Since the Enlightenment, there have been advocates for the rationalizing efficiency of enlightened sovereigns, bureaucrats, and technocrats. Today these enthusiasms are joined by calls for replacing or augmenting government with algorithms and artificial intelligence, a process already substantially under way. Bureaucracies are in effect algorithms created by technocrats that systematize governance, and their automation simply removes bureaucrats and paper. The growth of algorithmic governance can already be seen in the automation of social services, regulatory oversight, policing, the (...) justice system, and the military. However, there have also always been justified democratic and economic criticisms of autocracy, bureaucracy, technocracy, and algorithmic governance. Bureaucrats, technocrats, and algorithms embody biases that tend to serve the interests of elites, and all require transparency and democratic accountability, oversight individual citizens are ill equipped to exercise. As state apparatuses are increasingly automated, mechanisms for collective action and democratic oversight also need to be automated. Algorithms and cyborg citizenry will enable a posthuman democracy. Democratically accountable algorithmic governance, enabled by artificial intelligence and human enhancement, can automate bottom-up citizen surveillance, inform debate, aggregate decision making, and ensure the efficient working of the gradually withering state. As paid work disappears and we transition to a postcapitalist economy with a universal basic income, market mechanisms can be replaced with democratic planning. Indeed, only algorithmic governance can secure our future against accelerating threats from technological innovation. (shrink)

Leading digital platform providers such as Google and Uber construct marketplaces in which algorithms set prices. The efficiency-maximising free market credentials of this approach are touted by the companies involved and by legislators, policy makers and marketers. They have also taken root in the public imagination. In this article we challenge this understanding of algorithmically constructed marketplaces. We do so by returning to Hayek’s (1945) classic defence of the price mechanism, and by arguing that algorithmically-mediated price mechanisms do not, and (...) probably cannot, accumulate and signal that same kinds of knowledge that Hayek felt were essential to effective market governance. Indeed, we argue that algorithmically-constructed marketplaces are closer to, though distinct from, the central planning model that Hayek critiqued. Regardless of how you feel about Hayek’s argument, this has important consequences for how we respond to the rise of algorithmic governance tools in both markets and elsewhere. (shrink)

The current debate around artificial intelligence revolves around the question of how to make AI systems more ethical. This article advances the more radical thesis that ethical AI is categorically impossible—not due to technical limitations, but for fundamental philosophical reasons. The paper argues that moral action requires consciousness, intentionality, and the capacity to assume responsibility. AI systems possess none of these properties, nor can they acquire them through improved algorithms. The problem is not technical but categorical in nature: machines are (...) not moral agents and cannot become such. Building on this philosophical foundation, the article analyzes the structural contradictions of current AI ethics approaches. Using the fairness trilemma (Chouldechova, Kleinberg), it demonstrates that even formal-mathematical attempts to develop "fair" algorithms fail due to irresolvable goal conflicts. The case study on algorithmic risk assessment in criminal justice shows how AI systems systematically reproduce existing inequalities regardless of their developers' intentions. The consequence of this analysis is not pessimistic about technology but clarifying: instead of relying on algorithmic self-regulation, the focus must be directed toward institutional control, democratic governance, and human responsibility. The paper develops principles for regulation that treats AI systems as what they are: tools that require human oversight and political steering. The article thus positions itself against the dominant discourse that equates technical optimization with ethical improvement, advocating for a paradigm shift from "ethical AI" to "ethical AI governance.". (shrink)

This essay argues that Big Tech companies have evolved into a new class of geopolitical actors—corporate sovereigns—whose authority increasingly rivals or constrains that of nation-states. Their power does not derive from territorial control, but from the ownership and operation of digital infrastructures that have become essential to communication, commerce, administration, and security. Through cloud platforms, identity systems, data pipelines, algorithmic governance, lobbying networks, satellite networks, and AI capabilities, these firms now exercise forms of operational authority once reserved for (...) public institutions. This paper examines how infrastructural dependence, data visibility, economic concentration, political leverage, and security functions collectively produce a form of non-state sovereignty that reshapes global governance. It concludes that the rise of corporate sovereignty marks a structural transformation in the organization of power—one that existing regulatory frameworks cannot fully address, and that demands new philosophical and political models for accountability and democratic oversight. (shrink)

Inspired by recent breakthroughs in predictive modeling, practitioners in both industry and government have turned to machine learning with hopes of operationalizing predictions to drive automated decisions. Unfortunately, many social desiderata concerning consequential decisions, such as justice or fairness, have no natural formulation within a purely predictive framework. In efforts to mitigate these problems, researchers have proposed a variety of metrics for quantifying deviations from various statistical parities that we might expect to observe in a fair world and offered a (...) variety of algorithms in attempts to satisfy subsets of these parities or to trade o the degree to which they are satised against utility. In this paper, we connect this approach to fair machine learning to the literature on ideal and non-ideal methodological approaches in political philosophy. The ideal approach requires positing the principles according to which a just world would operate. In the most straightforward application of ideal theory, one supports a proposed policy by arguing that it closes a discrepancy between the real and the perfectly just world. However, by failing to account for the mechanisms by which our non-ideal world arose, the responsibilities of various decision-makers, and the impacts of proposed policies, naive applications of ideal thinking can lead to misguided interventions. In this paper, we demonstrate a connection between the fair machine learning literature and the ideal approach in political philosophy, and argue that the increasingly apparent shortcomings of proposed fair machine learning algorithms reflect broader troubles faced by the ideal approach. We conclude with a critical discussion of the harms of misguided solutions, a reinterpretation of impossibility results, and directions for future research. (shrink)

The crisis of truth in an era of knowledge systems supported by data at scale and operated together with algorithms cannot be explained solely through deficiencies in data, methods, or computational models. Behind this technical infrastructure lies a layer of epistemic intention on the part of agents that determines whether knowledge is offered in service of truth or directed toward sustaining identity, institutional power, or strategic advantage. Using The Cohesive Tetrad as a conceptual lens, this article develops a research architecture (...) for truth governance under minimal human dignity axioms. It introduces Epistemic Intention Purification and Intention-Trace Alignment as key constructs that connect Sabda (Revelatory Word, the authoritative source of norm and telos), Logos as disciplined reasoning, Qualia as lived experience, and Mistika as the intentional spiritual calibration of intention, with akhlak as the observable surface where their convergence is tested. Methodologically, the article proposes a normative conceptual design, formulates axioms, articulates the internal relations among the pillars of the Tetrad, and compares the resulting architecture with data-centered, rule-centered, and virtue-centered approaches in philosophy of science and public ethics. The analysis shows that monolingual regimes of truth tend to generate characteristic structural failure modes, whereas the Tetrad yields a non-reductive matrix that can host multiple methodologies while keeping akhlak as the locus of verification. The article concludes that, under the stated axioms, Epistemic Intention Purification and Intention-Trace Alignment provide a coherent and practically relevant framework for truth governance in law, public policy, and artificial intelligence, while remaining open to empirical testing and intercultural refinement. (shrink)

This paper critiques the hegemonic status of the "computational metaphor" in contemporary thought. It argues that this metaphor functions not as a neu- tral descriptive tool, but as a complete and coherent "Paradigm of Rationality" (PoR)—a contingent, historical framework that sets the a priori conditions for intelligibility. Using the PoR’s fourfold analytical structure (Ontological, Epistemic, Axiological, Methodological), the paper maps the core metaphys- ical commitments of the computational worldview, revealing its postulate of "reality-as-information" and its valorization of "optimization" as the (...) highest good. This new anthropology gives rise to a new subject, Homo Algorithmicus, whose sense of self is a machine-generated Subjectivity ex Machina. The paper then analyzes the paradigm’s logic of hegemonic control, specifically its "auto-concealment" as a post-ideological, neutral framework and its "judgment of unintelligibility," which pathologizes non-computable realities and inefficient practices. By making the architecture of this algorithmic prison visible, the pa-per serves a diagnostic and liberatory purpose: to create the critical distance necessary to challenge its premises and expand the horizon of reason beyond its computational limits. (shrink)

Research on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016, 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis of epistemic and normative (...) concerns, and to offer actionable guidance for the governance of the design, development and deployment of algorithms. (shrink)

Following media-theoretical studies that have characterized digitization as a process of all-encompassing cybernetization, this paper will examine the timely and critical potential of Günther Anders’s oeuvre vis-à-vis the ever-increasing power of cybernetic devices and networks. Anders has witnessed and negotiated the process of cybernetization from its very beginning, having criticized its tendency to automate and expand, as well as its circular logic and ‘integral power’, including disruptive consequences for the constitution of the political and the social. In this vein, Anders’s (...) works, particularly his magnum opus Die Antiquiertheit des Menschen [The Obsolescence of Man], sheds new light on the technologically organized milieus of the contemporary digital regime and also highlights a new form of cybernetic ‘conformism’. The goal of the essay is therefore, not only to emphasize the contemporary nature of Anders’s thought but also to use it to frame a critique of current neo-technocratic and, ultimately, post-political concepts, such as ‘algorithmic regulation’, ‘smart states’, ‘direct technocracy’, and ‘government as platform’. This essay argues that cybernetic capitalism is causing what Anders terms ‘ Unfestgelegtheit’ to disappear; that is, we are losing the originary possibility of technologically (re-)structuring our world in alternative ways, particularly given the determinist character of current technologies. (shrink)

We apply recent ideas about complexity and randomness to the philosophy of laws and chances. We develop two ways to use algorithmic randomness to characterize probabilistic laws of nature. The first, a generative chance* law, employs a nonstandard notion of chance. The second, a probabilistic* constraining law, impose relative frequency and randomness constraints that every physically possible world must satisfy. The constraining notion removes a major obstacle to a unified governing account of non-Humean laws, on which laws govern by (...) constraining physical possibilities; it also provides independently motivated solutions to familiar problems for the Humean best-system account (the Big Bad Bug and the zero-fit problem). On either approach, probabilistic laws are tied more tightly to corresponding sets of possible worlds: some histories permitted by traditional probabilistic laws are now ruled out as physically impossible. Consequently, the framework avoids one variety of empirical underdetermination while bringing to light others that are typically overlooked. (shrink)

This article advances the Algorithmic Authoritarianism Hypothesis: that contemporary artificial intelligence does not generate authoritarianism ex nihilo, but functions as a powerful accelerator of its political, psychological, and institutional preconditions. Drawing primarily on Hannah Arendt’s concepts of natality, judgment, and the erosion of the common world, alongside Michel Foucault’s account of governmentality and Shoshana Zuboff’s analysis of instrumentarian power, the article argues that algorithmic governance reshapes the conditions under which democratic action remains possible. By rendering social behaviour (...) predictable, optimisable, and anticipatable, AI systems displace political authorship from collective judgment toward technical modulation, producing a form of “soft authoritarianism” that preserves electoral forms while hollowing out substantive freedom. Using the United States as an illustrative case rather than an exceptional one, the article diagnoses a contemporary “digital Weimar” condition in which loneliness, epistemic fragmentation, and predictive control converge. Against this tendency, it reclaims political natality as the capacity to interrupt algorithmic recurrence through spontaneous action and judgment. The article concludes by outlining the contours of an “algorithmic humanism” oriented toward preserving unpredictability, plurality, and democratic agency under conditions of technological acceleration. (shrink)

Across ratings, feeds, search, and matching, the same actor measures attention, monetizes it, optimizes the interface to harvest more of it, and mediates daily decisions at civic scale. When these four functions coalesce, platforms drift from value toward compulsion, from pluralism toward conformity, and from open debate toward covert agenda-setting. We propose treating dominant attention platforms as civic media utilities with public-health stakes. Contributions: (1) a Civic-Utility Trigger that adds a Compulsion Index (CI) to necessity/dominance tests; (2) a Duty Stack—D1 (...) Right to a Reason & Appeal, D2 Responsibility Trace, D3 Church/State Separation of ranking and ads, D4 Duty to Diversify (local and global), D5 Auditability, D6 Reversibility, D7 Child Protections; (3) Instrumentation—operational definitions and governance for CI, a Variety Index (VI), an Imbalance Index (II), and a Responsibility Diffusion Index (RDI); and (4) four sector vignettes (consumer ratings, ride-hail matching, video/streaming feeds, paid search/news). We align the framework with current regulatory momentum and outline testable pilots. The opening vignette names the pattern plainly: algorithmic opium. (shrink)

This paper presents ten independent lines of evidence demonstrating that the same transformational algorithm governs collapse, reorganization, and rebirth across physical, biological, psychological, and spiritual domains. Through parallel analysis of supernova nucleosynthesis, mass extinctions, quantum wave function collapse, galactic mergers, cosmological cyclic models, trauma induced psychological growth, identity reconstruction, moral law dynamics, and the phenomenology of consciousness, we show that these systems exhibit identical structural, functional, and informational properties. The findings support the hypothesis that the universe and the human soul (...) operate on a shared computational architecture, suggesting a unified underlying Source Code. Across cosmology, biology, psychology, and spiritual traditions, collapse events have traditionally been interpreted as destructive anomalies. This paper challenges that assumption by demonstrating that collapse is the primary mechanism through which systems at every scale reorganize into higher complexity. Through ten parallel proofs, this paper argues that the universe and the human soul operate on the same transformational algorithm: Collapse → Reorganization → Rebirth → Higher Complexity. Drawing from supernova nucleosynthesis, mass extinctions, quantum measurement theory, galactic mergers, cosmological cyclic models, trauma psychology, identity reconstruction, moral law dynamics, and the phenomenology of consciousness, the analysis reveals structural, functional, and informational equivalence across domains. The findings support the hypothesis that the universe and consciousness share a unified computational architecture, suggesting that physical and psychological evolution are expressions of a single underlying Source Code. (shrink)

This paper argues that instituting Citizen Boards of Governance (CBGs) is the optimal strategy to democratically contain Big Tech’s algorithmic powers in the digital public sphere. CBGs are bodies of randomly selected citizens that are authorized to govern the algorithmic infrastructure of Big Tech platforms. The main advantage of CBGs is to tackle the concentrated powers of private tech corporations without giving too much power to governments. I show why this is a better approach than ordinary state (...) regulation or relying on market mechanisms. My proposal follows from the critique of Big Tech’s concentrated powers, and explains how this justifies democratizing algorithms in the digital public sphere. My approach thus speaks to a core commitment in democratic theory: enhancing the autonomy of the public sphere from the centers of powers in modern societies, be it corporations or governments. (shrink)

Modern legal orders presuppose that acts have authors: someone can be asked “why this?” and someone can answer. This paper argues that algorithmic decision-making destabilizes that institutional grammar of imputation in two coupled ways. First, it erodes agency in practice through routine delegation, converting professionals into relays of system outputs. Second, a technical shift from deterministic, inspectable procedures (“glass box”) to probabilistic, opaque learning systems (“black box”) converts legitimacy from reason-giving to performance, producing decisions that may be effective yet (...) structurally resistant to attribution. Methodologically, the paper combines conceptual analysis, doctrinal reading of EU governance instruments (GDPR and the EU AI Act), and institutional analysis of accountability pipelines. Drawing on the triadic model of agency (initiate, judge, respond) and the ontology of signature developed in LA CAUSA PRIMERA (Fernández Tamames, 2026), it argues that explainability, while valuable, is insufficient as a remedy, and that “electronic personhood” is a liability-displacement fiction. The political remedy proposed is sovereign signing: institutionalized, reflexive human signature for high-stakes algorithmic outputs, coupled with contestability, traceability, and enforceable liability anchoring. Imputation is thereby defended as political resistance against organized irresponsibility in algorithmic governance. (shrink)

The rapid integration of Machine Learning (ML) systems into the governance, financial, and social infrastructures of the Global South is frequently framed as a neutral imperative of technological development. This article challenges that narrative by analysing ML models not as passive tools, but as active "epistemic artefacts" that function through a mechanism of imposition termed the Algorithmic Gaze. Drawing on Foucault’s concept of panopticism and contemporary decolonial theory, I argue that the Algorithmic Gaze is an "imported epistemology" (...) that encodes the specific cultural values of its creators, primarily the hyper individualism, quantification, and capitalist logic of the Global North, while obscuring them behind a "cultural black box" of technical opacity. The article posits that the uncritical deployment of these systems in African contexts constitutes Algorithmic Coloniality. By extracting data as a raw resource and returning it as a disciplining logic, these systems overwrite local, relational forms of knowledge with an external, atomised metric of value. A case study of fintech in Nairobi illustrates how this gaze redefines "trust" from a communal virtue to a calculated probability, eroding indigenous social fabrics. Moving beyond critique, the article proposes the forging of an African AI Epistemology. Grounded in the philosophical ethic of Ubuntu, which privileges relationality over autonomy, and the integration of Indigenous Knowledge Systems (IKS), this framework offers a path toward resistance and re-inscription. I conclude that decolonising AI requires a shift from "data subjects" to "data stewards," advocating for communal data self-determination and the co-creation of hybrid models that reflect a pluriverse of human intelligence. (shrink)

Algorithmic governmentality is a new form of political governance interconnected with technology and computation. By coining the term “algorithmic governmentality,” Antoinette Rouvroy argues that this mode of governance reduces everything to data, and people are no longer individuals but dividuals (able to be divided) or readable data profiles. Implementing the concept of algorithmic governmentality, the current study analyses Project Itoh’s award-winning novel Harmony in terms of such relevant concepts as “subjectivity,” “infra-individuality” and “control,” as suggested (...) by Rouvroy and colleagues. The analysis further draws on Foucault’s ideas of “governmentality” and “neoliberalism” and Deleuze’s concepts of “dividuality” and “society of control.” In Itoh’s fictional world, the entire society is subject to the new medical government, which replaced the chaotic world with a perfect but harrowing benevolent society, eradicating disease, suicide and crime. As elucidated in this paper, despite the dominant controlling government, there is a hope for humans to regain and reestablish their subjectivity and give meaning to their own personality by defying the nihilistic computational system. (shrink)

This essay advances what I call the Algorithmic Authoritarianism Hypothesis: that artificial intelligence (AI) and its digital infrastructure do not create authoritarianism from nothing but radically accelerate its social, psychological, and institutional preconditions. Drawing upon Hannah Arendt’s phenomenology of loneliness, Michel Foucault’s analysis of disciplinary power, Gilles Deleuze’s “societies of control,” Byung-Chul Han’s psychopolitics, and Karl Marx’s theory of alienation, the essay interprets the current democratic erosion—especially in the United States—as a technologically mediated return of twentieth-century totalitarian tendencies. The (...) argument integrates philosophical inquiry with empirical evidence of political backsliding, inequality, and digital surveillance. The result is a framework for understanding how AI-driven governance transforms classical domination into predictive modulation, producing a soft totalitarianism that feels voluntary while undermining the very conditions of human freedom. (shrink)

This article is on algorithmically generated memories: data on past events that are stored and automatically ranked and classified by digital platforms, before they are presented to the user as memories. By mobilising Henri Bergson's philosophy, I centre my analysis on three of their aspects: the spatialisation and calculation of time in algorithmic systems, algorithmic remembrance, and algorithmic perception. I argue that algorithmically generated memories are a form of automated remembrance best understood as perception, and not recollection. (...) Perception never captures the totality of our surroundings but is partial and the parts of the world we perceive are the parts that are of interest to us. When conscious beings perceive, our perception is always coupled with memory, which allows us to transcend the immediate needs of our body. I argue that algorithmic systems based on machine learning can perceive, but that they cannot remember. As such, their perception operates only in the present. The present they perceive in is characterised by immense amounts of data that are beyond human perceptive capabilities. I argue that perception relates to a capacity to act as an extended field of perception involves a greater power to act within what one perceives. As such, our memories are increasingly governed by a perception that operates in a present beyond human perceptual capacities, motivated by interests and needs that lie somewhat beyond interests of needs formulated by humans. Algorithmically generated memories are not only trying to remember for us, but they are also perceiving for us. (shrink)

Government Exploration is pivotal for public development and profitable growth. still, icing that sensitive exploration content is safe from unauthorized access and cyber pitfalls is a significant challenge. Traditional storehouse styles frequently suffer from data breaches, inefficiencies, and availability issues. This study introduces an innovative result using AES- translated QR law technology to ameliorate the security and availability of government exploration accoutrements. By applying the AES(Rijndael) encryption algorithm, exploration data is securely converted into ciphertext and also bedded into QR canons. (...) This means that only authorized labor force with the correct decryption key can pierce the original content. Integrating QR law technology not only enhances data reclamation effectiveness but also boosts data protection, making it a strong and dependable system for securing government exploration records. (shrink)

This paper advances a constitutional re-foundation of democracy for the age of artificial intelligence. It argues that democratic legitimacy no longer rests on procedural participation or informational abundance but on fiduciary–epistemic trust—the moral architecture that sustains truthful, reciprocal knowing. Artificial intelligence challenges this foundation not merely through misinformation but through algorithmic clientelism: the systemic conversion of epistemic autonomy into managed dependence within opaque infrastructures of mediation. Integrating fiduciary theory (Frankel 1983; Smith 2023; Gold & Miller 2014; Kahl 2025i), epistemic (...) justice (Fricker 2007; Medina 2013), and hybrid-governance scholarship (Ofir & Levine-Schnur 2025; Lim & Lim 2025), the paper situates AI developers and institutions as epistemic fiduciaries whose legitimacy depends on duties of candour, care, impartiality, and accessibility. It introduces a unifying framework—Fiduciary Epistemic Governance (FEG)—that redefines AI accountability as a constitutional ethic of truth stewardship rather than risk management. The analysis culminates in a set of institutional innovations: Epistemic Fiduciary Entities (EFEs), Public Epistemic Trusts (PETs), and Epistemic Audits, each designed to embed fiduciary openness and relational accountability within AI governance. These mechanisms instantiate what the paper terms the Fiduciary Constitution of Democracy 2.0—a democratic order re-founded on fiduciary reciprocity between human and artificial knowers, where truth is preserved not by control but by trust. Beyond its policy applications, the paper contributes to fiduciary and political theory alike: it extends fiduciary law from property and management into epistemic governance and reconceives democracy itself as a fiduciary-epistemic constitution of knowledge—a living architecture of candour, care, and mutual responsibility. (shrink)

Monitoring and Evaluation (M&E) has emerged as a critical component of development practice, especially in African contexts where donor legitimacy and accountability are upheld by demonstrable quantitative impact. Typically seen as a technical endeavour, M&E is frequently presumed to yield impartial, objective assessments of societal advancement. This paper contests that assumption by proposing a philosophical redefinition of M&E as performative ritualism, a fusion of Judith Butler’s theory of performativity and Victor Turner’s anthropology of ritual. The analysis employs a critical conceptual (...) methodology and normative critique. Butler's explanation of performativity shows how indicators and reports work not just as reflections but also as performative utterances that have an "impact" through their repeated use in rituals. Turner's ritual theory shows that evaluation cycles baseline, midline, and endline act as rites of passage, turning ambiguity into symbolic closure for funders and policymakers. Post-humanist insights further expand these frameworks, illustrating how dashboards, algorithms, and artificial intelligence tools now operate alongside human evaluators, reinforcing evaluative categories through automated repetition. The findings indicate the cultural and ethical conflict of these activities within African cultures. Western evaluation rituals focus on linearity, abstraction, and bureaucratic closure. In contrast, African communitarian ethics focus on relational accountability, oral testimony, and cyclical processes of responsibility. The difference shows how theatrical imported evaluative systems are; it also shows how to recreate ethics by thinking of evaluation as participatory storytelling and communal debate instead of a bureaucratic audit. The article finds that monitoring and evaluation (M&E) is not a neutral tool, but a symbolic system that enacts legitimacy and authority. Thinking of M&E as performative ritualism brings out its cultural and philosophical sides and makes room for hybrid models that mix computational tools with community-based, relational practices. The paper provides a novel framework for examining development evaluation in the algorithmic era, rooted in African ethical philosophy. (shrink)

[EN] This treatise analyzes the transition from knowledge as an emancipatory tool to knowledge as an infrastructure of governability. In contemporary society, functionality has replaced truth as the primary systemic criterion, leading to a new epistemic asymmetry. The work introduces the concept of the 'Opaque Subject' as a practical position of resistance—a collective architecture capable of protecting unpredictability against the totalizing logic of algorithmic management and data extraction.

The rapid integration of artificial intelligence into financial markets is transform ing the architecture of investment decision-making. As algorithmic trading systems operate at increasing speed, scale, and autonomy, systemic risk may emerge not from irrational human behavior but from synchronized algorithmic rationality. This paper asks a critical question: Can AI trigger the next financial crisis, and if so, who bears responsibility? The study introduces the concept of a structural responsibility gap in AI-mediated investment environments. By distinguishing computational output (...) from normative judgment, it argues that investment decisions inherently involve risk endorsement, value commitment, and accountability beyond probabilistic calculation. When judgment is delegated to autonomous systems, responsibility becomes layered and fragmented across developers, institutions, investors, and regulators. Through analysis of algorithmic feedback loops, synchronization dynamics, and crisis amplification mechanisms—including flash crash events—the paper demon strates how automated coordination may intensify systemic instability. To address this gap, it proposes a Hierarchical Joint Responsibility Model designed to govern delegated judgment without attributing moral agency to AI systems. The stability of algorithmic finance ultimately depends not solely on technological control, but on the structural redesign of accountability. (shrink)

An increasing number of regulations propose the notion of ‘AI audits’ as an enforcement mechanism for achieving transparency and accountability for artificial intelligence (AI) systems. Despite some converging norms around various forms of AI auditing, auditing for the purpose of compliance and assurance currently have little to no agreed upon practices, procedures, taxonomies, and standards. We propose the ‘criterion audit’ as an operationalizable compliance and assurance external audit framework. We model elements of this approach after financial auditing practices, and argue (...) that AI audits should similarly provide assurance to their stakeholders about AI organizations’ ability to govern their algorithms in ways that mitigate harms and uphold human values. We discuss the necessary conditions for the criterion audit, and provide a procedural blueprint for performing an audit engagement in practice. We illustrate how this framework can be adapted to current regulations by deriving the criteria on which ‘bias audits’ for hiring algorithms can be performed, as required by the recently effective New York City Local Law 144 of 2021. We conclude by offering critical discussion on the benefits, inherent limitations, and implementation challenges of applying practices of the more mature financial auditing industry to AI auditing where robust guardrails against quality assurance issues are only starting to emerge. Our discussion as informed by experiences in performing these audits in practice highlights the critical role that an audit ecosystem plays in ensuring the effectiveness of such methodology. (shrink)

Contemporary digital capitalism increasingly governs not merely behaviour, but affect, attention, and interior mental states. While surveillance capitalism has been extensively theorised in relation to waking life, the domain of sleep and dreaming remains comparatively unexamined. This paper introduces the concept of dream enclosure to describe the transformation of involuntary mental life into administrable, tiered, and subscription-gated cognitive territory. Under this regime, nightmares become billable conditions, calm becomes a premium feature, and interior peace becomes access-controlled rather than morally guaranteed. Because (...) sleeping subjects are maximally vulnerable and unreachable, harms inflicted within dream space are both unappealable and administratively invisible. I argue that unconscious subjects cannot meaningfully consent to ongoing interior modification and that prior consent models fail to account for the moral status of offline selves, producing a novel class of moral injury. Dream enclosure thus marks the final phase in the governance of human experience, completing the transition from attention economies to consciousness economies. The paper concludes by proposing interiority firewalls — normative constraints that protect unconscious interior life as a non-commodifiable moral domain, and establish sleeping subjects as distinct moral patients whose vulnerability imposes categorical ethical limits on market and institutional intervention. (shrink)

In this paper, we relate the philosophical literature on pessimism traps to information cascades, a formal model derived from the economics and mathematics literature. A pessimism trap is a social pattern in which individuals in a community, in situations of uncertainty, copy the sub-optimal actions of others, despite their individual beliefs. This maps nicely onto the concept of an information cascade, which involves a sequence of agents making a decision between two alternatives, with a private signal of the superior alternative (...) and a public history of others’ actions. Key results from the economics literature show that information cascades occur with probability one in many contexts, and depending on the strength of the signal, populations can fall into the incorrect cascade very easily and quickly. Once formed, in the absence of external perturbation, a cascade cannot be broken – therefore, we derive an intervention that can be used to nudge a population from an incorrect to a correct cascade and, importantly, maintain the cascade once the subsidy is discontinued. We extend this to the case of multiple communities, each of which might have a different optimal action, and a government providing subsidies that cannot discriminate between communities and does not know which action is optimal for each. We study this both theoretically and empirically. (shrink)

The article examines a deep tension at the heart of contemporary truth governance in science, law, and artificial intelligence. Many institutions work with an architecture of pure logic that leans on scientism, hard evidentialism, a strict fact value split, and strong naturalism about human dignity and telos, yet they continue to speak in the ordinary language of rights, justice, and responsibility. Drawing on the canonical formulation of The Cohesive Tetrad, the paper develops an alternative architecture in which Sabda as (...) Revelatory Word sets norm and telos and stands in ordered relation with logic, Qualia, Mistika as the intentional spiritual journey toward direct encounter with the transcendent, Akhlak, and Intention-Trace Alignment. This configuration of truth domains is then tested against a set of minimal human dignity axioms and compared with the architecture of pure logic. The comparison is brought down to earth through two hard test beds, namely legal rationality and the governance of artificial intelligence, where the paper traces how each architecture organises purpose, constraint, and long term moral trace. In doing so, the article argues that systems built only on pure logic are prone to procedural absolutism, epistemic injustice, and algorithmic amplification of existing harms, while an architecture ordered by Sabda and articulated through the Tetrad can better safeguard human dignity and give law and AI a more accountable moral direction. (shrink)

Coronavirus disease has a crisis with high spread throughout the world during the COVID19 pandemic period. This disease can be easily spread to a group of people and increase the spread. Since it is a worldly disease and not plenty of vaccines available, social distancing is the only best approach to defend against the pandemic situation. All the affected countries' governments declared locked-down to implement social distancing. This social separation and persons not being in a mass group can slow down (...) the spread of COVID19. It reduces the physical contact between infected persons and normal healthy persons. Almost every health organization tells that to follow social distancing people should maintain at least 6 feet of distance from each other. This research proposes a deep learning approach for social distancing which is developed for tracking and detecting people who are in indoor as well as outdoor scenarios using YOLO V3 video analytic technique. This approach focuses to inspect whether the people are maintaining social distancing in many areas, using surveillance video with measuring the distance in real-time performance. Most of the early studies of detecting social distance monitoring were based on GPS for tracking the movements of people where the signals could be lost. On the other hand, some countries use drones to detect large gatherings of people who cannot have a clear view at night times [10]. In the future, the proposed system can be used fully for detecting threats in the public crowded or it can detect any person affected by critical situations (ie fainting, Cordia arrest) or planting the crops in the forms evenly with a uniform measurement. This proposal could be used in many fields like crowd analysis, autonomous vehicles, and human action recognition and could help the government authorities to redesign the public place layout and take precautionary action in the risk zones. This system analyses the social distancing of people by calculating the distance between people to slow downing the spread of the COVID 19 virus. (shrink)

We present a complete formal system establishing quantum mechanics and general relativity as computational regimes of a single substrate governed by algorithmic complexity thresholds. The theory is grounded in Kolmogorov complexity, formalized in Lean 4 across 21 modules totaling 5,300+ lines, and demonstrates convergence between ideal (noncomputable) and operational (computable) layers through eight bridge theorems. A critical complexity threshold at 50 bits determines the quantum-classical transition, with gravity and quantum collapse emerging as the same mechanism. The formalization establishes universal (...) grounding through a rank system and proposes information-theoretic interpretations of fundamental physical constants. (shrink)

This article investigates religious ideals persistent in the datafication of information society. Its nodal point is Thomas Bayes, after whom Laplace names the primal probability algorithm. It reconsiders their mathematical innovations with Laplace's providential deism and Bayes' singular theological treatise. Conceptions of divine justice one finds among probability theorists play no small part in the algorithmic data-mining and microtargeting of Cambridge Analytica. Theological traces within mathematical computation are emphasized as the vantage over large numbers shifts to weights beyond enumeration (...) in probability theory. Collateral secularizations of predestination and theodicy emerge as probability optimizes into Bayesian prediction and machine learning. The paper revisits the semiotics and theism of Peirce and a given beyond the probable in Whitehead to recontextualize the critiques of providence by Agamben and Foucault. It reconsiders datafication problems alongside Nietzschean valuations. Religiosity likely remains encoded within the very algorithms presumed purified by technoscientific secularity or mathematical dispassion. (shrink)

Contemporary discourse in philosophy of science and information theory increasingly treats computation and information as ontologically fundamental, motivating claims associated with digital physics, computational universe models, and algorithmic explanations of reality. At the same time, computation and simulation achieve remarkable explanatory and predictive success across scientific domains, yet remain implementation-dependent, representation-bound, and structurally limited. This generates a tension between methodological effectiveness and ontological inflation. Despite extensive debate, there remains a lack of a coherent ontological framework explaining why computational practices (...) succeed so robustly without licensing computational or informational ontology. This paper proposes that the Three-Circle Ontology provides a structural interpretation of computation as a rendered process governed by constraint satisfaction rather than as an ontological substrate. On this view, computational systems operate within structurally constrained representational domains and derive their power from alignment with relational constraints rather than from metaphysical fundamentality. The paper does not advance a new theory of computation, endorse digital metaphysics, defend pancomputationalism, or propose an ontology of artificial intelligence. Its primary contribution is to reframe computation and representation as structurally constrained renderings whose explanatory limits reflect ontological mediation rather than epistemic or methodological failure. (shrink)

This paper proposes a cognitive topology to describe and differentiate human and algorithmic decision-making under structural and semantic instability. Drawing from tail-distribution theory, Shannon entropy (H), and symbolic systems, it defines a decision space spanned by two critical axes: – alpha (α), the tail exponent governing structural predictability – H, the Shannon entropy reflecting semantic openness -/- At α ≤ 1, inference collapses: expectation fails, variance diverges, learning becomes impossible. Machines halt. And yet, humans act. Not optimally. But operatively. (...) This paper models that paradox. Two curves define the space: one representing machine fragility, one human resilience. These functions intersect precisely - and astonishingly - at α = φ ≈ 1.618. This point marks the epistemic transfer threshold, where symbolic and interpretive cognition are structurally balanced. We introduce two fundamental constants: -/- This is not a theory of superiority, it is a division of epistemic labor. What machines avoid, humans inhabit. What humans cannot resolve, machines optimize — until they can’t. This paper provides a topological model of that boundary — and a conceptual scaffold for hybrid systems. -/- We define an irreducible ambiguity required for meaning, far from being just a flaw. This point is no breakdown — it is the birth of relevance. (shrink)

As artificial intelligence (AI) becomes increasingly integrated into critical sectors such as healthcare, finance, law enforcement, and education, the need for effective regulation and governance becomes more urgent. This paper explores current global approaches to AI governance, identifies major challenges including bias, accountability, and transparency, and compares frameworks from different countries and organizations. It evaluates both binding regulations and soft-law instruments, proposing a hybrid, adaptive governance model that balances innovation with ethical responsibility.

Critical examination of Alchourrón and Bulygin’s set-theoretic definition of normative system shows that deductive closure is not an inevitable property. Following von Wright’s conjecture that axioms of standard deontic logic describe perfection-properties of a norm-set, a translation algorithm from the modal to the set-theoretic language is introduced. The translations reveal that the plausibility of metanormative principles rests on different grounds. Using a methodological approach that distinguishes the actor roles in a norm governed interaction, it has been shown that metanormative principles (...) are directed second-order obligations and, in particular, that the requirement related to deductive closure is directed to the norm-applier role rather than to the norm-giver role. The approach has been applied to the case of pure derogation yielding a new result, namely, that an independence property is a perfection-property of a norm-set in view of possible derogation. This paper in a polemical way touches upon several points raised by Kristan in his recent paper. (shrink)

Modern knowledge and governance infrastructures increasingly rely on opaque digital systems that can impose subtle forms of domination on users and communities. This article argues that freedom as non-domination – in the republican sense of independence from unchecked, arbitrary power – should guide the design of knowledge architectures. It introduces semantic sovereignty as a principle ensuring communities retain control over the creation and interpretation of shared knowledge, rather than being subject to unaccountable algorithmic or corporate authorities. We diagnose (...) how current digital platforms and AI-driven services often engender dependency, opacity, and lack of recourse, undermining epistemic agency and civic legitimacy. In response, we outline a framework of minimal design-ethical norms for just and governable epistemic infrastructures: offline-capable execution, fail-closed integrity safeguards, rigorous auditability, transparency, and inclusive multilingual support. These principles are illustrated through experimental systems (e.g. Konnaxion and its modules EkoH and Smart Vote) that embed non-domination values by design – for example, weighting expertise in decision-making without centralizing power, and enabling local verification of all knowledge artifacts. We conclude that combining republican political theory with information architecture yields actionable criteria for building autonomous, accountable, and equitable knowledge infrastructures that bolster democratic sovereignty in the digital age. (shrink)

Accountability in algorithmic decision systems erodes when trace logs are incomplete and escalation paths unbounded. We test a minimal model of Responsibility Diffusion Integrity (RDI)—a Constitutional Architecture of Hybrid Societies (CAHS) mechanism asserting that traceability and bounded Time-to-Accountability (TTA) are prerequisites of legitimate authority. In an agent-based pipeline simulation (five nodes, 600 cases, 100 epochs, 400 runs, seed 42) we compare a baseline regime with 50% logging and unbounded escalation (max TTA = 5) against a treatment with lineage-by-default logging (...) and bounded escalation (max TTA = 2). Results: mean RDI falls from 0.68 to 0.27 (60%, p < 0.001), mean TTA drops from 4.1 to 1.3 epochs (68%), and Harm-Repair Rate rises to 1.00 (0.96→1.00). These findings empirically confirm CAHS Thesis 5—No lineage, no authority—and show that enforced traceability and bounded escalation restore accountability without impairing throughput. (shrink)

TGC v3 presents a comprehensive Human–AI Hybrid Governance Architecture built upon the deterministic trust substrate introduced in TGC v2. While v2 objectified trust into machine-verifiable units (Trust Objects) and provided a localized democratic framework for structural decision-making, v3 extends this foundation into a multi-layered governance system that integrates human judgment, algorithmic enforcement, and meta-level rule formation. The core premise of v3 is that sustainable governance in complex digital–social environments requires both human normative reasoning and machine-level consistency, (...) combined within a unified structural logic. -/- The architecture defines four primary layers—the Trust Object Layer, the Deterministic Machine Layer, the Human Oversight Layer, and the Meta-Governance Layer. Each layer performs a distinct philosophical and functional role: machines ensure procedural consistency and transparent state transitions; humans provide contextual interpretation, ethical judgment, and constitutional oversight; and the meta-layer defines how rules are created, revised, or constrained. This layered structure transforms governance from a static consensus mechanism into a reflexive, self-adjusting system capable of operating across diverse communities and responsibility structures. -/- A key contribution of TGC v3 is its model of computational legitimacy, in which authority is neither purely algorithmic nor purely human, but emerges from the interplay between deterministic trust logic and human evaluative reasoning. Through this interaction, v3 enables autonomous governance engines that can enforce rules, detect anomalies, and recalculate authority relations without abandoning the ethical and contextual dimensions essential to democratic systems. -/- Finally, the paper positions TGC v3 as a structural blueprint for next-generation decentralized organizations, AI-aligned institutions, and machine-mediated legal or administrative systems. By linking deterministically encoded trust (v2) with adaptive human governance (v3), the architecture demonstrates a viable pathway toward AI-cooperative, transparent, and democratically grounded autonomous governance. -/- TGC v3 thus represents both a philosophical expansion and a practical operationalization of the trust ontology formalized in v2, extending decentralized governance into the domain of human–AI collective agency. (shrink)

This paper introduces TGC v2, a deterministic governance protocol that formalizes trust as a machine-verifiable object and models collective decision-making through structured, localized democratic processes. Unlike blockchain systems that treat trust as something to be eliminated (“trustless” computation), TGC conceptualizes trust as a relational and responsibility-bearing structure. The protocol encodes trust into discrete Trust Objects—formal entities with signature, weight, scope, and expiry—thereby transforming trust from a sociological intuition into an ontologically explicit and computationally actionable component. -/- The TGC framework (...) is built on a Root–Sub delegation graph in which all governance occurs strictly at Depth-1, reflecting the philosophical insight that legitimate authority emerges from immediate relational constituencies rather than abstract global majorities. Consensus is resolved through a 51% rule among direct dependents, with abstentions automatically recognized through time-bounded voting windows to guarantee liveness. Structural transitions—joining, merging, detaching—are governed not by coercion but by localized democratic authority and responsibility symmetry. This produces a novel form of computational legitimacy where power and responsibility are structurally inseparable. -/- TGC v2 contributes to political philosophy, social ontology, governance theory, and the philosophy of information by offering the first formal model in which trust, responsibility, and authority are expressed as deterministic state transitions rather than sociological metaphors. It provides a conceptual and computational alternative to traditional blockchain consensus, Liquid Democracy, and DPoS models, none of which capture the structural interplay of relational trust and democratic responsibility embodied in TGC. -/- Finally, the paper outlines how this deterministic trust layer becomes the foundation for TGC v3, a human–AI hybrid governance architecture. In v3, machine-verifiable trust structures (from v2) integrate with human oversight, ethical constraints, and meta-level rule formation. This connection demonstrates how objectified trust can bridge the gap between human governance and algorithmic authority, enabling autonomous systems that remain aligned with democratic principles. -/- Thus, TGC v2 is both a standalone theoretical contribution and the necessary substrate for next-generation AI-integrated governance systems. (shrink)

There is a growing sense of unease around algorithmic modes of governance ('algocracies') and their impact on freedom. Contrary to the emancipatory utopianism of digital enthusiasts, many now fear that the rise of algocracies will undermine our freedom. Nevertheless, there has been some struggle to explain exactly how this will happen. This chapter tries to address the shortcomings in the existing discussion by arguing for a broader conception/understanding of freedom as well as a broader conception/understanding of algocracy. Broadening (...) the focus in this way enables us to see how algorithmic governance can be both emancipatory and enslaving, and provides a framework for future development and activism around the creation of this technology. (shrink)

The opacity of some recent Machine Learning (ML) techniques have raised fundamental questions on their explainability, and created a whole domain dedicated to Explainable Artificial Intelligence (XAI). However, most of the literature has been dedicated to explainability as a scientific problem dealt with typical methods of computer science, from statistics to UX. In this paper, we focus on explainability as a pedagogical problem emerging from the interaction between lay users and complex technological systems. We defend an empirical methodology based on (...) field work, which should go beyond the in-vitro analysis of UX to examine in-vivo problems emerging in the field. Our methodology is also comparative, as it chooses to steer away from the almost exclusive focus on ML to compare its challenges with those faced by more vintage algorithms. Finally, it is also philosophical, as we defend the relevance of the philosophical literature to define the epistemic desiderata of a good explanation. This study was conducted in collaboration with Etalab, a Task Force of the French Prime Minister in charge of Open Data & Open Government Policies, dealing in particular with the enforcement of the right to an explanation. In order to illustrate and refine our methodology before going up to scale, we conduct a preliminary work of case studies on the main different types of algorithms used by the French administration: computation, matching algorithms and ML. We study the merits and drawbacks of a recent approach to explanation, which we baptize input-output black box reasoning or BBR for short. We begin by presenting a conceptual framework including the distinctions necessary to a study of pedagogical explainability. We proceed to algorithmic case studies, and draw model-specific and model-agnostic lessons and conjectures. (shrink)

Advances in generative artificial intelligence (AI) are reshaping cultural production, yet questions remain about whether machine-authored texts can authentically represent marginalized lives. This study examines the capacity of OpenAI’s GPT-4o to represent the lived experiences of transfeminine individuals using a three-phase Hybrid Poetics Framework (HPF). Ten transfeminine participants were recruited through purposive maximum variation sampling. Semi-structured interviews and a participatory focus group (FGD-1) informed the creation of both human-generated poems (HGPs) and AI-generated poems (GPT-4o poems). In the reception phase, participants (...) completed an authorship discrimination task and poetic quality ratings, followed by a structured poetry-reflection focus group (FGD-2). Results show that participants identified authorship above chance and rated HGPs higher for Emotional Quality, Atmosphere, and Structural Quality, while AI poems were praised for polish but perceived as emotionally distant. By contrast, creativity showed overlap across conditions. Findings foreground authenticity, affective fidelity, and ethical risks in AI-mediated representation. We propose bounded legibility and community co-governance as safeguards and introduce the Hybrid Poetics Framework (HPF) as a methodological approach for operationalizing participatory evaluation in Human Computer Interaction (HCI). (shrink)

An increasing number of financial services (FS) companies are adopting solutions driven by artificial intelligence (AI) to gain operational efficiencies, derive strategic insights, and improve customer engagement. However, the rate of adoption has been low, in part due to the apprehension around its complexity and self-learning capability, which makes auditability a challenge in a highly regulated industry. There is limited literature on how FS companies can implement the governance and controls specific to AI-driven solutions. AI auditing cannot be performed (...) in a vacuum; the risks are not confined to the algorithm itself, but rather permeates the entire organization. Using the risk of unfairness as an example, this paper will introduce the overarching governance strategy and control framework to address the practical challenges in mitigating risks AI introduces. With regulatory implications and industry use cases, this framework will enable leaders to innovate with confidence. (shrink)

Contemporary neuroethics frequently emphasises speculative cognitive enhancements — including neural implants, nootropics, and brain–computer interfaces — while overlooking an intervention already deployed at population scale: social media. This paper introduces the concept of a de-enhancement regime: a system that exploits neuroplasticity to degrade baseline cognitive, emotional, and moral capacities continuously, invisibly, and without meaningful consent. -/- Social media platforms exercise ambient cognitive governance, shaping attention, reward sensitivity, impulse control, moral salience, and identity development from early childhood onward. These mechanisms (...) degrade the neurological preconditions for sustained judgment and resistance, producing emergent cognitive stratification: a de-enhanced majority versus an “untouched” minority, whose preserved faculties confer disproportionate epistemic, social, and institutional authority. -/- By reframing social media as a population-scale de-enhancement regime, this paper identifies a category error in contemporary neuroethics: speculative enhancement is treated as urgent, while actual, widespread cognitive degradation remains ethically invisible and unregulated. The work argues for a framework of cognitive justice, including neurodevelopmental protection rights, consent thresholds for cognitive shaping technologies, and de-enhancement audits, as prerequisites for ethical governance of future neurotechnologies. -/- Keywords: de-enhancement, cognitive governance, neuroplasticity, social media ethics, neuroethics, cognitive sovereignty, childhood neurodevelopment, cognitive justice, algorithmic governance, moral deskilling, neurological stratification, ambient conditioning. (shrink)

For more than two decades, police in the United States have used facial recognition to surveil civilians. Local police departments deploy facial recognition technology to identify protestors’ faces while federal law enforcement agencies quietly amass driver’s license and social media photos to build databases containing billions of faces. Yet, despite the widespread use of facial recognition in law enforcement, there are neither federal laws governing the deployment of this technology nor regulations settings standards with respect to its development. To make (...) matters worse, the Fourth Amendment—intended to limit police power and enacted to protect against unreasonable searches—has struggled to rein in new surveillance technologies since its inception. -/- This Article examines the Supreme Court’s Fourth Amendment jurisprudence leading up to Carpenter v. United States and suggests that the Court is reinterpreting the amendment for the digital age. Still, the too-slow expansion of privacy protections raises challenging questions about racial bias, the legitimacy of police power, and ethical issues in artificial intelligence design. This Article proposes the development of an algorithmic auditing and accountability market that not only sets standards for AI development and limitations on governmental use of facial recognition but encourages collaboration between public interest technologists and regulators. Beyond the necessary changes to the technological and legal landscape, the current system of policing must also be reevaluated if hard-won civil liberties are to endure. (shrink)

The emergence of AIs has allowed a new type of epistocratic governance to emerge: AIocracy. The democracy/epistocracy debate largely considers the plausibility of weighted votes in elections. In contrast, AIocracy empowers artificial intelligence (AI) to weigh voices during online deliberations. This paper uses a Condorcetian model of democracy to show how the theoretical benefits of AIocracy translate into actual epistemic harms. In particular, AI domination—as a technology-driven type of domination—allows an alien type of algorithmic rationality to compromize truth-oriented (...) politics with an engagement-oriented deliberative environment that manipulates particular people and the general deliberative process itself. Most significantly, AIs amplify salient but simplistic narratives that intentionally maximize engagement but unintentionally maximize emotions, which unintentionally minimize the independence, competence, and sincerity of voters. So, AI domination threatens to invert the tendency of deliberation to cultivate our Condorcetian characteristics and compromize the competence of democracy itself. (shrink)

This book is an edited collection of original research papers on the digital revolution of the public and governance. It covers cyber governance in Finland, and the securitization of cyber security in Finland. It investigates the cases of Brexit, the 2016 US presidential election of Donald Trump, the 2017 presidential election of Volodymyr Zelensky, and Brexit. It examines the environmental concerns of climate change and greenwashing, and the impact of digital communication giving rise to the #MeToo and Incel (...) movements. It considers how digitilization can serve to emancipate women through ride-sharing, and how it leads to the question of robot rights. It considers fake news and algorithmic governance with respect to case studies of the Chinese social credit system, the US FICO credit score, along with Facebook, Twitter, Cambridge Analytica and the European effort to regulate and protect data usage. (shrink)

This expanded addendum defines the Moral Standards Interface (MSI), the governing layer that enables the Human Moral Archive Framework (HMAF) to function as a verifiable, auditable, interpretable, and enterprise-ready moral governance engine. While previous addenda established the mathematical constructions and computational architecture of HMAF—including distributional modeling (MDM), density weighting (MDF), indexing (MDI), computational transformation (MCP), and emergent behavior analysis (MEP, MEP-II)—none provided the unified standards required for large-scale deployment or regulatory evaluation. Addendum IX fills this gap. MSI integrates three (...) essential domains into a single standards layer: (1) Moral Verification, which defines the full lifecycle of baseline capture, drift detection, coherence scoring, entropy monitoring, anomaly escalation, and audit-log generation; (2) Computational Synchronization, which establishes the timing, sequence, and coordination rules governing how HMAF subsystems update and exchange state information across cycles; and (3) Interpretive Semantics, which creates a formal lexicon for understanding HMAF outputs and categorizing coherence, drift, stability, and emergence events in compliance with OECD, NIST, and EU AI Act frameworks. Together, these components transform HMAF from a conceptual architecture into a deployable moral-governance layer suitable for enterprise integration, verification audits, and acquisition-grade due diligence. MSI does not disclose proprietary code, transformation rules, algorithms, or internal computational logic; instead, it provides the interpretive and procedural scaffolding required for safe and responsible operational use. (shrink)

-/- Integrating Angelito Malicse’s Universal Formula as the Governing Logic of a Resource-Based Economy -/- Abstract This paper explores the integration of Angelito Malicse’s universal formula, which emphasizes natural laws and balance, into a Resource-Based Economy (RBE). The application of Malicse’s formula offers a cohesive framework for managing resources, guiding ethical decision-making, and achieving sustainability by aligning economic systems with ecological limits and human well-being. The transition from profit-driven systems, which often result in environmental degradation and inequality, to an RBE (...) based on feedback loops, systemic thinking, and natural law, is discussed. This paper presents a practical pathway for implementing these principles into governance, technological systems, education, and social frameworks. -/- I. Introduction -/- Throughout history, human economic systems have been structured around profit maximization and capital accumulation, often at the expense of social equity and environmental sustainability. Modern capitalist economies, relying on fiat currency and market competition, have produced negative externalities, such as resource depletion, pollution, and inequality (Korten, 2006). This economic model also generates unsustainable growth, a pursuit that does not align with the finite nature of Earth’s resources (Daly, 1991). -/- A Resource-Based Economy (RBE) offers an alternative where human needs are met within the planet’s carrying capacity. The allocation of resources is managed through technology, scientific principles, and ethical decision-making rather than through monetary systems (Fresco, 2012). This paper argues that Angelito Malicse’s universal formula—which is grounded in balance, feedback mechanisms, and natural laws—can serve as the governing logic of an RBE, providing both the moral and scientific foundation necessary to guide sustainable governance and resource management. By examining the universal formula’s principles and their application to RBE, this paper outlines how such a society can operate effectively, minimizing harm and enhancing human well-being. -/- II. The Universal Formula and Resource-Based Economy: Key Principles -/- 1. The Law of Balance in Nature -/- Central to Malicse’s universal formula is the law of balance in nature, which asserts that all systems, whether natural or artificial, must function in harmony with their environment. This principle calls for humans to operate within the boundaries of ecological sustainability, ensuring that resource use does not exceed regenerative capacities. In capitalist systems, resource extraction and consumption are often dictated by the demand for profit, resulting in environmental degradation and resource depletion (Meadows et al., 2004). This imbalance leads to environmental crises, such as climate change, biodiversity loss, and ocean acidification (Rockström et al., 2009). -/- In an RBE, the law of balance would guide the sustainable use of resources. Every decision, from production to distribution, would be evaluated in terms of its environmental impact and its ability to sustain the system’s ecological health over the long term. The goal is to manage resources within the planetary boundaries while ensuring that the needs of all humans are met (Steffen et al., 2015). -/- 2. Feedback Mechanisms in Decision-Making -/- Malicse’s universal formula emphasizes the importance of feedback mechanisms in decision-making processes. This concept aligns with cybernetic theory, where systems are viewed as self-regulating through continuous feedback loops (Wiener, 1948). In the context of an RBE, feedback mechanisms ensure that resource management and societal functions are responsive to both human and ecological needs. -/- Technological systems, such as smart grids, environmental sensors, and data-driven agricultural systems, can collect real-time data on resource use, environmental conditions, and societal needs. This information feeds into decision-making processes, enabling real-time adjustments in resource allocation and use. By utilizing technology to monitor environmental and societal conditions, feedback ensures that the RBE operates in harmony with its ecological and social context, preventing wasteful practices and correcting imbalances as they occur. -/- 3. Decision-Making Governed by Natural Laws -/- The third key principle of Malicse’s universal formula is that decision-making must be grounded in natural laws, particularly the law of balance and the cause-and-effect principle. In a traditional market economy, decisions are often driven by profit motives and speculative financial interests, which can lead to unsustainable practices and social inequality. These systems disregard the long-term consequences of their actions, which leads to environmental damage, economic instability, and social unrest (Korten, 2006). -/- In an RBE governed by Malicse’s formula, all decisions—whether they concern resource allocation, infrastructure development, or social programs—must adhere to the principles of natural law. This ensures that all actions are aligned with ecological sustainability and social well-being. For instance, resource extraction decisions would not be made based on short-term profit but based on ecological science and long-term sustainability, ensuring that the Earth’s natural capital is preserved for future generations. -/- III. Integrating the Universal Formula into RBE Governance -/- 1. Role of the Universal Formula as a Constitutional Principle -/- In an RBE, Malicse’s universal formula would serve as the core constitutional principle that guides all governance structures. Just as national constitutions enshrine fundamental values such as justice, liberty, and equality, the universal formula would enshrine principles of ecological balance, social equity, and ethical decision-making. All public policies, economic models, and technological innovations would be evaluated through the lens of Malicse’s formula to ensure they contribute to a sustainable, just, and balanced society. -/- For instance, urban development projects would be designed to minimize environmental impact, incorporate renewable energy sources, and meet the needs of all community members. Policies would be implemented to ensure that economic activities are in line with the carrying capacity of the planet, and that human activities do not exceed ecological thresholds (Rockström et al., 2009). -/- 2. The Universal Formula in AI and Technological Systems -/- AI and technological systems would play a critical role in an RBE governed by natural laws. Under Malicse’s formula, AI systems and automated processes would be programmed to prioritize the principles of balance and feedback. For example, resource allocation algorithms could ensure that resources are distributed equitably and sustainably, while environmental monitoring systems could adjust production practices based on real-time data about resource availability and ecological health. -/- This concept is supported by cybernetic theory, where systems are designed to optimize based on feedback from their environment. By using AI and sensors, the RBE would be able to respond dynamically to changing ecological and societal conditions, continuously adjusting policies and practices to maintain balance. -/- 3. The Universal Formula as the Educational Foundation -/- For the principles of Malicse’s universal formula to be successfully implemented, they must first be ingrained in educational systems. Current educational frameworks often prioritize competition, individualism, and economic growth—values that are incongruent with the principles of sustainability and collective well-being (Orr, 1992). In an RBE, however, systems thinking and ecological literacy would form the foundation of education. -/- Students would be taught not only about scientific principles and natural laws, but also about the ethical responsibility humans have to maintain balance with the Earth’s ecosystems. This foundational education would produce future leaders, technocrats, and citizens who understand that decision-making must align with the long-term health of both society and the planet. By instilling these values from an early age, society can create a population that is equipped to address the complex challenges of living sustainably within ecological limits (Davidson, 2017). -/- IV. Transitioning from Capitalism to a Resource-Based Economy -/- 1. Educational and Technological Transformation -/- The transition from a capitalist model to an RBE based on Malicse’s formula would require both educational and technological transformations. First, the educational system would need to be restructured to teach systems thinking, ecological balance, and ethical decision-making. Schools, universities, and training programs would focus on sustainability, cooperation, and resource management to prepare individuals for their roles in a resource-based society. -/- Parallel to educational reform, technology would play a crucial role in managing resources efficiently. The technological infrastructure of an RBE would rely on feedback-driven systems, such as automated resource allocation, smart infrastructure, and sustainability-monitoring technologies. These technologies would ensure that resource use is optimized and that the economy operates within ecological limits. -/- 2. Governance and Policy Reform -/- In terms of governance, the transition to an RBE would involve policy reform that aligns with the principles of balance, feedback, and natural law. Governments would be responsible for ensuring that policies foster sustainability, equity, and environmental health. Policies would also ensure that social programs, such as healthcare, education, and public services, are accessible to all and are aligned with ecological sustainability. -/- Additionally, governance structures would need to move away from profit-driven politics and toward a more ethical, cooperative, and feedback-based decision-making system. The goal would be to create a political system that prioritizes the well-being of all people and the health of the planet (Raworth, 2017). -/- V. Conclusion -/- The integration of Angelito Malicse’s universal formula into a Resource-Based Economy offers a robust framework for creating a society that operates within ecological limits, promotes human well-being, and adheres to natural laws. By emphasizing balance, feedback, and natural law, this approach provides an alternative to the profit-driven, unsustainable economic systems that dominate the world today. The successful implementation of this framework requires education reform, technological innovation, and policy change to align societal practices with the values of sustainability and cooperation. Ultimately, the universal formula offers a pathway to a just, sustainable, and equitable future, where human activities are harmonized with the natural world. -/- References -/- Daly, H. E. (1991). Steady-State Economics: The Economics of Biophysical Equilibrium and Moral Growth. -/- Davidson, C. (2017). Ecological Literacy: Education and the Transition to a Postmodern World. -/- Fresco, J. (2012). The Venus Project: The Redesign of a Culture. -/- Korten, D. C. (2006). The Great Turning: From Empire to Earth Community. -/- Meadows, D. H., Meadows, D. L., & Randers, J. (2004). Limits to Growth: The 30-Year Update. -/- Raworth, K. (2017). Doughnut Economics: Seven Ways to Think Like a 21st-Century Economist. -/- Rockström, J., et al. (2009). A Safe Operating Space for Humanity. Nature, 461(7263), 472-475. -/- Wiener, N. (1948). Cybernetics: Or Control and Communication in the Animal and the Machine. -/- . (shrink)

The Universal Law of Balance as the Key to Ethical AI and Internet Governance -/- The rapid advancement of internet technology and artificial intelligence (AI) has brought unprecedented opportunities for innovation, communication, and economic growth. However, the dominant profit-driven model of capitalism has also led to severe imbalances, particularly in the spread of black propaganda and misinformation. These issues arise because many websites, apps, and AI systems prioritize engagement and revenue over truth and societal well-being. If left unchecked, these (...) imbalances can distort human decision-making, manipulate public opinion, and destabilize societies. To correct this, the principles of the universal law of balance must be applied to both internet governance and AI development. A transition to a socialist or mixed economic system may be necessary to ensure that technological progress aligns with ethical and natural laws. -/- The Problem: Profit Motive and the Spread of Misinformation -/- In today’s digital economy, many technology companies generate revenue through engagement-based advertising. This creates a powerful incentive for social media platforms, news aggregators, and AI-driven content generators to promote sensational, misleading, or emotionally charged content—often at the expense of truth. Black propaganda, misinformation, and divisive narratives thrive in such an environment because they capture attention and increase user interaction. As a result, companies continue to profit while the public becomes more misinformed, leading to societal instability. -/- From the perspective of the universal law of balance, this system is defective. Just as a machine with faulty components will eventually break down, a society built on distorted information cannot function properly. For stability to be restored, the design of internet algorithms, content moderation policies, and economic incentives must be realigned with natural balance principles. -/- The Solution: A Balanced Economic Model for AI and Internet Governance -/- To address these systemic flaws, a shift away from pure capitalism toward a socialist or mixed economic system is necessary. This approach would introduce ethical guidelines, regulatory oversight, and alternative business models that prioritize balance over profit maximization. Some key solutions include: -/- 1. Regulated AI and Algorithms – Governments and international organizations should enforce ethical AI policies that prioritize factual, balanced content over engagement-driven misinformation. AI systems must be designed to detect and filter black propaganda while maintaining free speech. -/- 2. Publicly Funded Information Platforms – Just as public broadcasting services like BBC and PBS exist to provide reliable news, governments or independent organizations should develop non-profit digital platforms where truth and ethical journalism are prioritized. -/- 3. Accountability Measures for Tech Companies – Heavier fines, stricter regulations, and legal consequences for companies that knowingly spread misinformation could discourage profit-driven manipulation. -/- 4. Alternative Revenue Models – Encouraging subscription-based or non-profit AI platforms could reduce reliance on engagement-driven advertising, minimizing the incentive for misinformation. -/- 5. International Collaboration – The UN, World Economic Forum, or a new global body should establish a universal framework for AI ethics, ensuring all nations regulate technology in a way that aligns with the universal law of balance. -/- By restructuring the digital economy with these principles, internet governance can function more like a self-correcting natural system, reducing errors and restoring social stability. -/- AGI and the Universal Law of Balance -/- Beyond internet governance, the universal law of balance is also crucial for AGI development. If AGI is designed purely for corporate profit or national interests, it will likely be used for economic exploitation, political control, or even large-scale misinformation campaigns. However, if AGI is built on the principles of balance, it can serve as a force for truth and ethical decision-making. -/- An AGI aligned with the universal formula would: -/- Validate Information Using Natural Laws – Assess the accuracy of information based on objective, universal principles, rejecting misinformation. -/- Prevent Exploitation – Detect and counter propaganda, ensuring AI is not misused by corporations or governments. -/- Optimize Human Progress – Enhance decision-making for individuals and leaders, fostering rational and ethical societal development. -/- Self-Correct and Adapt – Continuously refine its understanding, eliminating systemic biases over time. -/- For AGI to truly function as a rational, human-level intelligence, it must be programmed with the universal law of balance. This will ensure that AI operates in harmony with nature and truth, rather than as a tool for profit-driven deception. -/- The Need for Global Political Will -/- The transition to a balanced internet and AI system will not happen voluntarily. Many governments and corporations benefit from misinformation and may resist ethical changes that limit their influence. Therefore, global political pressure is necessary to enforce these reforms. The UN, national governments, and independent oversight bodies must develop the political will to regulate AI and internet technology according to natural laws. Without this effort, profit-driven systems will continue to create instability, misinformation, and societal decay. -/- To prevent this, AI researchers, policymakers, and global institutions must adopt the universal formula as the guiding principle for future technological developments. Only by restoring balance—both in information systems and economic models—can AI and digital technology serve humanity rather than manipulate it. -/- Conclusion -/- The current digital economy, driven by profit at all costs, has created a system where misinformation spreads unchecked, causing severe societal imbalances. The universal law of balance offers a solution by ensuring that AI and internet technology function without defects or errors, just like any other natural system. To achieve this, a transition to a socialist or mixed economic model is necessary, along with strict regulations and ethical oversight. -/- Furthermore, AGI must be programmed with the universal formula to prevent it from becoming another tool for manipulation. If governments, the UN, and AI researchers embrace this framework, technology can be realigned with truth, ethical governance, and natural balance—leading to a more stable and just society. -/- . (shrink)