Frontier AI systems, including large-scale machine learning models and autonomous decision-making technologies, are deployed across critical sectors such as finance, healthcare, and national security. These present new cyber-risks, including adversarial exploitation, data integrity threats, and legal ambiguities in accountability. The absence of a unified regulatory framework has led to inconsistencies in oversight, creating vulnerabilities that can be exploited at scale. By integrating perspectives from cybersecurity, legal studies, and computational risk assessment, this research evaluates regulatory strategies for addressing AI-specific threats, such as model inversion attacks, data poisoning, and adversarial manipulations that undermine system reliability. The methodology involves a comparative analysis of domestic and international AI policies, assessing their effectiveness in managing emerging threats. Additionally, the study explores the role of cryptographic techniques, such as homomorphic encryption and zero-knowledge proofs, in enhancing compliance, protecting sensitive data, and ensuring algorithmic accountability. Findings indicate that current regulatory efforts are fragmented and reactive, lacking the necessary provisions to address the evolving risks associated with frontier AI. The study advocates for a structured regulatory framework that integrates security-first governance models, proactive compliance mechanisms, and coordinated global oversight to mitigate AI-driven threats. The investigation considers that we do not live in a world where most countries seem to be wishing to follow European Union ideals, and in the wake of this particular trend, this research presents a regulatory blueprint that balances technological advancement with decentralised security enforcement.
Open access
2 source records
Ethics and Social Impacts of AI
Artificial Intelligence in Healthcare and Education
Dakai Kang, Junchao Chen, Tien Tuan Anh Dinh, Mohammad Sadoghi
The rise of cryptocurrencies like Bitcoin and Ethereum has driven interest in blockchain database technology, with smart contracts enabling the growth of decentralized finance (DeFi). However, research has shown that adversaries exploit transaction ordering to extract profits through attacks like front-running, sandwich attacks, and liquidation manipulation. This issue affects blockchains where block proposers have full control over transaction ordering. To address this, a more fair transaction ordering mechanism is essential. Existing fairness protocols, such as Pompe and Themis, operate on leader-based consensus protocols, which not only suffer from low throughput caused by the single-leader bottleneck, but also allow adversarial block proposers to manipulate transaction ordering. To address these limitations, we propose a new framework, FairDAG, that runs fairness protocols on top of DAG-based consensus protocols. FairDAG improves protocol performance in both throughput and fairness quality by leveraging the multi-proposer design and validity property of DAG-based consensus protocols. We conducted a comprehensive analytical and experimental evaluation of two FairDAG variants - FairDAG-AB and FairDAG-RL. Our results demonstrate that FairDAG outperforms prior fairness protocols in both throughput and fairness quality.
Abstract This paper thoroughly explores the complex interplay between blockchain technology and the General Data Protection Regulation (GDPR) of the European Union, alongside the substantial challenges and potential opportunities stemming from their interaction. While the challenges of decentralization and immutability in blockchain are well-documented, this paper advances the discussion by incorporating legal developments, such as evolving interpretations of joint controllership and new advisory opinions. It also evaluates emerging use cases, including blockchain integration in digital currencies like Worldcoin, highlighting contemporary compliance challenges and innovative solutions. By proposing actionable frameworks that leverage technological advancements like chameleon hashes and zero-knowledge proofs, this paper provides a forward-looking analysis of how blockchain systems can align with GDPR principles, offering theoretical insights and practical pathways for compliance. The conclusion underscores the urgent need for clear regulatory frameworks. These frameworks are crucial to enable a balanced approach that fosters innovation while ensuring robust data protection compliance, and their absence could hinder the potential impact of the research.
Pump-and-Dump (PD) schemes pose a significant threat to the stability and fairness of Decentralized Finance (DeFi) markets, often resulting in substantial financial losses for investors. The early and accurate detection of these schemes is crucial for preserving trust in the rapidly expanding cryptocurrency ecosystem. However, existing detection methods primarily rely on post-event analysis and heuristic-based approaches, which are often inadequate for real-time and precise identification of PD activities. In this paper, we present PUMPWATCHER, an innovative framework that employs Graph Neural Networks (GNNs) and contrastive learning to detect PD schemes by modeling transaction behaviors within temporal graphs. PUMPWATCHER integrates advanced transaction graph construction, temporal GNNs, and contrastive learning techniques to enhance node and edge representations, thereby improving the detection of intricate and covert PD operations. We validate PUMPWATCHER on a dataset from Uniswap, encompassing 924,508 transactions across 858 tokens within December 2022. The results show that PUMPWATCHER outperforms state-of-the-art models, achieving a superior balanced accuracy of 92.3%, while significantly minimizing false positives and negatives. These outcomes highlight its potential to set a new standard in real-time detection of market manipulation, paving the way for more secure and resilient DeFi ecosystems.
Enterprise Information Systems have a long-established and crucial role for modern organizations, as they enable seamless integration and management of critical business processes, ensuring efficiency in operations, data accuracy, and enhanced decision-making capabilities. One of their most interesting emerging technologies refer to the use of Artificial Intelligence as they may seamlessly automate routine tasks, offer predictive analytics, and provide deep insights, ultimately leading to intelligent data-driven decisions and improved operational efficiency. Of course, this direction of work is accompanied by some important challenges that come from the opacity of certain AI models and their potential biases due to low-quality training data used. In this paper, we argue that such challenges can be mitigated by a novel framework able to integrate, in a transparent manner, quality-related metadata on datasets used for training the AI-enabled emerging technologies in the field of EIS systems. These metadata are minted as Non-Fungible Tokens (NFTs) over the blockchain.
Introduction:The study examined blockchain technology as a pillar of Web3, highlighting its principles of immutability, transparency, and decentralization. It analyzed the paradox that these same virtues could become disadvantages when it was necessary to correct errors, delete data, or deal with malicious uses, generating legal and ethical tensions.Development:Cases and studies were reviewed that showed how immutability guaranteed integrity and resistance to censorship but was incompatible with rights such as the “right to be forgotten” under the GDPR. Situations were also documented in which decentralization empowered both legitimate actors and criminals, eliminating consumer protection mechanisms. Faced with these dilemmas, solutions such as off-chain storage, updatable smart contracts, decentralized identity, and zero-knowledge proofs were evaluated. The proposal for double validation was highlighted, which incorporated a layer of smart contract verification to authenticate the origin and legitimacy of information before it was recorded. The validation of sensitive content by the people involved was also proposed as a strategy to prevent defamation, misinformation, or the dissemination of illegal material.Conclusion:The paper concluded that the potential of blockchain lay in its integration within an ethical, legal, and social framework. The implementation of mandatory verification and validation mechanisms strengthened accountability and individual protection, transforming blockchain into a tool that is not only secure and transparent, but also fair and socially responsible.
As generative AI (GenAI) technologies proliferate in urban governance, the challenge of building trustworthy AI systems becomes increasingly urgent. This chapter critically examines “trustworthiness” not as a purely technical attribute, but as a socio-political construct shaped by power, participation, and policy. Focusing on smart cities as testbeds of algorithmic governance, it explores how decentralized Web3 technologies—such as blockchain, DAOs, and data cooperatives—can offer structural alternatives to centralized, opaque systems. Drawing on action research from the Horizon Europe ENFIELD project and framed by EU policy developments like the AI Act and the Draghi Report, the chapter proposes a multi-layered governance model. It evaluates seven emerging techniques to strengthen GenAI accountability: (i) federated learning, (ii) blockchain provenance tracking, (iii) zero-knowledge proofs, (iv) DAO-based verification, (v) digital watermarking, (vi) explainable AI (XAI), and (vii) privacy-preserving machine learning (PPML). The chapter ultimately argues that trustworthy AI must be embedded in participatory governance, algorithmic transparency, and plural civic oversight. By reframing trust as a relational, institutional, and democratic issue, it contributes to reimagining smart cities not as technocratic projects, but as inclusive arenas for data justice and democratic renewal.
Contemporary Artificial Intelligence ("AI") systems, particularly Large Language Models ("LLMs"), face an imminent shortage of high-quality, humangenerated textual data, a phenomenon often termed "data exhaustion".This article examines the limitations of existing centralized data-annotation frameworks, highlighting critical issues such as bias, high computational overhead, and insufficiently adaptive infrastructures.Current market participants-including Scale AI, Appen, CloudFactory, and others-excel at rapidly scaling annotation services yet struggle with ethical sourcing, privacy compliance, and equitable compensation.In addition, legal and regulatory concerns, exemplified by stringent mandates such as the General Data Protection Regulation ("GDPR"), constrain the free flow of data essential for advanced AI research.As a corrective measure, decentralized data production paradigms are proposed, including the adoption of smart contracts, token-based incentives, and participatory governance through Decentralized Autonomous Organizations ("DAOs").While existing decentralized initiatives-SingularityNET, Fetch.ai,Ocean Protocol, Numeraire, and DcentAI-offer incremental innovations in reputation management and stakeholder engagement, they fail to fully address the nuanced requirements of large-scale "Mechanical Turk"-style data creation.In contrast, the author proposes a Weighted Directed Acyclic Graph ("WDAG") governance model which provides a multi-dimensional reputation framework, facilitating real-time validation of data contributions, adaptive ethical and legal compliance, and collaborative oversight by diverse community members.Findings suggest that such WDAGcentric systems can more effectively maintain data quality, ensure ethical alignment, and incentivize broad participation, thereby mitigating the looming data shortage and expanding AI's societal benefits.Ultimately, successful implementation requires coordinated efforts among policymakers, industry practitioners, and civil society actors to sustain both the technological and ethical integrity of AI research.By integrating WDAG-based governance with emerging decentralized solutions, the AI community may realize a more equitable, scalable, and future-ready paradigm for data provisioning.
Open access
2 source records
Ethics and Social Impacts of AI
Artificial Intelligence in Healthcare and Education
Stefano Balietti, Pietro Saggese, Stefan Kitzler, Bernhard Haslhofer
This chapter explores how Decentralized Autonomous Organizations (DAOs), a novel institutional form based on blockchain technology, challenge traditional centralized governance structures. DAOs govern projects ranging from finance to science and digital communities. They aim to redistribute decision- making power through programmable, transparent, and participatory mechanisms. This chapter outlines both the opportunities DAOs present, such as incentive alignment, rapid coordination, and censorship resistance, and the challenges they face, including token concentration, low participation, and the risk of de facto centralization. It further discusses the emerging intersection of DAOs and artificial intelligence, highlighting the potential for increased automation alongside the dangers of diminished human oversight and algorithmic opacity. Ultimately, we discuss under what circumstances DAOs can fulfill their democratic promise or risk replicating the very power asymmetries they seek to overcome.
This article explores the transformation of the state’s role in regulating personal data in the post-GDPR world. The author analyzes the impact of the EU’s General Data Protection Regulation (GDPR) on the evolution of the global privacy protection landscape, identifying trends towards harmonization and fragmentation of national legislations. The changing functions of the state as a regulator and guarantor of personal data protection in the context of digitalization are unveiled. The potential of blockchain technologies and distributed ledgers in ensuring user control over data is investigated. The influence of the development of the data market and new business models on the regulatory approaches of states and corporations is analyzed. The consequences of the spread of decentralized services for the relationships between the state, business, and civil society are considered. Priority directions for improving Ukrainian legislation in the field of personal data protection are substantiated, taking into account the realities of Web 3.0 and the need to balance innovation and security. The key idea is that the post-GDPR world stands at a crossroads between further fragmentation of the regulatory landscape and a long path towards harmonizing privacy standards. The choice of development trajectory depends on the coordinated political will of states, corporations, and global civil society to protect personal data as a shared value that unites humanity in the digital age. The article delves into the complex interplay of technological, legal, and societal factors shaping the future of data governance, offering insights into the challenges and opportunities ahead. It highlights the need for adaptive and inclusive regulatory frameworks that balance individual rights, economic interests, and public goods in an increasingly data-driven world.
Sarfaraz Gudumian, A. Lizy, S Jagadeeswari, S. Chinnadurai · 6 authors
Blockchain technology’s secure, decentralized platforms have revolutionized multiple industries. This paper discusses possible problems with employing blockchain technology to ensure security. This poses a question about scalability, privacy, and regulatory compliance. It suggests an approach to Blockchain-based Digital Signature Security Analysis (B-DSSA). This solution increases digital signature algorithms using the transparency and immutability of blockchain. This engineering makes it possible for electronic communications to be valid, intact and non-repudiated, making it applicable for secured settings like healthcare, finance, supply chain management etc. As results indicate, substantial advances have been made in preventing unauthorized access and tampering in digital transactions. By combining public and private blockchains, this method achieves scalability while protecting the privacy of sensitive data. This configuration makes it possible for real-time applications in healthcare and finance by optimizing resource utilization, maintaining high data integrity, and enabling speedier processing. Document signing systems, identity verification, and contract execution are some of the examples showing the flexibility and endurance of blockchain-based security solutions through B-DSSA. The paper reveals how blockchain technology may revolutionize the field of safety, leading to further research on marketing orientation issues.
Blockchain technology leverages a cryptographic system to provide secure and immutable storage of transaction histories within a decentralised framework. While various industries have demonstrated interest in integrating blockchain into their IT systems, concerns regarding accessibility, privacy, performance, and scalability persist. Permissioned blockchain frameworks offer a viable solution for securing confidential records. Extensive research has been conducted to explore the opportunities, challenges, application areas, and performance evaluations of different public and permissioned blockchain platforms. Given the sensitive nature of medical information, healthcare organisations must adhere to various legal obligations, including HIPAA regulations, to protect these data. Although navigating these requirements can be challenging, it is crucial for safeguarding the reputation of healthcare providers, maintaining patient trust, and avoiding legal repercussions. Permissioned blockchains represent decentralised digital ledgers tailored to collaborate among businesses and organisations. Their popularity has increased significantly in recent years, resulting in the availability of several leading options, such as Hyperledger Fabric, Corda, Quorum, and MultiChain. Each of these platforms presents its own set of advantages and disadvantages. Although blockchain technology remains relatively nascent in the permissioned realm, several factors warrant consideration when comparing these platforms. This study will review the existing landscape of blockchain technologies in healthcare applications and identify the research scopes. This research aims to determine how permissioned blockchain technology can effectively fulfil the requirements for managing healthcare data.
Autonomous AI agents present transformative opportunities and significant governance challenges. Existing frameworks, such as the EU AI Act and the NIST AI Risk Management Framework, fall short of addressing the complexities of these agents, which are capable of independent decision-making, learning, and adaptation. To bridge these gaps, we propose the ETHOS (Ethical Technology and Holistic Oversight System) framework, a decentralized governance (DeGov) model leveraging Web3 technologies, including blockchain, smart contracts, and decentralized autonomous organizations (DAOs). ETHOS establishes a global registry for AI agents, enabling dynamic risk classification, proportional oversight, and automated compliance monitoring through tools like soulbound tokens and zero-knowledge proofs. Furthermore, the framework incorporates decentralized justice systems for transparent dispute resolution and introduces AI specific legal entities to manage limited liability, supported by mandatory insurance to ensure financial accountability and incentivize ethical design. By integrating philosophical principles of rationality, ethical grounding, and goal alignment, ETHOS aims to create a robust research agenda for promoting trust, transparency, and participatory governance. This innovative framework offers a scalable and inclusive strategy for regulating AI agents, balancing innovation with ethical responsibility to meet the demands of an AI-driven future.
Mir Mehedi Rahman, Bishwo Prakash Pokharel, Sayed Abu Sayeed, Sujan Bhowmik · 6 authors
In the evolving landscape of cybersecurity, traditional information technology (IT) infrastructures often struggle to meet the demands of modern risk management frameworks, which require enhanced security, scalability, and analytical capabilities. This paper proposes a novel artificial intelligence (AI)–driven IT infrastructure backed by blockchain technology, specifically designed to optimize risk management processes in diverse organizational environments. By leveraging artificial intelligence for predictive analytics, anomaly detection, and data-driven decision-making, combined with blockchain’s secure and immutable ledger for data integrity and transparency, the proposed infrastructure offers a robust solution to existing challenges in risk management. The infrastructure is adaptable and scalable to support a variety of risk management methodologies, providing a more secure, efficient, and intelligent system. The findings highlight significant improvements in the accuracy, speed, and reliability of risk management, underscoring the infrastructure’s capability to proactively address emerging cyber threats. To ensure the proposed model effectively addresses the most critical issues, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique will be used to analyze and evaluate the interrelationships among the existing critical factors. This approach evaluates the interrelationships and impacts of these factors, verifying the model’s comprehensiveness in managing organizational risk. This study lays the foundation for future research aimed at refining AI-driven infrastructures and exploring their broader applications in enhancing organizational cybersecurity.
This research explores how integrating Omnipresent AI and smart contracts enhances the efficiency of medical insurance claims in commercial health insurance. AI enables real-time data processing, instantly accessing and evaluating medical records from secure ledgers. Blockchain technology integrates these records with smart contract policies for swift, accurate assessments. Smart contracts, hosted on a third-party certification platform, authenticate, grant access, and document the process, eliminating manual application for records. This streamlines claim reviews, enabling immediate payouts to policyholders. By leveraging AI, blockchain, and smart contracts, this approach optimizes efficiency, reduces costs, and accelerates settlements for insured beneficiaries.
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
Md. Hasibul Alam Ratul, Sepideh Mollajafari, Martín Wynn
Digital evidence plays a crucial role in cybercrime investigations by linking individuals to criminal activities. Data collection, preservation, and analysis can benefit from emerging technologies like blockchain to provide a secure, distributed ledger for managing digital evidence. This study proposes a blockchain-based solution for managing digital evidence in cybercrime cases in the judicial domain. The proposed solution provides the basis for the development of a new model that leverages a consortium blockchain, allowing secure collaboration among judicial stakeholders, while ensuring data integrity and admissibility in court. An extensive literature review demonstrates blockchain’s potential to create a more secure, efficient evidence management system. The proposed model was implemented in a test environment using a localised blockchain for developing and testing smart contracts, as well as integrating a web interface, with off-chain storage for managing evidence data. The system was subsequently deployed in both the Polygon and Ethereum test networks, simulating real-world blockchain environments, revealing that the operational cost in the Polygon network is reduced by 99.96% compared to Ethereum, thereby offering scalability without compromising security. This study underscores blockchain’s potential to revolutionise the chain of custody procedures, improving dependability and security in evidence management and providing more sustainable solutions within the criminal justice system.
Abstract In this chapter, we describe the importance of good governance in the metaverse. It offers unlimited opportunities and presents unique governance challenges. First, we describe the concept of good governance and its relevance to the metaverse. We emphasize that the speed of metaverse adoption depends upon the presence or absence of effective governance. Recognizing the metaverse as the next iteration of the internet, we present significant governance issues. Some issues such as interoperability, security, safety, privacy, law, and digital inequality are critical governance issues in the metaverse. Next, we explore the diverse governance frameworks to ensure the implementation of policies and regulations. These frameworks include decentralized governance, cross-sector collaboration, and standards-based governance. We also describe the best practices which are essential for good governance. To materialize the concepts and principles discussed, we present a compelling case study centered on Decentraland. This insightful exploration dissects a decentralized autonomous organization (DAO)-based governance structure, offering valuable insights into the intricacies and stages of governance proposals. We acknowledge both the merits and potential drawbacks inherent to this approach. This chapter aims to offer an all-encompassing view of metaverse governance, essentially serving as a comprehensive roadmap for traversing the multifaceted landscape of this digital frontier.
E-Government and Public Services
Ethics and Social Impacts of AI
Legal, Health, Environmental and COVID-19 Challenges