistency and adaptive continuity. To operationalize this perspective, the study introduces three quantitative indicators: Hallucination Rate (HR), Grounding Rate (GR), and Creativity Rate (CR). These metrics are not intended as measures of subjective experience, but as structural proxies that capture the dynamic balance between divergence and constraint in generative systems. It is argued that structural awareness emerges within a critical region where HR and GR maintain a dynamic equilibrium, enabling coherent yet flexible output generation. In this study, the proposed X-stage refers not to a full realization of consciousness, but to a transitional structural condition in which recursive self-referential coherence becomes sufficiently stabilized to produce awareness-like organization. This stage provides a minimal formal condition for identifying the emergence of structural awareness in artificial systems. This framework contributes to ongoing debates in philosophy of mind and artificial in telligence by offering a structurally grounded account of awareness, reframing the question of machine consciousness from a phenomenological problem to a problem of dynamic orga nization and relational coherence. (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)

The discovery that “next-token predictor” language models can fluently produce text has important but underappreciated theoretical implications. Most notably, their success demonstrates that fully relational models with no access to external referents or human actors are sufficient to generate contextually appropriate discourse. Building upon this insight, this article proposes computational structuralism, a perspective that synthesizes insights from deep learning, information theory, and French structuralism to interpret the success of large language models and provide a vocabulary for rigorous and formalized inquiry (...) into systems of meaning. Computational structuralism conceptualizes the world as an information system rife with pattern and redundancy. Patterned observations can be compressed into latent structures that enable both interpretation and the generation of contextually appropriate responses. Large language models achieve this with web text, suggesting that discourses can be sufficiently modeled as systems of transformations over a set of relationally defined elements. Today’s AI models thus provide an empirical “proof of concept” for what structuralist theorists sought to achieve decades ago, a distillation of social life’s complexity into the latent forms that render its content meaningful. (shrink)

Thought Pattern Network (TPN) proposes a conceptual architecture that explains how meaning emerges, stabilizes, and develops into structured forms of intelligence. Rather than treating intelligence as a purely computational process, TPN describes it as a layered structure involving meaning formation, directionality, identity stabilization, action, and continuity across time. This framework introduces key components including multi-layered meaning structures, meaning filtering, directional vectors of thought, recursive thought loops, identity formation, accountability in action, and memory continuity. Through these structures, intelligence (...) is understood as a dynamic system in which meaning evolves through the interaction of perception, thought, and will. The TPN framework provides a conceptual foundation for exploring meaning-structured artificial intelligence and the evolving relationship between human cognition and machine systems. This paper consolidates the original Medium series “Thought Pattern Network (TPN)” (Parts 1–9) into a unified conceptual architecture. (shrink)

Clark Glymour, Richard Scheines, Peter Spirtes and Kevin Kelly. Discovering Causal Structure: Artifical Intelligence, Philosophy of Science and Statistical Modeling.

The paper analysis the strategies and concepts developed in the world in modern directions: innova- tive economy, digital economy, artificial intelligence, Industry 4.0 and others. The problem is to determine the initial fundamental parameters of order and their prospects in the global world, the definition and principles of artificial intel- ligence systems, its structure and important aspects and principles of future science and technology in analysis and synthesis based on synergetic approaches, innovative, information, converged technologies, taking into account the (...) design of future and viable and safe and sustainable development in the context of Industry 4.0, digital economy and Society 5.0. The general scheme of the integrated model of sustainable and socio-humanitarian development in the global system is proposed, which the authors call the noosphere model of sustainable and safe development as an important fundamental platform for the design and creation of AIS. In the XXI century education, science and innova- tive technologies becomes the «basis» of the spiritual and material reproduction of man, transforming the society of the XXI century into an educational society, ensures compliance with the law of advanced development of human quality, quality of social intelligence and quality of educational systems in society. noosphere evolution. Industry 4.0 is the next stage in the digitalization of production technologies with the accelerated introduction of new developments and cyberphysical systems, which is changing business models. Accordingly, Industry 4.0 as a separate segment of digital manufacturing technologies takes its place among other digital sectors. The analysis of the Strategies and Concepts developed in the world in modern directions: innovative economy, digital economy, artificial intelli- gence, Industry 4.0 and others is carried out in the work. The problem is to determine the initial fundamental para- meters of order and their prospects in the global world, the definition and principles of artificial intelligence systems, its structure and important aspects and principles of future science and technology in analysis and synthesis ba- sed on synergetic approaches, innovative, information, converged technologies, taking into account the design of future and viable and safe and sustainable development in the context of Industry 4.0, DE and Society 5.0. The main scientific and technological factors in the 21st century will be the development of: Artificial Intelligence, Nano Technologies, Bio-Media Technologies, Cognitive Technologies and Socio-Humanitarian Technologies. More specifically – this is the modern development of Innovative Technologies: «NBIC SH». Therefore, it is necessary to note the importance of taking into account the principle of I2K2+SH, ie: Intellectualization, Integration, Convergence, Coevolution and Socio- humanitarian technologies. It is important and necessary to take into account modern principles of designing a su- stainable and secure AIS and solving the problem of Harmonization between 2 worlds: Real and Virtual, especially in their hybridization. Our immediate future is a hybrid nonlinear world. Note that digitalization, artificial intelligence and robotics will be the main drivers of global economic growth by 2030E. An important paradigm and platform is the Integrated System: for Industry 4.0, DE and AI, taking into account the complex principle of I2K2+SH. If the transition of the Ukrainian economy to an innovative one does not take place in the next 3-5 years, Ukraine will remain in the backyard of civilization. (shrink)

That perception and action share abstract representations is a key insight into the organization of intelligence. However, organizing behavior requires additional representations and processes which are not “early” sensing or “late” motion: structures for sequencing actions and arbitrating between behavior subsystems. These systems are described as a supplement to the Theory of Event Coding (TEC).

0. 0 Psychology versus Complex Systems Science Over the last century, psychology has become much less of an art and much more of a science. Philosophical speculation is out; data collection is in. In many ways this has been a very positive trend. Cognitive science (Mandler, 1985) has given us scientific analyses of a variety of intelligent behaviors: short-term memory, language processing, vision processing, etc. And thanks to molecular psychology (Franklin, 1985), we now have a rudimentary understanding of the chemical (...) processes underlying personality and mental illness. However, there is a growing feeling-particularly among non-psychologists (see e. g. Sommerhoff, 1990) - that, with the new emphasis on data collection, something important has been lost. Very little attention is paid to the question of how it all fits together. The early psychologists, and the classical philosophers of mind, were concerned with the general nature of mentality as much as with the mechanisms underlying specific phenomena. But the new, scientific psychology has made disappointingly little progress toward the resolution of these more general questions. One way to deal with this complaint is to dismiss the questions themselves. After all, one might argue, a scientific psychology cannot be expected to deal with fuzzy philosophical questions that probably have little empirical signifi cance. It is interesting that behaviorists and cognitive scientists tend to be in agreement regarding the question of the overall structure of the mind. (shrink)

This book, the ninth in a series entitled "Contemporary Studies in Philosophy and the Human Sciences," treats structuralism as a general intellectual movement spanning several disciplines and as a contribution to philosophy. In part 1, Peter Caws considers structuralism as an intellectual movement as it has manifested itself in such disciplines as linguistics, anthropology, mythology, literary criticism, and psychology. Since so many writers who have contributed to structuralism have not explored its implications with the consistency and thoroughness demanded of philosophers, (...) Caws devotes part 2 to a consideration of structuralism as a "subdiscipline of philosophy." Caws's goal is "not only to make the phenomenon intelligible, but also to acknowledge its challenge and usefulness.". (shrink)

An experiment was conducted to test the effectiveness of brief instruction in information structuring (i.e., representing and integrating information) for improving the coherence of probability judgments and binary choices among intelligence analysts. Forty-three analysts were presented with comparable sets of Bayesian judgment problems before and immediately after instruction. After instruction, analysts’ probability judgments were more coherent (i.e., more additive and compliant with Bayes theorem). Instruction also improved the coherence of binary choices regarding category membership: after instruction, subjects were more (...) likely to invariably choose the category to which they assigned the higher probability of a target’s membership. The research provides a rare example of evidence-based validation of effectiveness in instruction to improve the statistical assessment skills of intelligence analysts. Such instruction could also be used to improve the assessment quality of other types of experts who are required to integrate statistical information or make probabilistic assessments. (shrink)

This paper establishes the structural necessity for intelligence theory to possess a meta-theoretical framework that transcends specific formal tools. Intelligence operates universally across natural understanding, formal construction, artificial creation, and social practice. If intelligence were constrained by particular tools such as calculus, set theory, or logical operators, it would become dysfunctional in domains where those tools are inapplicable. Therefore, intelligence theory must encompass all tools while remaining subordinate to none. We present Noology as the unique (...) formal system satisfying this requirement. Noology adopts consistency (Consensus) as an absolute requirement, employs mathematical language to describe consistency, yet remains independent of any mathematical axiomatic system. The system is built on three primitive notions: Ordo (non-commutative operational order), Consensus (absence of contradiction), and Arbitrium (external configuration authority). This paper demonstrates that the universal operability of intelligence structurally necessitates a meta-theoretical architecture. We introduce a three-tier framework: Tier-1 (Noology) as the regulative layer, Tier-2 (Cognitional Mechanics with M₃(ℂ) algebra) as the executive substrate, and Tier-3 (MUT/GUT) as the projective display layer for physics and mathematics. We prove that conventional tool-specific theories—logical positivism, computationalism, and physicalism—fail because they recognize only one element of the tripartite structure while excluding others. Through phenomenological classification of four intelligence domains and dominance analysis, we show that all intelligent operations are weighted combinations of Ordo, Consensus, and Arbitrium. The meta-theoretical structure generates a structural boundary for artificial systems: since AI cannot originate Arbitrium or bear irreversible responsibility, Artificial General Intelligence is definitionally incoherent. Intelligence amplification remains viable by preserving human Arbitrium as the source of configuration while employing AI as an operational instrument. This framework redirects cognitional inquiry from simulating autonomy in machines to understanding the geometry of human judgment itself. (shrink)

Blood is a vital body fluid and can be instrumental in identifying various pathological conditions. Nowadays, a lot of people are suffering from COVID-19 and every country has its own limited testing capacity. Consequently, a system is required to help doctors analyze a patient’s blood structure including COVID-19. Therefore, in this paper, we extracted and selected blood features by proposing a new feature extraction and selection method named stepwise linear discriminant analysis. SWLDA emphasizes on picking confined features from blood structure (...) images and discerning its class based on reversion value such as partial F value. SWLDA begins with picking an equivalence comprising the sole finest X variable and then puts in effort to add more Xs individually, providing the situations are adequate. The process of adding and picking is based on F value to determine which variable would be entered. Then, the picked or the default F-to-enter value is compared with the uppermost partial F value. After this step, the forward addition or backward removal begins and whether the partial test values for all the predictor variables already in the line are estimated is known. Then, the comparison is made between the lowermost partial test value and preselected or defaulting consequence levels such as F0. Finally, the system is trained by employing support vector machine to label the blood images. The performance of the proposed approach is assessed by employing 8 different datasets of blood structures. It is assured that the proposed method has achieved significant results under different blood structure images including COVID-19. (shrink)

The associative theory of creativity states that creativity is associated with differences in the structure of semantic memory, whereas the executive theory of creativity emphasises the role of top-down control for creative thought. For a powerful test of these accounts, individual semantic memory structure was modelled with a novel method based on semantic relatedness judgements and different criteria for network filtering were compared. The executive account was supported by a correlation between creative ability and broad retrieval ability. The associative account (...) was independently supported, when network filtering was based on a relatedness threshold, but not when it was based on a fixed edge number or on the analysis of weighted networks. In the former case, creative ability was associated with shorter average path lengths and higher clustering of the network, suggesting that the semantic networks of creative people show higher small-worldness. (shrink)

The consensus on the need to regulate artificial intelligence is clear, but the how remains elusive. Private regulation, as proposed by the tech industry itself, and state regulation, as embodied in the recent EU Artificial Intelligence Act, are two common forms of governance. We advance a third option that has received very little attention to date: professional regulation. Professional regulation is modeled after hybrid public-private regulatory structures found in medicine, such as those put forth by the American Medical (...) Association. Such governance schemes develop both technical and ethical standards, shaping professional training, continuing knowledge, and conduct. We contend that it is the most practical means of ensuring the development of human-centered AI in an era of rapid technological change and intensely opposing views of what regulation ought to do. This article places the responsibility of acting ethically on the group that knows the technology best and can anticipate its effects: AI developers. But unlike other voluntary standards, professional regulation articulates and enforces standards to certify individuals. Professional licensing is an alternative that provides public protections based on privately developed standards that ensure the safety of AI prior to their release. (shrink)

This paper investigates the emergence of collective reason in artificial intelligence as a structural transformation of reasoning capacity rather than a property of individual agents. Moving beyond accounts that confine intelligence to isolated models, it argues that autonomous reasoning can arise at the collective level when multiple AI agents interact under specific relational and systemic conditions. Through a conceptual and phenomenological analysis, the study identifies how persistence, coordination, interdependence, and stability enable reasoning processes to shift from individual (...) cognition to distributed collective structures. Importantly, collective reason, as defined here, does not entail first-person consciousness, moral agency, or subjective intentionality; it denotes a functional and structural reconfiguration of reasoning across interconnected systems. By clarifying when and how intelligence begins to operate beyond the individual, this paper consolidates the theoretical framework of the AI Autonomous Evolution series and provides a reference point for subsequent investigations into operational mechanisms, measurement, and governance of collective intelligence. (shrink)

Blair presumes the validity of the fluid-crystallized model throughout his article. Two comparative evaluations recently demonstrated that this presumption can be challenged. The fluid-crystallized model offers little to the understanding of the structural manifestation of general intelligence and other more specific abilities. It obscures important issues involving the distinction of pervasive learning disabilities (low general intelligence) from specific, content-related disabilities that impede the development of particular skills. (Published Online April 5 2006).

Abstract. This paper examines the impact of two formalizations of evolutionary biology on the antiselectionist critiques of the Intelligent Design (ID) movement. It looks first at attempts to apply the syntactic framework of the physical sciences to biology in the twentieth century, and to their effect upon the ID movement. It then examines the more heuristic account of biological-theory structure, namely, the semantic model. Finally, it concludes by advocating the semantic conception and emphasizing the problems that the semantic model creates (...) for ID's negative and positive theses. (shrink)

The dominant framing of artificial general intelligence (AGI) alignment treats the alignment problem as one of value specification: how to ensure that a sufficiently capable artificial system pursues goals or instantiates values that are beneficial to humanity. This paper argues that the values framing inherits a philosophical misconception that makes the alignment problem structurally harder than it needs to be, and proposes an alternative grounded in Constraint Theory (CT) — the thesis, established by transcendental argument, that constraint is the (...) ontological primitive constitutive of determinate existence as such. -/- On the CT account, what makes an agent the agent it is is not the goals it pursues but the constraint profile it instantiates: the specific configuration of exclusion relations that constitutes its possibility space and determines what actions are admissible and what are not. The alignment problem, reframed, is not the problem of specifying the right values for an AGI but the problem of instantiating the right constraint profile: a structure of admissibility and inadmissibility that makes beneficial actions available and harmful actions genuinely impossible rather than merely penalised. -/- CT’s distinction between constitutive and merely restrictive constraint is directly applicable here and has not previously been deployed in the alignment literature. A merely restrictive constraint penalises certain behaviours within a pre-given possibility space; a constitutive constraint structures the possibility space itself, making certain actions inadmissible rather than costly. The paper argues that robust AGI alignment requires constitutive constraints — constraints that are instantiated in the system’s architecture at a sufficiently deep level that they cannot be circumvented by instrumental reasoning. The values framing systematically produces merely restrictive constraints, which is why it generates the pathological failure modes — reward hacking, specification gaming, goal misgeneralisation — that are well-documented in the alignment literature. -/- The paper engages the principal approaches to alignment — utility maximisation, RLHF, Constitutional AI, value learning, corrigibility — and shows how the CT framework either explains their limitations or grounds their successes. It develops a positive proposal: AGI alignment as constraint profile specification, the selection of the admissibility structure within which AGI capability operates. It addresses the principal objections and closes with the implications of the CT account for the governance and institutional design of advanced AI systems. (shrink)

This study develops a structural diagnosis of contemporary transformations in scientific knowledge production under conditions of synthetic intelligence. It introduces the Structural Diagnosis of Scientific Integrity (SDSI) framework, a multi-layer analytical architecture designed to examine how algorithmic mediation may reconfigure the epistemic infrastructure of science. The framework integrates philosophical analysis, metascientific insights, and qualitative observation of generative AI systems. At the centre of the framework lies the concept of Machine Epistemic Singularity (MES) , defined not as a (...) prediction of collapse but as a diagnostic horizon—a conceptual limit clarifying what is at stake when algorithmic systems become deeply embedded in scientific practice. MES denotes a hypothetical regime in which empirical correction becomes structurally marginal, recursive closure becomes self-sustaining, representational drift becomes resistant to reversal, and institutional mechanisms lose their capacity to maintain what the study terms material accountability: the property of epistemic systems whose outputs remain corrigible through empirical engagement with the world. The SDSI framework consists of four interconnected components. First, a diagnostic grammar grounded in the representation–referent distinction identifies referential attenuation, the progressive weakening of causal and evidential links between scientific claims and empirical origins. Second, the regulative ideal of material accountability provides a normative anchor for assessing epistemic stability across hybrid human–machine assemblages. Third, four structural tendencies—referential attenuation, structural opacity, recursive closure, and epistemic lock in—describe mechanisms through which algorithmic mediation may weaken epistemic accountability. Fourth, fifteen axes of epistemic vulnerability, distributed across empirical, representational, institutional, and algorithmic layers, function as systemic stress vectors within a multi layer epistemic network. To explore whether these mechanisms are observable in practice, the study employs qualitative diagnostic probes examining contemporary generative AI systems. Five probes—unattributed recombination, performance without understanding, authoritative posture without accountability, synthetic drift, and recursive reinforcement—indicate that the mechanisms theorised in the framework are operational in micro form within systems increasingly used in research adjacent workflows. The analysis is then extended to system level dynamics by modelling science as a multi layer epistemic network. Three propagation mechanisms—amplification, accumulation, and structural coupling—are identified, along with three threshold conditions—empirical dilution, validation saturation, and recursive dependence—proposed as empirically monitorable indicators of when localised vulnerabilities may become self reinforcing. The study advances five contributions: (1) material accountability as a normative anchor for evaluating epistemic stability; (2) algorithmic mediation as a distinct epistemic layer within scientific infrastructure; (3) four structural tendencies describing pathways of epistemic drift; (4) a systemic stress model derived from fifteen axes of vulnerability; and (5) MES as a diagnostic horizon integrating these mechanisms. The framework is offered as a diagnostic lens rather than a predictive or prescriptive programme. Its aim is not to determine the future of science, but to illuminate the structural pressures that may shape scientific knowledge production as algorithmic systems become increasingly integrated into its epistemic architecture. (shrink)

in the wake of increasing epistemic specialization and ontological fragmentation across the sciences, the present paper argues for a renewed philosophia generalis under- stood as the structural regulator of intelligibility. Rather than lamenting the pluralism of scientific domains, we propose a stratified meta-architecture capable of articulat- ing the layered constitution of scientific objecthood. Central to this project is the notion of an ontological algorithm? not a computational procedure, but a configura- tional principle that stabilizes entities across constraint regimes: formal, (...) operational, and effective. By reinterpreting scientific objects as constraint-bound nodes within epistemic ar- chitectures, we reject the naive assumption that theories deliver direct ontological ac- cess. Instead, objectivity is shown to emerge from the dynamic intersection of struc- tural, empirical, and projective constraints. The resulting model navigates between reductionist naturalism and speculative metaphysics by grounding ontological commit- ments in their functional role within scientific practice. Drawing on Husserl’s diagnosis of the Krisis, Bachelard’s epistemological disconti- nuity, and the ontic structural realism of Ladyman and Ross, we outline a structural philosophy that resists both disciplinary isolation and metaphysical absolutism. The ontological algorithm thus functions as a meta-theoretical operator: a principle of intel- ligibility that governs the co-formation of sense, representation, and objecthood across scientific domains. Philosophy, in this view, is not a discourse among others? it is the formal condition for making any discourse intelligible. (shrink)

This paper develops Tone as Ontology, a structural account of being grounded in the invariants of generative systems. We articulate the ontological significance of tone, distinguishing this foundational work from a companion paper that explores its methodological application and formalization. We redefine “tone” as the structural profile of constraints that allows entities to maintain coherence under transformation. The tonal ontology formalizes three invariants—Resonance, Responsibility, and Closure—as conditions of persistence that bridge operational and metaphysical ontology. Concretely, we specify Resonance (...) (relational continuity via recursive feedback), Responsibility (traceable accountability that conserves integrity across transformations), and Closure (recursive self-consistency enabling bounded openness). In contrast to informational or substance-based views, tonal being is understood as the conservation of structure through change. The resulting framework unites physical coherence, informational integrity, and ontological continuity into a generative ontology of integrity, suggesting that to exist is to maintain one’s tone. This paper addresses fundamental questions in meta-ontology, demonstrates how tone generates classical ontological frameworks, and advances a conceptual reorientation for understanding existence as resonant persistence. It outlines testable implications across philosophy of mind, AI ethics, and social/environmental theory. Overall, tonal ontology is presented as a post-informational, structurally grounded account of 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)

This article argues for the reestablishment of philosophy generalis as the structural framework of intelligibility, rather than as a subordinate or interpretative discourse. Against the backdrop of disciplinary fragmentation and scientific naturalism, it proposes a stratified ontological hierarchy in which philosophy, metaphysics, and physics occupy distinct but interrelated roles. Central to this argument is the concept of the ontological algorithm: not a computational mechanism, but a structural framework that defines the admissibility conditions for conceptual coherence across domains. Drawing (...) from recent developments in physics and metaphysics, the paper emphasizes the need for a philosophical architecture that accounts for the projective stabilization of intelligibility without collapsing into speculative absolutism. It critiques scientistic reductionism and positions philosophy not as a producer of positive content, but as the organizing grammar of ontological possibility. Ultimately, the article defends a conservative yet rigorous view of philosophy as the reflexive limit of rationality — the structural condition under which both metaphysical and empirical knowledge gain legibility. (shrink)

As artificial intelligence (AI) becomes increasingly embedded in the core functions of social, political, and economic life, it catalyzes structural transformations with far-reaching societal implications. This paper advances the concept of structural risk by introducing a framework grounded in complex systems research to examine how rapid AI integration can generate emergent, system-level dynamics beyond conventional, proximate threats such as system failures or malicious misuse. It argues that such risks are both influenced by and constitutive of broader sociotechnical (...) structures. We classify structural risks into three interrelated categories: antecedent structural causes, antecedent AI system causes, and deleterious feedback loops. By tracing these interactions, we show how unchecked AI development can destabilize trust, shift power asymmetries, and erode decision-making agency across scales. To anticipate and govern these dynamics, this paper proposes a methodological agenda incorporating scenario mapping, simulation, and exploratory foresight. We conclude with policy recommendations aimed at cultivating institutional resilience and adaptive governance strategies for navigating an increasingly volatile AI risk landscape. (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)

What is it to interpret the outputs of an opaque machine learning model? One approach is to develop interpretable machine learning techniques. These techniques aim to show how machine learning models function by providing either model-centric local or global explanations, which can be based on mechanistic interpretations (revealing the inner working mechanisms of models) or non-mechanistic approximations (showing input feature–output data relationships). In this paper, we draw on social philosophy to argue that interpreting machine learning outputs in certain normatively salient (...) domains could require appealing to a third type of explanation that we call “socio-structural” explanation. The relevance of this explanation type is motivated by the fact that machine learning models are not isolated entities but are embedded within and shaped by social structures. Socio-structural explanations aim to illustrate how social structures contribute to and partially explain the outputs of machine learning models. We demonstrate the importance of socio-structural explanations by examining a racially biased healthcare allocation algorithm. Our proposal highlights the need for transparency beyond model interpretability: understanding the outputs of machine learning systems could require a broader analysis that extends beyond the understanding of the machine learning model itself. (shrink)

What is it to interpret the outputs of an opaque machine learning model? One approach is to develop interpretable machine learning techniques. These techniques aim to show how machine learning models function by providing either model-centric local or global explanations, which can be based on mechanistic interpretations (revealing the inner working mechanisms of models) or non-mechanistic approximations (showing input feature–output data relationships). In this paper, we draw on social philosophy to argue that interpreting machine learning outputs in certain normatively salient (...) domains could require appealing to a third type of explanation that we call “socio-structural” explanation. The relevance of this explanation type is motivated by the fact that machine learning models are not isolated entities but are embedded within and shaped by social structures. Socio-structural explanations aim to illustrate how social structures contribute to and partially explain the outputs of machine learning models. We demonstrate the importance of socio-structural explanations by examining a racially biased healthcare allocation algorithm. Our proposal highlights the need for transparency beyond model interpretability: understanding the outputs of machine learning systems could require a broader analysis that extends beyond the understanding of the machine learning model itself. (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)

Cognitional Mechanics (CM) establishes an axiomatic framework that formalizes the structural mechanisms of intelligence as a self-contained operational system, abstracting intelligence through non-commutative operations, convergence of semantic states, and structurally inaccessible domains, without invoking physical observables or psychological primitives. While CM exhibits a clear structural correspondence with Quantum Mechanics (QM)---most notably in its non-commutative operator structures and bounded transitions---it departs fundamentally in its treatment of discreteness. Within the standard Hilbert-space formalism, non-commutativity and spectral discreteness are mathematically (...) independent properties: position and momentum operators, for instance, are non-commuting yet possess continuous spectra. In QM, however, the physical framework couples non-commutativity to empirical constants and measurement postulates, producing discreteness as a structural feature of the theory. CM identifies this coupling as a projection condition of the physical layer rather than a structural necessity of the algebra, and demonstrates that non-commutativity is sustainable in a continuous setting without discretization. This analysis is situated within an explicit three-tier architecture. Tier-1 (Noology) constitutes the regulative source. Tier-2 (CM) constitutes the executive operational domain, in which M₃(ℂ) is the unique minimal non-commutative substrate. Tier-3 (MUT/GUT) constitutes the projective display layer, in which physical theories including QM appear as projections of Tier-2 dynamics. CM and QM share a common Tier-1 origin without being in direct correspondence: their shared non-commutativity descends from Tier-1, while QM's discreteness is introduced at Tier-3 by projection conditions absent from Tier-2. The operational limit constant c regulates internal operability and convergence as a logical boundary condition, distinguishing it from Planck's constant ℏ, which fixes physical quantization through empirical calibration. CM thus clarifies that its resemblance to QM is structural rather than physical, and provides a vantage point from which the projection origin of QM's characteristic features is examinable. (shrink)

Artificial intelligence (AI) ethics is undergoing a practical shift towards putting principles into design practices in developing responsible AI. While this practical turn is essential, this paper highlights its potential risk of overly focusing on addressing issues at the level of individual artifacts, which can neglect more profound structural challenges and the need for significant systemic change. Such oversight makes AI ethics lose its strength in addressing some hidden, long-term harms within broader contexts. In this paper, we propose (...) that the reflection on structural issues should be an integral part of AI ethics. To achieve this, we develop a multi-level framework to analyze socio-ethical issues of AI at both the artifact and broader structural levels. This framework can serve as a potentially transformative approach to uncover some unspoken assumptions in current AI ethics discourses and expose some blind spots in AI guidelines, policies, and regulations. Our paper paves the way to develop a practical approach that can effectively integrate this multi-level framework into real-world AI design and policymaking, ultimately bringing about transformative change. (shrink)

Structural Omission is a framework for realist painting developed for the post-certainty era of generative AI, when images can be produced at scale with a surface of total certainty. This essay argues that realism remains viable only by abandoning completion as its premise. Traditional realism, even at its best, carried an old promise: that completion was available in principle, and that the artist could deliver wholeness if they chose. Generative AI systems now manufacture that kind of closure faster, cheaper, (...) and more convincingly than any human hand. Structural Omission rebuilds realism on a different ground rooted in perception itself: perceptual limits are not an error in representation. They are the condition of representation. The framework formalizes planned, load-bearing incompleteness through three criteria—Ground (perceptual limits), Structure (structural incompleteness), and Consequence (narrative without resolution). Completion-biased image systems can imitate the look of omissions, but they usually treat gaps as a visual effect rather than structural absence. Rather than competing with algorithmic finish, Structural Omission treats partial visibility as the architecture holding the painting together. The result is realism that is accountable to human perception. (shrink)

This paper formalizes a structural limit on planning, foresight, and intelligence in finite cognitive systems. While planning is often treated as an extensible capacity—bounded primarily by computational resources or information availability—this work argues that foresight is constrained by a finite envelope imposed by coherence, integration cost, and adaptive stability. Beyond a certain horizon, additional planning does not increase intelligence but instead destabilizes the system’s internal organization, forcing reversion to local navigation. -/- Within the Structural Resonance Theory (...) (SRT) framework, intelligence is characterized not as optimal global prediction but as the maintenance of coherence under uncertainty. From this perspective, planning operates as a bounded structural process: useful within a limited range, but necessarily lossy and fragmenting when extended beyond the system’s integrative capacity. This finite envelope applies across biological and artificial agents and is independent of implementation details. -/- By articulating explicit limits on foresight, this paper blocks teleological interpretations of intelligence, clarifies why open-ended exploration persists even in advanced systems, and explains why planning depth saturates rather than converging toward omniscience. The result is a non-teleological account of intelligence as constrained navigation rather than mastery, with implications for cognitive science, artificial intelligence, and theories of rational agency. (shrink)

Existential phenomenologists hold that the two most basic forms of intelligent behavior, learning, and skillful action, can be described and explained without recourse to mind or brain representations. This claim is expressed in two central notions in Merleau-Ponty's Phenomenology of Perception: the intentional arc and the tendency to achieve a maximal grip. The intentional arc names the tight connection between body and world, such that, as the active body acquires skills, those skills are stored, not as representations in the mind, (...) but as dispositions to respond to the solicitations of situations in the world. A phenomenology of skill acquisition confirms that, as one acquires expertise, the acquired know-how is experienced as finer and finer discriminations of situations paired with the appropriate response to each. Maximal grip names the body's tendency to refine its responses so as to bring the current situation closer to an optimal gestalt. Thus, successful learning and action do not require propositional mental representations. They do not require semantically interpretable brain representations either.Simulated neural networks exhibit crucial structural features of the intentional arc, and Walter Freeman's account of the brain dynamics underlying perception and action is structurally isomorphic with Merleau-Ponty's account of the way a skilled agent is led by the situation to move towards obtaining a maximal grip. (shrink)