This Article examines the national security risks posed by intelligent non-fungible tokens, or iNFTs, which combine blockchain-based digital assets with adaptive artificial intelligence. It argues that iNFTs amplify concerns surrounding money laundering, terrorist financing, sanctions evasion, cybercrime, and disinformation while creating difficult cross-border problems of jurisdiction, choice of law, and enforcement. The Article concludes that current AML/KYC frameworks are inadequate and calls for updated legislation, international regulatory harmonization, and AI-enabled blockchain analytics.
While Decentralized Autonomous Organizations (DAOs) and Artificial Intelligence are reshaping the governance of academic societies, reliably integrating on-chain decisions with off-chain physical activities remains a critical challenge. The fundamental bottleneck is the difficulty of reliably integrating real-world execution outcomes into the digital decision-making loop. To address this, we propose an endogenous contribution evaluation framework integrating Decentralized Physical Infrastructure Networks (DePIN) and Vision-Language-Action (VLA) models. This approach maps physical entities to on-chain decentralized identities. By leveraging VLA edge nodes to analyze multimodal behavioral data collected via DePIN, the system autonomously generates a verifiable Proof of Real-World Contribution (PoRWC). This proof subsequently drives on-chain incentive distribution through a reputation-weighted consensus mechanism. Consequently, this framework establishes an endogenously trustworthy closed loop from physical processes to digital governance. We demonstrate its feasibility and scalability through a case study of the Chinese Association of Automation (CAA), providing a robust engineering path for the parallel governance of modern academic societies.
Decentralized Autonomous Organizations (DAOs) suffer from critical governance challenges, such as low voter participation, large token holdersâ dominance, and inefficient proposal analysis by manual processes. We propose APOLLO (Autonomous Predictive On-Chain Learning Orchestrator), an AI-powered approach that automates the governance lifecycle in order to address these problems. The gemma-3-4b Large Language Model (LLM) in conjunction with Retrieval-Augmented Generation (RAG) powers APOLLOâs multi-agent system, which enhances contextual comprehension of proposals. The system enhances governance by merging real-time on-chain and off-chain data, ensuring adaptive decision-making. Automated proposal writing, logistic regression-based approval probability prediction, and real-time vote outcome analysis with contextual feature-based confidence scores are some of the major advancements. LLM is used to draft proposals and a feedback loop to enrich its knowledge base, reducing whale dominance and voter apathy with a transparent, bias-resistant system. This work demonstrates the revolutionary potential of AI in promoting decentralized governance, paving the way for more effective, inclusive, and dynamic DAO systems.
This study examined the role of visual design in Non-Fungible Tokens (NFTs) as a strategic component of value creation and inclusive participation within the decentralized creative economy. The research addressed the growing need to understand how visuals influence identity, accessibility, and community-driven economic engagement in digital platforms. The study was conducted using a qualitative interpretive approach, employing visual rhetorical analysis and digital ethnography. Ten top NFT collections on the OpenSea marketplace were analyzed based on their visual characteristics, creator inclusion, and community interaction. The visual elements were assessed through rhetorical lenses ethos, pathos, and logos, while participation was observed through social media metrics and community discourse. The findings indicated that accessible and culturally resonant visual designs enhanced user engagement and contributed to broader economic inclusion. Collections with visually open, emotionally engaging, and culturally inclusive features attracted stronger community involvement and sustained market relevance. In contrast, NFTs with elitist or exclusive visual narratives demonstrated more limited participation and accessibility. The study concluded that visual identity plays a central role in shaping the success of NFTs not only as aesthetic artifacts but as instruments of economic democratization. It was recommended that future research explore algorithmic visual production and long-term community dynamics across diverse cultural contexts. This research highlighted the potential of visual design to foster equity, representation, and sustainable value in the evolving digital economy.
This paper presents an ontology-driven administrative monitoring system that integrates blockchain smart contracts to ensure transparency, accountability, and integrity in organizational processes.This tracks resources and manages administrative workflows in an open and decentralised manner, addressing long-standing governance challenges such as opacity and unaccountability.Many important administrative processes involve the movement of resources from one point to another; these resources and processes can be modelled similarly to the movement of goods in a supply chain.Motivated by the need to improve institutional governance, particularly in contexts where individual actions often undermine fairness, this study combines ontology and blockchain to formalize administrative processes and enhance traceability.A pre-created ontology from the author's previous work, developed for postgraduate administration at the University of Ibadan, was adopted.The ontology defines classes and activities which were translated into blockchain entities and smart contracts, implemented in Solidity, and deployed on the Ethereum test network.Test cases derived from ontology competency questions validate the functional correctness of the smart contracts.The results confirm that administrative activities can be monitored transparently and immutably, providing a foundation for broader applications in public administration, education, and corporate governance.
This paper explores how Decentralized Autonomous Organizations (DAOs) could inform and shape participatory procedures in democratic governance. We apply DAO decision-making, such as rule-based input aggregation, transparent participation, and programmable decision-making, to a real-world case: the legislative development of the Swiss E-ID law, a proposal to establish a digital identity system for secure online authentication for Swiss residents. Using data from the official legislative consultation, we simulate how DAO-inspired mechanisms could have altered the aggregation of input and policy outcomes. Our analysis contributes conceptually and empirically to debates on digital democratic innovations, showing how programmable governance can be used not only to design new institutional forms, but also to critically assess the procedural dynamics of existing ones.
Autonomous technology has advanced rapidly in recent years, with intelligent systems demonstrating increasingly sophisticated capabilities in perception, decision-making, and adaptive behavior. These advancements have positioned autonomous agents to be teammates, enabling collaboration with humans in diverse domains and prompting emergence of Human-Autonomy Teaming (HAT) systems. HAT systems increasingly involve multiple autonomous agents working alongside humans in dynamic, high-stakes environments. HAT systems are often engineered with static hierarchical structures that predefine leadership authority for a set of tasks, thereby constraining their adaptability to shifting situational demands or unanticipated conditions, resulting in unintended degradation of collaboration and task performance. For dynamic environments, HAT systems require flexible or emergent leadership structures between agents. This dissertation investigates shared leadership for enabling flexible authority distribution between human and autonomous agents to enhance collaboration and performance in multi-agent systems composed of human and autonomous agents. The objectives of this research were (1) to understand how shared leadership functions in human teams can be adapted for multi-agent HAT systems, (2) to model leadership emergence from the human's perspective and identify factors governing the temporal patterns, and (3) to compare performance and perceived team dynamics between shared leadership and centralized leadership. vspace{0.1in} newline Study 1 was a systematic literature review of shared leadership in human teams for deriving mechanisms that can be engineered into HAT. The review revealed that humans rely on interpersonal trust and performance-based competence assessments for leadership distribution, with decentralization and mutual influence as the most influential mechanisms for enabling sharing leadership. The review also identified questionnaire-based assessments and network analysis as viable measurement approaches, with the latter also a viable approach for implementing shared leadership in HAT. These findings established the theoretical and methodological foundation for operationalizing and assessing shared leadership in HAT. Study 2 was an experiment recruiting human participants to complete a series of object-recognition tasks which involved assignments of multiple unmanned aerial vehicles (UAVs) in a simulated search and rescue context. Modeling the experimental data using network analysis, specifically in how the human's trust-competence perceptions of the autonomy evolve over time, revealed temporal patterns of leadership assignment. The study included the Trust-Competence-Identity Network (TCIN) that was developed to capture the humans' perception of agents across repeated task iterations. Logistic regression at the population level demonstrated that competence functioned as a capability-based predictor, while temporal exponential random graph models at the individual levels demonstrated that trust operated as an individualized experience-driven factor for predicting leadership assignment. The results provided foundational evidence supporting TCIN in predicting leadership emergence in HAT, illustrating the co-variation of key factors in human selection of autonomous agents as the leader. Study 3 was another experiment recruiting human participants to complete a series of object-recognition tasks that included conditions of the traditional centralized leadership and shared leadership for comparison of performance in multi-agent HAT. Study 3 also included a newly developed shared leadership questionnaire for HAT, adapted from validated instruments in human teams to measure leadership dynamics in HAT. Shared leadership demonstrated superior performance compared to centralized leadership, suggesting that distributing authority between humans and autonomous agents produces better outcomes than concentrating authority. Logistic regression at the population level demonstrated that trust moderated the rate at which complementary claiming-granting increased, while temporal exponential random graph models at the individual levels demonstrated that participants ultimately adopted complementary patterns. The shared leadership questionnaire also revealed that participants perceived more leadership distribution, team collaboration, and deference to expertise under shared leadership than the centralized leadership condition. These findings demonstrate that shared leadership in HAT involves both temporal learning processes and recognition of functional benefits that transcend individual differences in agent evaluation, establishing shared leadership as a viable organizational structure for multi-agent teams.
Pankaj Gugnani, Monis Khan, Spandan Barve, Debanjan Sadhya ¡ 5 authors
Public blockchains like Ethereum generate vast amounts of transactional data, offering insight into significant economic and decentralized application activity. However, these data are often unstructured and semantically poor. Understanding the functionality of smart contracts is crucial for unlocking the potential of this data. This work presents a novel methodology to transform raw blockchain transaction logs into semantically rich representations. We first classify smart contracts by analyzing their function names and structures using N-gram profiling and embedding comparisons against known standards like ERC/EIP. This process assigns functional labels (e.g., DeFi, Exchange, Token) to the smart contracts. Subsequently, we model the framework as a heterogeneous graph of users and labeled contracts. We employ a modified Node2Vec algorithm with an enforced alternating node-type walk strategy to effectively capture the dynamics of user-contract interactions. This process yields low-dimensional vector embeddings for users and contracts, making blockchain data readily available for knowledge discovery. We demonstrate the utility of our approach through a smart contract recommendation system that suggests relevant contracts to users based on their interaction history and learned embeddings.
In recent years, deep learning (DL) has shown remarkable performance in smart contract vulnerability detection, with graph neural networks (GNNs) serving as a key technique for learning structured code representations. However, existing graph-based approaches suffer from semantic fragmentation, noisy node interference, and weak semantic alignment, which limit detection robustness and deployment efficiency. To address these challenges, we propose UCGVulDetector, a unified and efficient framework designed for smart contract security in blockchain-based communication systems. It consists of three modules: (1) Structural Simplification (UDP): a hierarchical pruning strategy that refines abstract syntax trees by removing redundant nodes while preserving key semantics; (2) Graph Information Enhancement (UGSF): constructing heterogeneous graphs from Solidity ASTs and integrating control-flow, data-flow, and state-slot-chain (SSC) relations to capture multi-dimensional semantics; and (3) Graph Encoding and Alignment (PGE+TSCC): employing a pairwise graph encoder combined with temperature-scaled contrastive learning to align vulnerability semantics in a shared latent space. These modules collaboratively unify structural and semantic information to enhance feature representation. Experiments on the SolidiFI and MESSI datasets demonstrate that UCGVulDetector achieves F1-score improvements of 10.53%$\mathbf{1 1. 5 0 \%}$%ver state-of-the-art methods, delivering more accurate and robust vulnerability detection performance.
As artificial intelligence becomes deeply embedded in critical sectors like finance and medicine, we face a pressing challenge: how to guarantee its integrity. At the heart of this issue is a conflict between the proprietary nature of AI models, which are valuable assets, and the growing need for transparency in their operations. This paper lays out an architectural blueprint that resolves this tension by bringing together blockchain technology and Zero-Knowledge Proofs (ZKPs). We show how it's possible to verifiably confirm that an AI model has run correctly without exposing any of its confidential internal parameters. We walk through a simulation where a Deep Neural Network (DNN) produces an inference, and a ZKP is generated to prove the calculation used the legitimate model weights. This proof, along with the public data, is then recorded on a decentralized ledger, creating a permanent, auditable trail. A key part of our work is a comparison of two major ZKP technologies, zk-SNARKs and zk-STARKs, where we break down their respective trade-offs. Our simulation's effectiveness is demonstrated through resilience testing; it successfully identified and rejected 100% of fraudulent attempts, including both tampered outputs and counterfeit models. This demonstrates the architecture's efficiency in creating a provably secure and auditable trail, lighting a path toward genuinely trustworthy AI.
Rana Hassam Ahmed, Muhammad Zeeshan, Unais Ali, Muhammad Sarfraz Khan ¡ 7 authors
Smart contracts power decentralised applications, but once deployed, their flaws stay exploitable. Existing fuzzers such as ConFuzzius, Smartian, and VULSEYE use hybrid static and dynamic analysis but depend on fixed heuristics and lack adaptive learning. AI-FUZZ is an adaptive machine learning guided fuzzing framework that pairs deep reinforcement learning with stateful graybox fuzzing. It learns from execution traces to improve input generation, focus on high-risk contract states, and cut redundant executions. The framework also includes static analysis, adaptive mutation, and an oracle-based validation to boost accuracy and reduce false positives. Tested on 42,738 real-world contracts, AI-FUZZ achieved a 96.8% true positive rate, 4.2% false positive rate, 27% higher detection coverage than leading fuzzers, and a 33% reduction in average detection time. It scales across small, medium, and large contracts and offers a self-improving, efficient, and reliable approach for large-scale blockchain security audits.
Artificial intelligence increasingly governs access to credit, employment, and identity verification, raising questions of rights protection when deployed across borders. This paper develops a computable framework for Fundamental Rights Impact Assessment (FRIA) that transforms the legal principles of necessity and proportionality into quantifiable metrics. By embedding these standards into algorithmic pipelines, the framework enables verifiable auditing of high-risk AI systems. Simulations were conducted in two domains, credit scoring and biometric authentication, using synthetic datasets modeled on European and non-European jurisdictions. The necessity audits reduced the average input set by 24.6 Âą 2.3 variables while sustaining predictive accuracy, while proportionality assessments exposed heavy reliance on sensitive features in 39%* of credit scoring models and significant subgroup disparities in biometric authentication. Distributed verification protocols preserved results on blockchain ledgers, ensuring transparency and cross-border accountability. The findings demonstrate that computable FRIAs can operationalize fundamental rights obligations, producing results that can be inspected by regulators and reviewed in courts. The study concludes that computable methods offer a practical bridge between jurisprudential principles and algorithmic implementation, though persistent divergences in cross-border proportionality standards remain a major challenge for harmonized enforcement.
National identity systems require efficient, equitable decision-making that safeguards personal data. This article proposes a Self-Sovereign Identity (SSI) architecture, supported by a Verify-Without-Reveal (VWR) framework, designed for national-scale implementation. SSI places credentials in a citizen wallet and enables selective disclosure and zero-knowledge proofs, so services can verify attributes without seeing underlying records. VWR adds the policy and accountability spine: yes/no attribute APIs for holder-absent cases, purpose-bound and zero-trust enforcement on every call, and an immutable audit layer on a permissioned ledger. The study synthesises current standards and leading implementations in Europe and worldwide and formulates a deployable blueprint with clear roles, consent and lawful-override flows, per-agency pseudonyms, and regulator and citizen visibility. The study outlines reference APIs, user experiences for wallets and verifiers, and performance metrics suited for national workloads. Privacy-preserving AI strengthens biometric liveness, fraud detection, and anomaly response without centralising sensitive data. The framework aligns with GDPR data minimisation and purpose limitation, supports the European Digital Identity Wallet, and meets high-risk AI governance requirements. Results show how SSI proofs and VWR controls reduce unconsented disclosure and cross-agency browsing, while keeping latency low and interoperability high. The contribution is both conceptual and operational: a phased migration path that turns verify-without-reveal into the default mode for government and regulated services, improving security, inclusion, and public trust.
This paper reviews the innovative applications of AI and Web3 in metaverse social platforms. It first analyzes the foundational roles of AI (e.g., virtual avatar generation, intelligent interaction, personalized recommendation) and Web3 (e.g., blockchain, NFTs, decentralized identity) in enabling immersive, secure, and user-centric social interactions. It then examines their synergies, with case studies of Decentraland and The Sandbox illustrating practical integrations. The research identifies key challenges, including technical bottlenecks (e.g., AI realism, blockchain scalability), user-related issues (e.g., awareness, privacy concerns), and industry-level hurdles (e.g., regulatory ambiguities, homogenization). Finally, it proposes future directions: advancing AI/Web3 technologies, expanding application scenarios across education and entertainment, and implementing strategic recommendations to foster inclusive and sustainable metaverse social ecosystems.
This paper addresses the critical need for accountability in artificial intelligence (AI) systems, particularly in domains where decisions have significant societal and ethical implications. We propose a novel framework leveraging auditable attestations to ensure provable compliance with predefined standards and regulations. The core of our approach involves generating verifiable proofs about the behavior and characteristics of machine learning models, allowing for independent audits and assessments. We explore the theoretical foundations of such attestations, focusing on cryptographic techniques like zero-knowledge proofs and secure multi-party computation, which enable the verification of model properties without revealing sensitive information. Furthermore, we discuss the practical implementation of our framework, including the design of attestation protocols, the selection of relevant model properties to verify, and the development of tools for generating and validating attestations. We illustrate the effectiveness of our approach through case studies in areas such as fairness in lending, transparency in healthcare, and safety in autonomous driving. Our results demonstrate the potential of auditable attestations to enhance trust and accountability in AI systems, fostering responsible innovation and deployment.
Arthur Carvalho, Liudmila Zavolokina, Suman Bhunia, Gerhard Schwabe
Regulatory changes have enabled American student-athletes to profit from their name, image, and likeness (NIL). However, only a fraction of the student-athlete population is actually profiting from their NIL, which raises questions concerning fairness and inclusiveness. Motivated by that scenario, we look at technological solutions capable of sharing a limited amount of financial resources fairly and inclusively. Following a design science methodology, we define design requirements for such technological solutions after interviewing student-athletes, which leads us to establish the inclusive-meritocratic fairness criterion. Subsequently, we determine design principles that artifacts aiming at helping student-athletes should satisfy. We find that a solution that satisfies the proposed design principles is to associate student-athletes with digital collectibles represented as non-fungible tokens (NFTs). The core idea behind our artifact is that student-athletes receive royalties in primary markets after NFTs are randomly minted, plus deterministic royalties in secondary markets whenever a transaction involving their collectibles happens. Interviews with student-athletes validate our design. We conclude the paper by discussing how our ideas give rise to a new NIL design theory.
Open access
Digital Games and Media
Ethics and Social Impacts of AI
Consumer Behavior in Brand Consumption and Identification
Third-party risk management (TPRM) reaches an inflection point, with artificial intelligence (AI) capabilities meeting pressing demands for real-time vendor risk oversight of increasingly complex digital ecosystems. Conventional assessment methodologies resting on manual questionnaires, annual review cycles, and document-centric evaluations are poorly matched to the pace and interconnectedness driving modern technology. This article analyzes how intelligent automation is remaking basic processes in vendor governance, from optimization of questionnaires through semantic modeling to predictive monitoring allowed through continuous data synthesis. Unstructured vendor control documentation is now parsed by natural language models to extract control metadata and produce risk assessments that must be validated, rather than created, by humans. Algorithmic integrity is tackled with multi-model verification architectures that employ parallel processing pipelines where ensemble methods quantify confidence levels and flag gaps in the vendor control environment for risk subject matter expert review. Brain-inspired computing principles underpin system design, with hierarchical feature extraction possible, along with adaptive learning from assessment outcomes. Technical debt becomes a critical governance factor, particularly in the context of data dependencies and configuration management across model lifecycles. Explainable artificial intelligence provides transparency that is vital to regulatory recognition, allowing risk officers to trace decision pathways and understand feature attributions underlying automated recommendations. Convergence of distributed ledger technology with intelligent risk systems unlocks opportunities for tamper-proof audit trails and privacy-preserving attestations in support of cross-organizational governance frameworks framed by emerging digital resilience mandates.
As artificial intelligence (AI) technologies increasingly enter critical sectors like healthcare, transportation, and finance, developing effective governance frameworks is crucial for managing ethical, security, and societal risks. This paper conducts a comparative analysis of AI risk management strategies across the European Union (EU), United States (U.S.), United Kingdom (UK), and China. Using a multi-method qualitative approach, we investigate how these regions classify AI risks, implement compliance, structure oversight, and respond to innovation. Findings from high-risk contexts demonstrate the advantages and limitations of different regulatory models. The EU implements a structured, risk-based framework prioritizing transparency, while the U.S. uses decentralized, sector-specific regulations that promote innovation but risk fragmented enforcement. The UK's flexible strategy facilitates agile responses but may lead to inconsistent coverage, whereas China's centralized directives allow rapid implementation while constraining public oversight. These insights highlight the need for AI regulation that is globally informed yet context-sensitive, balancing effective risk management with technological progress. We conclude with policy recommendations for enhancing effective, adaptive, and inclusive AI governance globally.
Mustafa M. Abd Zaid, Husam I. Shaheen, Nigora Abduraimova, Jasim Gshayyish Zwaid ¡ 7 authors
Smart contracts are self-executing computer-based agreements that are implemented on blockchain systems and that their security is of paramount importance because they are not subject to change. The knowledge of token-level semantics can be helpful in determining the areas that may pose a weakness in these contracts. Nevertheless, current techniques tend to be based on rule based analysis, or syntax level analysis, which find it difficult to reflect the richer semantic structures that result in complex vulnerabilities. In order to overcome these limitations, this paper presents a framework that combines the pretrained transformer functionality of CodeBERT with task-specific fine-tuning and, as such, auto-detects and highlights vulnerabilities in smart contract Integrated Development Environment (IDEs). The method is an examination of token-level semantics, making it possible to identify vulnerabilities and understand them correctly in context. This framework can be applied directly in real-time to IDEs by developers to get vulnerability notifications and recommendations. The experimental outcomes prove that FTC-BERT is much more effective in detecting vulnerabilities and remembering experiments than traditional, and it is a semantic-sensitive, efficient, and automated method to detect vulnerabilities in smart contracts.
This research examines how emerging forms of digital sovereignty, decentralized infrastructures, and anticipatory AI governance are reshaping nationhood in the algorithmic age. Drawing on the conceptual framework of Algorithmic Nations (Calzada 2018) and incorporating new empirical insights from embedded action research (2022â2025), the study analyses the Basque Country as a paradigmatic case of a âsmall stateless nationâ navigating the global reconfiguration of power between states, corporations, and communities. The presentation synthesizes three competing post-Westphalian paradigmsâNetwork States (Srinivasan 2022), Network Sovereignties (De Filippi 2024), and Algorithmic Nations (Calzada 2018)âas shown in the comparative table on page 19, highlighting their differing assumptions regarding governance, identity, participation, and technological control. Building on the diagnostic indicators of Europeâs digital dependence (page 10) and the transition from Gaia-X to EuroStack (page 11), the study evaluates the strategic implications of digital public infrastructures, data cooperatives, federated architectures, and Web3 ecosystems for stateless nations. Through comparative analysis of the Global North (e.g., Scotland, Quebec, Flanders), the Global South (e.g., Kurdistan, SĂĄmi, Tamil, Amazigh), and the Basque Country (pages 16â17), the work demonstrates how communities with diverse geopolitical constraints can articulate forms of AI sovereignty grounded in rights-based, culturally rooted, and community-driven governance. The Basque case illustrates how fragmented digital systems (.eus, EJIE/Izenpe, Osakidetza, MUBIL, etc.) can evolve toward an interoperable, multi-scalar technopolitical architecture, aligning linguistic, territorial, and infrastructural dimensions. The analysis argues that AI-driven infrastructures, data governance, and decentralized architectures are not merely technical layers but emerging geopolitical terrains where stateless, indigenous, diasporic, and minority nations can renegotiate autonomy. The concept of Algorithmic Nations provides a framework for understanding how community sovereignty can be built through data commons, federated systems, and anticipatory governance, particularly in multilingual and culturally distinct territories such as the Basque Country. Overall, the study contributes to debates on global digital governance, digital sovereignty, and the future of nationhood by proposing that algorithmic infrastructures are becoming central to political organization. It calls for democratic, inclusive, and community-oriented models of AI governance capable of avoiding techno-authoritarianism, Big Tech dependency, and âsovereignty washing,â while enabling emancipatory, culturally anchored, and future-oriented forms of collective self-determination.
Democratic institutions increasingly rely on verifiable digital trust to enable fair participation and evidence-based decisions. Truvry is a decentralised protocol that converts behaviour-based evidence (usage patterns, transaction integrity, peer attestations) into portable cryptographic proofs that remain independent of any single platform or identifier, allowing individuals to transfer trust capital across domains while preserving privacy. The current prototype is zero-knowledgeâcompatible; in this version we use hashed proof anchoring and field-level redaction (no zk-SNARK module is deployed), with configurable smart-contract verifiers. By decoupling trust from identity, Truvry widens citizen inclusion, mitigates gatekeeping bias, and supplies auditable inputs for AI-mediated governance. In prototype tests (n=112), end-to-end proof issuance averaged 3.7 s (fastest local 1.4 s), verifier parse+check averaged 1.8 s, and the current minimum anonymisation entropy is 8.9 bits; gas costs for optional on-chain anchoring remained below US$0.02. All results are based on simulated user streams; a production pilot is planned.
This thematic issue examines how artificial intelligence, metaverse imaginaries, and decentralized Web3 systems have become arenas for states to build infrastructures, set technical standards, and project geopolitical power. It reconceptualizes technology not merely as an object of regulation but as a medium of statecraft through which sovereignty, security, and leadership are contested and remade in a multipolar digital order. This issue analyzes three interconnected dimensions: (a) the impact of global AI competition on state-making processes, enhancing coercive, extractive, delivery, and informational capacities similar to earlier state formation phases; (b) the nature of technological leadership as a relational and dynamic process influenced by interactions between leading and following states; and (c) the role of security logics in transforming external rivalry and internal governance through securitization. Through comparative analysis of the US, China, the EU, and emerging economies, this issue explores how diverse political systems encode openness, sovereignty, and accountability into their technological regimes, demonstrating that technological governance is inseparable from state-making. The contributions map competing logicsâsovereign, liberal, entrepreneurialâshowing that digital governance emerges not as convergence toward a singular model but as recursive entanglements of imagination and infrastructure.
The Paris Journal on AI & Digital Ethics Bootstrapping Trust across Web2 and Web3 Domains Using Publicly Verifiable Web Data Yuan Luš, Qiang Tang² Corresponding authors:luyuan@iscas.ac.cn ⢠qiang.tang@sydney.edu.au Abstract Through [âŚ]