The Metaverse represents a paradigm shift from two-dimensional internet interaction to immersive, persistent, three-dimensional environments. As this ecosystem evolves, the attack surface expands exponentially, introducing novel vulnerabilities that traditional HTTPS and TLS protocols cannot adequately address. This chapter explores the future of secure communication within the Metaverse, moving beyond standard data encryption to address the protection of biometric data, haptic feedback integrity, and avatar identity. We will analyze the unique challenges posed by the convergence of Virtual Reality (VR), Augmented Reality (AR), and the Internet of Things (IoT). The chapter will propose a multi-layered security framework integrating Quantum-Resistant Cryptography, Zero-Knowledge Proofs (ZKPs) for identity verification without exposure, and AI-driven behavioral analysis to detect “Man-in-the-Avatar” attacks. Finally, we will discuss the regulatory and ethical implications of surveillance in a world where user movements and gaze are constantly tracked.
Nagwan AlQershi, T. Ramayah, Mohammed Alzoraiki, Gamal Abdualmajed Ali · 5 authors
Purpose This study examines how agentic leadership, conscious unbossing and AI-augmented ethical decision-making (AI-EDM) jointly shape crisis-resilient leadership among emerging global leaders. Addressing a key gap in leadership scholarship, the study conceptualizes artificial intelligence (AI) not merely as a technical tool but as a governance architecture that structures decision authority, accountability and ethical action under conditions of uncertainty. Design/methodology/approach A mixed-method design was employed. Survey data were collected from 423 early-career professionals across 12 countries. Partial least squares structural equation modeling using SmartPLS (version 4.1.1.6) tested a moderated mediation model in which agentic leadership predicts crisis-resilient leadership effectiveness (CR-LE) through AI-EDM, with conscious unbossing and crisis exposure as moderating conditions. Qualitative interviews with 24 Gen Z leaders operating in crisis-prone contexts were used to contextualize and interpret the quantitative findings. Findings Agentic leadership significantly predicted CR-LE, with this relationship mediated by AI-EDM. The indirect pathway was stronger among leaders exhibiting higher levels of conscious unbossing, indicating that leadership agency is conditioned by decentralized and algorithmically governed authority structures. Crisis exposure intensified the salience of ethical AI engagement, highlighting the heightened accountability pressures leaders face under volatility. Qualitative findings reinforced these results, illustrating how leaders navigate responsibility for outcomes shaped by AI-mediated decision systems they do not fully control. Research limitations/implications The cross-sectional design limits causal inference, and scalar measurement invariance was not tested. Future longitudinal and cross-contextual research is needed to examine how leadership agency and accountability operate across different institutional and regulatory environments. Practical implications The findings underscore the need to reconceptualize leadership development and organizational governance for AI-mediated contexts. Rather than focusing solely on ethical capability enhancement, organizations and policymakers must equip leaders to operate under algorithmic constraints by clarifying decision rights, escalation pathways and contestability mechanisms. Originality/value This study advances leadership theory by identifying accountability displacement under AI-mediated governance as a central mechanism reshaping leadership agency in crisis contexts. It reframes leadership not as autonomous discretion or technological mastery but as accountable action under constraint within socio-technical governance systems.
Abstract. The accelerating development of Artificial General Intelligence (AGI) raises a fundamental question that Stuart Russell articulated with precision: how do we ensure that increasingly powerful AI systems remain aligned with human values? This paper argues that the answer lies not in constraining AGI, but in building parallel infrastructure that preserves human sovereign will-expression. BeTrueCore Modular System — built on Web3 Intuitive Symmetry Methodology (Web3-ISM) v1.2 — proposes a sociotechnical architecture where AI acts as notary, not judge. Drawing on Gödel's incompleteness theorems, wabi-sabi philosophy, Bayesian evolution, and cryptographic governance primitives (ZK-SNARKs, MACI, Lit Protocol), the system transforms collective human intuition into mathematically verifiable decisions. We argue that BeTrueCore does not compete with AGI — it provides the ethical infrastructure upon which AGI-era governance must be built. Keywords: AGI alignment, gorilla problem, assistive gaming, collective intelligence, digital sovereignty, cryptographic Voting, Weight Unit, voice of silence, self-awareness game, AI as a notary, participatory democracy.
Decentralized Autonomous Organizations (DAOs) enable blockchain-based collective governance, yet existing studies often evaluate DAO governance through isolated mechanisms, particularly voting systems. This narrow view does not sufficiently explain recurring problems such as governance capture, weak accountability, inadequate safeguards, and inefficient resource allocation. This paper proposes a Layered Governance Coverage Model that conceptualizes DAO governance as a system of seven interdependent institutional functions spanning participation, agenda formation, collective choice, safeguards, execution, incentives, and meta-governance. The model uses a four-level strength scale to assess not only whether governance functions are present, but also how strongly they are institutionalized. It is empirically applied to thirty-seven active DAOs through evidence-based coding of publicly available governance artifacts. The results show that governance breadth does not necessarily imply governance maturity: collective choice and execution mechanisms are more developed than accountability, safeguards, and meta-governance. Beyond DAO-native settings, the paper positions governance maturity as a trust and resilience regime for blockchain-based IoT and AI infrastructures, where governance affects security, reliability, data integrity, and risk oversight. The paper discusses AI-enabled governance analytics as a support mechanism for monitoring governance activity, detecting anomalies, and improving governance observability. The proposed framework contributes a structured approach for evaluating and designing resilient governance architectures in DAOs and blockchain-based IoT/AI systems.
The rapid rise of Web3 technologies, representing the third phase of the internet, is creating a decentralized ecosystem that grants users ownership and control. Concurrently, metaverse platforms supported by virtual and augmented reality technologies signify the emergence of persistent, shared digital universes where users interact through digital avatars. These developments necessitate significant changes in consumer rights and protections within digital marketing processes. While decentralized structures and blockchain-based systems enhance user data sovereignty, they also require the development of novel governance and financial frameworks. In this context, existing legal instruments—particularly the European Union’s Digital Services Act, the U.S. Federal Trade Commission guidelines, and the OECD Principles on Digital Economy—are insufficient to address the technological complexities and dynamic evolution of Web3 and metaverse ecosystems. Notable regulatory gaps persist in key areas, including data security, informed user consent, algorithmic transparency, and digital identity governance. Moreover, the marketing of blockchain-based financial instruments such as Decentralized Autonomous Organizations and Non-Fungible Tokens introduces new vectors of consumer risk and legal ambiguity, exacerbating market volatility. The opacity of algorithm-driven marketing and the potential for covert manipulation in AI-powered personalization further erode consumer trust and undermine market integrity. To ensure robust consumer protection in the digital marketing landscape, legal and regulatory frameworks must align with ongoing technological innovation. This includes mandatory implementation of algorithmic explainability standards, establishment of transparent and accountable governance mechanisms for DAOs, and development of enforceable contractual norms and minimum information disclosure requirements in NFT transactions. Furthermore, comprehensive digital literacy initiatives and consumer awareness programs are essential to mitigate emerging risks while optimizing the inclusive potential of Web3 technologies. These policy measures are crucial to safeguarding consumer rights and fostering sustainable trust within the evolving digital marketing ecosystem through 2025 and beyond.
The Absolute Smart Contract (ASC) presents a universal conceptual framework that unifies the spiritual, natural, and scientific dimensions of existence under one governing intelligence. It views reality — from atomic order to human morality — as operating within intrinsic laws of balance, reciprocity, and consequence. Whether expressed as divine will, natural order, or logical computation, each represents the same intelligent structure sustaining creation. The ASC is not a religion or ideology; it is a neutral interpretive model that reconciles seemingly divided worldviews through recognition of one absolute principle — the self-enforcing intelligence of existence itself. ASC further proposes a comparative framework through which religious, ideological, philosophical, and secular systems may be examined according to their stated principles, methods, and observable outcomes. In this sense, ASC functions less as a doctrine and more as an observational tool intended to promote awareness, reflection, and informed judgment.
Version 2.4.0 supersedes v2.3.0 (DOI: 10.5281/zenodo.20355497) and is the sixth paper in the immo.quick Core technical series (10.5281/zenodo.19634279 → 19799660 → 19969948 → 20078326 → 20355497 → this paper). Overview This paper presents the complete institutional specification of immo.quick Core — a nine-layer deterministic compliance enforcement infrastructure operating across 47 jurisdictions. It is not a paper about technology. It is a paper about institutional legitimacy — about what it means, in a world of deterministic machines, for an institution to prove that it acted correctly. Every previous compliance document in history has answered the question: "Did we follow the process?" This paper answers a different question: "Can we prove, with mathematical certainty, that no impermissible movement produced a consequence — and that no unknown party could have caused one?" The answer is yes. The architecture enforces it. The enforcement is not optional. What v2.4.0 Adds to v2.3.0 v2.3.0 established the complete epistemological foundation, the nine-layer architecture, 15 jurisdictions, complete sector analysis, geopolitical dimensions, and the economic case. v2.4.0 adds four structural elements not present in v2.3.0: Element 1 — The Nine Gamechangers: The first systematic documentation of the capability advances that place immo.quick Core in a categorically different strategic position. These are not product features. They are architectural consequences of the nine-layer system — capabilities that emerge from the architecture and could not exist without it: EPA Offline-First Verification (SSL for compliance decisions), Bi-Temporal Legal State Replay (compliance time machine), Cross-Institution Proof Network (SWIFT for compliance verdicts), Regulatory DNA Sequencing (live law tracking to zero-downtime deploy), Intraday Settlement Finality (T+0 in under 2 seconds), Legal Pathway Optimizer (optimal jurisdiction in 9ms), Machine Law Constitution (immutable rule foundation on Ethereum and IPFS), Compliance Credit Score (compliance as a balance sheet asset), and Post-CMOS Governance Readiness (investor track — strategic roadmap signal). Element 2 — Law as Code / German Federal Government Initiative: The Bundesregierung's Digitalcheck program and the formal Law-as-Code initiative (2023–2026) represent the first sovereign government mandate for machine-readable law. immo.quick Core's Machine Law Engine is the only production implementation of this paradigm at institutional scale. This is not coincidence. It is architectural convergence. Element 3 — White House National Cybersecurity Strategy (2023) and EO 14028: The US Executive Order on Improving the Nation's Cybersecurity and the National Cybersecurity Strategy mandate zero-trust architecture, post-quantum cryptography migration, and SBOM requirements for critical infrastructure. immo.quick Core satisfies all three mandates simultaneously — by architectural construction, not by configuration. Element 4 — The Legacy Integration Protocol: Precisely how immo.quick Core connects to, validates, wraps, and structurally elevates existing compliance infrastructure without requiring system replacement. The anti-rip-and-replace architecture. Architecture Summary The nine-layer enforcement system comprises: Layer 0 (DEPE — Deterministic Execution Proof Engine, 49ms total from proposal to permanent proof), Layer 1 (PAS — Prior Admissibility Space, closed-world assumption with five mandatory conjunctive conditions), Layer 2 (BTL — Bi-Temporal Ledger, BFT quorum n=9 f=3 q=7, WORM architecture), Layer 3 (EAP — Exogenous Anchor Protocol, hardware-attested dual-channel measurement, 28ms maximum heartbeat gap), Layer 4 (SOTB — Sensor/Oracle Trust Bridge), Layer 5 (MLE — Machine Law Engine, 7-stage compilation pipeline), Layer 6 (ZKP — Zero-Knowledge Proof subsystem, Groth16/PLONK/Bulletproofs), Layer 7 (PQC — Post-Quantum Cryptography, CRYSTALS-Kyber-1024/Dilithium-3/SPHINCS+, NIST FIPS 203/204/205), Layer 8 (GLD — Governance Logic Divergence engine, maker-checker independence quantification). Document Structure Part I — The Complete Problem Statement. Part II — The Nine-Layer Architecture. Part III — The Nine Gamechangers (v2.4.0 new). Part IV — Law as Code: The German Federal Government Initiative (v2.4.0 new). Part V — The White House Cybersecurity Strategy and EO 14028 (v2.4.0 new). Part VI — Complete Legal and Jurisdictional Grounding (47 jurisdictions). Part VII — What immo.quick Core Does to Existing Systems: The Legacy Integration Protocol (v2.4.0 new). Part VIII — The Complete Platform: Every Module. Part IX — Complete Sector Analysis (Banking, Insurance, Real Estate, Government, Cloud). Part X — The Geopolitical Dimension. Part XI — The Economic Case: Monopoly, Moat, FOMO, EBITDA. Part XII — The Falsifiability Standard. Conclusion — For the Permanent Record. Key Claims Established The Boundary-Behavior Gap — the space between process documentation and governance proof — is closed by mathematical construction for the first time. The Past Irreversibility Principle: every transaction processed without immo.quick Core produces a compliance history that is permanently unrecoverable. The Falsifiability Standard: all claims in this document are falsifiable by counter-proof. No counter-proof has been produced. None is expected. Historical Compliance Failures Addressed Wirecard AG (2020, €1.9B), Libor manipulation (2012, $9B+ fines), UBS rogue trader (2011, $2.3B), Cum-Ex dividend stripping (ongoing, €55B+ EU-wide), 1MDB (2015, $4.5B), Danske Bank AML (2018, €200B flow), Credit Suisse/Archegos (2021, $5.5B). immo.quick Core produces a PAS BLOCK with DPA on every one of these at T=0 — not after the fact, not during audit, at the moment of formation. Version Series 10.5281/zenodo.19634279 → 19799660 → 19969948 → 20078326 → 20355497 → 20562464 (this paper) Related Work Economics of Deterministic Compliance Infrastructure: DOI 10.5281/zenodo.20229204. immo.quick Serverless Edition v1.1.0: DOI pending.
DAOs have introduced a new paradigm in corporate governance, where decisions are devolved from hierarchies to autonomous code-based protocols. However, such a change has created an essential question of accountability gap in the legal and ethical responsibilities of individuals who develop and deploy such systems. This paper examines the two-fold problems of defining algorithmic fiduciaries and defining liability in developers when it comes to autonomous smart governance. In this paper, using a mix of law theory and empirical technical evidence, the author discusses the practicability of the traditional fiduciary duties, specifically, the Duty of Care and the Duty of Loyalty, as reliably specified in deterministic smart contract specifications. The article makes use of actual data, such as DeepDAO to gauge governance metrics and SCRUBD to assess contract vulnerabilities, in order to discuss the difference between Code is Law and systemic accountability. Findings show that the concentration of voting power and the existence of avoidable code vulnerabilities are reasons to shift to a professional standard of blockchain developers. The results indicate that the greater the algorithms' role in making decisions about material financial resources, the more it need to be mandated as functional fiduciaries. The study concludes with the suggestion of a hybrid accountability framework with developer safe harbors of audited code and the introduction of on-chain indemnity pools. In conclusion, this paper will support the thesis that in order to become mainstream, the delegation of responsibility needs to be enshrined in the design of decentralized governance, and technological autonomy will not lead to legal immunity.
Recent innovation theories on economics remain largely grounded in assumptions of hierarchical firms and closed organizational boundaries, offering limited insight into how innovation unfolds within decentralized, digitally native organizations. Decentralized Autonomous Organizations (DAOs) represent an emerging form of innovation ecosystem characterized by blockchain-based transparency, open participation, and token-driven governance, in which sustainability can be embedded directly into organizational design. This study compares two standards, ERC-8004 and Google A2A, who address the same agent interoperability question, while the former is governed by DAO and the latter by corporation consortium. They are examined through an LLM-powered comparative pipeline for large-scale governance discourse analysis, integrating automated annotation, neural topic modeling, and multi-layer network analysis to study socio-technical power structures. The study provides evidence-based insights for scholars, policymakers, and designers seeking to align innovation, technological governance, and sustainability in future organizational forms.
Frontier AI governance frameworks increasingly use cumulative training compute as the primary criterion for designating high-impact models, but enforcement rests on self-reporting because no technical verification primitive for training exists. Any future international agreement on frontier AI faces the same problem at higher stakes: coordinated regulation of technologies with significant externalities has historically rested on technical verification, without which agreements are declaratory. Recent governance analyses judge zero-knowledge proofs a promising candidate but currently impractical at frontier scale [26, 4]. We argue the impracticality is paradigm-bound rather than fundamental, and propose a verification architecture for frontier dense pre-training combining a pre-committed training specification, inter-node network observations, and on-the-fly Merkle commitments of intermediate computation, verified through a zero-knowledge Virtual Machine (zkVM) with native BF16/FP32 precompiles. The proof checks the actual floating-point computation the GPU performed rather than a fixed-point approximation, and preserves model-architecture confidentiality through a private training specification. The protocol produces three proof types: a genesis proof at initialisation, in-training step proofs across the run, and ex-ante attestations enforcing policy-relevant claims as running invariants, turning the training record into a governance-enforceable artefact. We estimate a deployable proof of concept within approximately 36 months at single-digit-percent training-side overhead, against a six-to-ten-year cycle for verification-grade custom silicon. Thirteen open research and engineering problems are catalogued as a research agenda for external contribution
This article examines human-artificial intelligence (AI) teaming in Ukrainian combat operations from 2022 to 2025, exploring the integration of AI systems with human decision-making in military contexts and how crisis-driven innovation can lead to human-AI teaming. The research addresses three questions: the effectiveness of human-AI teams compared with human-only or fully autonomous systems; the effect of organizational structures on the sustainability of AI integration; and strategic implications for the development of the doctrine and international security governance in the case of the North Atlantic Treaty Organization (NATO). The methodology uses mixed-method comparative case study analysis of three different Ukrainian systems: the Geographic Information System (GIS) Arta geospatial intelligence platform, the reconnaissance-strike unmanned aerial vehicle complex, and volunteer-supported decentralized targeting networks. Data collection was a combination of technical reports, operational battlefield metrics, and the standards of the NATO doctrines. Findings show that Ukrainian human-AI systems show good tactical performance in permissive electromagnetic environments with response times of 30 to 45 seconds and targeting accuracy of two meters but have significant vulnerabilities to electronic warfare—31% mission failure rates. Volunteer networks are highly resilient and have slower decision cycles. The research adds to strategic security, deterrence theory, military innovation theory, and organizational theory through the identification of mechanisms by which human-AI systems affect the stability of deterrence and offers recommendations for the development of the doctrine and international governance of AI for NATO.
Chapter 6 explains how Citizenship by Investment (CBI) programmes can operationalise an AI-driven due diligence (DD) system while remaining compliant with data protection, cybersecurity and emerging AI governance standards. It begins by outlining the structural weaknesses of current CBI know your customer (KYC) and DD practices—high cost, duplication across jurisdictions, repetitive documentation burdens and legacy data-security risks—and argues for a minimum DD standard that improves efficiency without weakening oversight or privacy protections. The chapter thereafter proposes a hybrid technical architecture combining AI with blockchain-enabled tools. It cautions against storing personal data on-chain due to General Data Protection Regulation constraints (especially the right to erasure) and instead supports an off-chain storage model anchored by on-chain hashes for verification, auditability and traceability. Smart contracts are presented as a mechanism to automate requests, validations and multilateral reuse of verified KYC outputs across Citizenship by Investment Units, reducing redundant screening while strengthening integrity through immutable audit trails. The chapter also addresses blockchain sustainability concerns by contrasting Proof of Work with Proof of Stake. Finally, the chapter operationalises AI governance through impact assessment and conformity assessment models aligned with the EU Artificial Intelligence Act. It proposes an Organisation of Eastern Caribbean States-oriented institutional framework involving a regional AI board, national supervisory authorities and independent conformity assessment bodies to ensure accountability, human oversight, monitoring and reporting across the AI lifecycle.
The relationship between ethics and economics in the context of technological growth is examined in this chapter. Data privacy in the digital economy, cryptocurrencies, decentralized finance (DeFi), and artificial intelligence (AI) are its three key areas of concentration. This chapter examines the influence of ethical issues on all these factors. It highlights the necessity for ethical governance of AI for both commercial and personal use. The chapter makes the case that, in an increasingly digital world, equitable and sustainable economic systems can only be achieved by proactive legislation, inclusive design, and privacy-aware business models.
Ethics and Social Impacts of AI
Interdisciplinary Studies: Technology, Society, and Humanities
Operations and supply chains have witnessed spectacular transformations through Industry 4.0 and Industry 5.0. Is the next industrial revolution – Industry 6.0 – unfolding in the context of artificial intelligence (AI) and human-AI collaboration? And, possibly, even Industry 7.0 and superintelligence (SI) are just around the corner? In this paper, we conceptualize the transition toward Industry 6.0 as the Ecosystem Age that builds upon technologies developed in Industry 4.0 and viability-centric socio-ecological principles proposed in Industry 5.0, emerging into a cyber-socio-technical-ecological industrial revolution. We create a taxonomy of industrial revolutions based on the types of work that have been replaced/transformed by machines over time, and utilize it to delineate Industry 6.0 and forecast Industry 7.0 framed in technology (e.g., generative AI, agentic AI, edge AI, and humanoid robots), organization (i.e., decentralized, autonomous, agentic-driven planning and control), and modelling (OR-AI symbiosis) dimensions. Second, we discuss potential impacts of the transition toward Industry 6.0 on Operations Research (OR) with associated challenges and chances, outlining a 7-layer architecture of OR-AI symbiosis in digital twins. We elaborate on the technology and viability principles that frame Industry 6.0 and discuss scenarios for further transitioning toward next industrial revolutions and cyber-virtual, AI-driven networks that learn, adapt, self-organize, and regenerate. We conclude by outlining research opportunities for OR in the new era of supply chain and operations management in the AI and superintelligence age.
PARALLAX-5 is a transition-level obligation interface for value-bearing decentralized systems. The interface consists of five primitive obligations: value conservation, authorization closure, signature integrity, temporal distinctness, and external-attestation trust. Under an explicit security-interface adequacy condition, every trust-base-respecting loss-inducing transition has a non-empty violation signature; the claim is falsifiable by basis counterexamples that are precisely defined. The substrate composes with a production EVM semantics via a typeclass-based refinement: nineteen abstract theorems lift to compiled Lean 4 proof terms over EvmYulLean's EvmYul.EVM.State (Cancun fork). The Lean 4 module compiles to 95 theorems with zero sorry; 129 Python fire tests pass across three suites; a 53-incident empirical catalog (2016–2026, $5.97 billion aggregate losses) classifies each entry by minimum observability set. The package also defines a step-secure execution-time shield, an AI-Agent Containment Theorem, a five-component PARALLAX-CROPS trust-surface vector, a 19-field machine-checkable certificate schema with seven-state lifecycle, an onchain certificate registry (Solidity 0.8.24, live on Sepolia at 0x8015A98dF9037Cd79a03B291a6fF3C2841992D5b), and three worked examples covering value conservation, bridge attestation, and AI-agent runtime gating. The standard text is dedicated under CC0 with structurally irrevocable non-capturability commitments; code artifacts are released under Apache-2.0; this paper is licensed under CC-BY 4.0. v1.0.1 changes (vs v1.0.0, doi:10.5281/zenodo.20400525): repository-hygiene release. Removed four non-substrate subsystems (hse, product, economics, chronos) that were not paper-aligned. Standardized fire-test count from 134 to 129 to reflect the cleaned codebase. Restructured standalone specifications under docs/ directory with canonical names (CHARTER.md, FORK_PROTOCOL.md, CERTIFICATE_SCHEMA.md, etc.). Converted forge-std to a proper git submodule. Added CITATION.cff, CHANGELOG.md, CONTRIBUTING.md, SECURITY.md. The substrate's mathematical content, theorems, and verification gates are unchanged from v1.0.0.
This working paper is an output of the Community Privacy Residency held in Taipei in 2025. https://community-privacy.github.io/ Keywords: Image-based abuse; non-consensual intimate imagery; evidentiary privacy; protected identity; sexual autonomy; Global South; digital evidence; hash evidence; zero-knowledge proofs; privacy-enhancing cryptography; survivor-auditable governance.
Muhammad Tahir, Adem Orsdemir, Usman Khalid, Alptekın Küpçü
The rapid adoption of blockchain technologies has intensified the need for robust security mechanisms in Ethereum smart contracts (SC). Due to immutability, vulnerabilities cannot be patched after deployment, leading to significant financial losses. While traditional static and dynamic analysis tools are widely used, recent research has explored Machine Learning (ML) and Deep Learning (DL) techniques for automated vulnerability detection. However, many existing approaches focus on single-vulnerability detection or suffer from high false positive rates and limited interpretability. To address these challenges, this study proposes an interpretable DL framework for multi-vulnerability detection in SC. The proposed model employs a lightweight One-Dimensional Convolutional Neural Network (1D-CNN) integrated with Integrated Gradients from Explainable AI (XAI) to provide transparent model decisions. SC opcode sequences are transformed into RGB-encoded sequential representations, preserving execution order while enabling efficient feature extraction. This study adopts a multi-class classification setting to evaluate generalization across diverse vulnerability types. The framework is evaluated using the publicly available Messi-Q dataset, containing labeled samples across multiple vulnerability types. Experimental results demonstrate effective multi-class detection, with performance varying across vulnerability types due to dataset imbalance and structural similarities. The model maintains efficiency while providing interpretable insights for selected vulnerability classes. The model provides opcode-level attribution, revealing localized patterns for certain vulnerabilities and more distributed attention for others. These findings demonstrate the practicality of lightweight and interpretable DL methods for scalable SC security analysis.
Abstract. The BeTrueCore protocol is presented — a decentralized collective decision-making system designed to resolve the fundamental contradiction between the authenticity of collective expression and manipulative influence in the digital environment. The system integrates three innovations: The Vote Weight Unit (VWU) model, which quantifies individual contributions based on time-weighted quality metrics rather than financial or institutional status. A cryptographic architecture based on the Minimal Anti-Collusion Infrastructure (MACI) and Zero-Knowledge Proofs (ZK-Proofs), decoupling the act of observation from its social consequences. An AI governance layer restricted to a "read-only" mode, delegating the final decision exclusively to cryptographic verification. Within the VWU model, the cumulative rating evolves via exponential smoothing with adaptive learning correction. The concept of the "Panopticon-Stent" is introduced as a novel architectural principle for transforming surveillance infrastructure from an instrument of control into an instrument of collective self-knowledge. We further propose the 23×32 ethical coding framework, mapping 23 Asilomar AI principles against 32 Thoughtful Decision Seeds Hygiene parameters to generate 736 technical ethical requirements, constituting a "digital DNA" for AI systems serving humanity. Keywords: collective decision-making, zero-knowledge proofs, MACI, vote weight unit, AI governance, digital democracy, Panopticon, Wabi-sabi, ethical coding, Web3
The debate between H.L.A. Hart and Lon L. Fuller is one of the most important discussions in legal philosophy. Hart argued that law is mainly a system of rules created and recognized by state institutions, and that law can exist separately from morality. Fuller, on the other hand, believed that law must contain certain moral qualities, such as clarity, consistency, and fairness, in order to be considered legitimate. Today, rapid technological development and the rise of decentralized digital systems have created new challenges for both theories. Technologies such as blockchain, cryptocurrencies, smart contracts, and Decentralized Autonomous Organizations (DAOs) allow communities to create and enforce rules without relying on governments or traditional legal systems. This paper examines whether the moral ideas within Hart’s and Fuller’s theories can still survive in a digital and post-sovereign world where many competing systems of rules exist outside state control. The paper uses a doctrinal and qualitative research method. It analyzes Hart’s The Concept of Law and Fuller’s The Morality of Law together with recent scholarship on digital governance, legal pluralism, and decentralized technologies. The paper argues that both theories still remain partly relevant, although they face serious limitations in decentralized environments. Hart’s theory is useful for explaining how communities accept and follow shared rules, even without a central authority. Fuller’s theory is especially relevant because decentralized systems often depend on clear, transparent, and predictable procedures to maintain trust among users. However, both theories struggle to explain legitimacy and morality in global digital communities where people follow different values and where no single sovereign authority exists. The paper concludes that modern legal theory must move beyond traditional state-centered ideas of law and develop more flexible approaches suitable for decentralized and technology-driven governance systems.
AI as Productive EnergyCivilization Physics — AI Economics & Human Systems Series This paper argues that AI should be understood less as a software feature embedded inside inherited workflows and more as a new form of productive energy: callable cognitive capacity that can be routed into many different tasks at low marginal cost. Like steam power and electricity before it, AI becomes economically transformative not when it exists as a tool, but when organizations and individuals reorganize production around its actual operational characteristics—rapid iteration, reusable context, broad symbolic competence, and continuous human evaluation . The analysis begins by distinguishing between adoption and reorganization. AI usage is spreading rapidly across firms and individuals, yet large-scale enterprise value remains uneven. The paper argues that this gap exists because many organizations are still attaching AI to old workflows rather than redesigning production loops around AI-native properties. AI therefore resembles earlier general-purpose technologies whose transformative impact depended on complementary organizational change rather than the technology alone. The historical analogy to steam and electricity provides the structural frame. Steam engines initially solved localized pumping problems before eventually reorganizing manufacturing and transportation systems. Electricity delivered its full productivity gains only after factories were redesigned around distributed power rather than centralized mechanical layouts. AI follows the same pattern: early deployment appears as isolated assistance or software augmentation, while deeper transformation emerges only when systems are rebuilt around AI’s strengths. To explain how this transformation occurs, the paper introduces the concept of micro-integration. A micro-integration is a bounded closure in which recurring friction is addressed through a tight loop connecting: Context retrieval. Model generation or agent action. Human evaluation and correction. Deployment or operational action. Telemetry and reusable feedback. Micro-integrations represent the primary mechanism through which AI diffuses socially and economically. Rather than following a single centralized adoption ladder, AI spreads through thousands of localized closures tailored to specific bottlenecks. The paper identifies several major routes of micro-integration: Local business arbitrage using AI-generated websites, lead extraction, and automation. Agentic product engineering through code generation, workflow automation, and autonomous tooling. Short-form content production using AI-assisted editing, generation, and localization. Personal health systems combining wearable data, coaching models, and behavioral planning. Personal knowledge systems integrating memory, search, scheduling, and persistent context. These use cases demonstrate that AI diffusion occurs not only through frontier labs or large enterprises, but through ordinary individuals and small teams building localized productive closures around recurring problems. A central theoretical contribution is the idea that AI acts as productive energy rather than as isolated intelligence. Productive energy becomes transformative when combined with complementary systems, feedback loops, and institutional structures. AI therefore does not simply automate work; it changes the feasible scale and granularity of human coordination, iteration, and cognitive outsourcing. The paper also emphasizes the importance of feedback loops in AI-native production. Anthropic’s analysis of software-development workflows illustrates how human-supervised “feedback loop” patterns dominate successful AI-assisted engineering. AI generates drafts or actions, humans evaluate and correct them, and the resulting loop stabilizes into reusable infrastructure. This pattern recurs across domains: AI succeeds where rapid feedback and bounded closures keep outputs connected to reality. At the same time, the paper recognizes important structural constraints. AI-assisted systems expand attack surfaces, increase dependency on centralized infrastructure, and may intensify concentration of compute, cloud resources, and capital. Micro-integrations can improve local productivity while still existing atop highly centralized infrastructure stacks. The paper therefore argues that governance, provenance, and accountability remain critical even in highly decentralized AI diffusion. The policy implications follow directly. Governance frameworks should focus less on generalized AI ethics rhetoric and more on preserving traceability, responsibility assignment, and operational accountability within AI-native closures. Public policy should support domain-specific AI literacy, micro-specialization pathways, and transparent feedback systems rather than only large-scale centralized deployment strategies. The paper concludes that AI diffusion is fundamentally plural rather than linear. AI spreads not through a single “leveling-up” ladder, but through countless small closures where callable intelligence removes recurring friction from work, culture, health, and everyday life. Within the Civilization Physics framework, this work establishes a broader principle: AI becomes economically transformative when human systems reorganize around its productive properties rather than merely embedding it inside inherited industrial structures. The future AI-native economy therefore emerges through distributed closures, continuous human evaluation, and increasingly dense networks of AI-assisted productive energy. Keywords: AI Economics · Productive Energy · Micro-Integration · AI-Native Economy · Human-AI Interaction · Workflow Redesign · General-Purpose Technology · Cognitive Infrastructure · Organizational Change · Civilization Physics
C. V. Suresh Babu, S. Kala, Z. Zebaa Shaikh, Mohammed Nijam · 5 authors
This chapter examines how decentralized governance models can improve accountability in AI-powered global supply chains by exploring the potential of Decentralized Autonomous Organizations (DAOs) for transparent and participatory digital due diligence. The work is intended for academics, policymakers, business leaders, and civil society organizations involved in supply chain governance and human rights protection. Using a conceptual and comparative analytical approach, the chapter reviews existing literature on AI-driven compliance systems and decentralized governance, and analyzes emerging initiatives such as the proposed Amnesty International DAO. The findings indicate that DAO-based governance can enhance transparency, stakeholder participation, and traceability in monitoring human rights risks, while also presenting challenges related to legal uncertainty, governance complexity, and scalability. The chapter concludes that hybrid governance models combining AI analytics with decentralized oversight may offer a more balanced framework for accountable and ethical SCM
This paper introduces the Synchrony Layer, a shared context and coordination standard for AI-generated software. As AI makes it easy for anyone to generate apps, workflows, agents, and automations, software ecosystems risk becoming fragmented across schemas, permissions, APIs, dependencies, provenance, runtime behavior, and approval rules. The Synchrony Layer addresses this by turning generated code into structured Generated Software Objects with formal specs, schemas, permissions, validation rules, compatibility semantics, provenance records, update policies, and execution receipts. The paper presents STACY Sync as a reference implementation for web, mobile, backend, workflow, agentic, off-chain, and on-chain software systems. It also explains how blockchain and data-availability layers such as Avail, Celestia, and EigenDA/EigenLayer can support public shared context for marketplaces, agent ecosystems, Web3 applications, attestations, provenance, and auditable execution records. Core thesis:AI makes software abundant. Synchrony makes abundance usable.