Smart contracts are programs stored on the blockchain, often developed in a high-level programming language, the most popular of which is Solidity. Smart contracts are used to automate financial transactions and thus bugs can lead to large financial losses. With this paper, we address this problem by describing a verification environment for Solidity in Isabelle/HOL. To this end, we first describe a calculus to reason about Solidity smart contracts. The calculus is formalized in Isabelle/HOL and its soundness is mechanically verified. Then, we verify a theorem which guarantees that all instances of an arbitrary contract type satisfy a corresponding invariant. The theorem can be used to verify invariants for Solidity smart contracts. This is demonstrated by a case study in which we use our approach to verify a simple token implemented in Solidity. Our results show that the framework has the potential to significantly reduce the verification effort compared to verifying directly from the semantics. • We provide a novel calculus to support the verification of Solidity smart contracts. • The calculus is formalized in Isabelle and its soundness is mechanically verified. • We demonstrate the approach by verifying an invariant for an implementation of a token in Solidity.
This article examines the transformative potential of blockchain technology and its integration with artificial intelligence (AI) in clinical trials, focusing on their combined ability to enhance integrity, operational efficiency, and transparency in the data governance. Through an in-depth analysis of recent advancements, the article highlights how blockchain and AI address critical challenges, including patient data privacy, regulatory compliance, and security. The article also identifies key barriers to adoption in the mentioned integration, such as scalability limitations, association with existing healthcare systems, and high implementation costs. By presenting a comprehensive overview of the current research and proposing strategic directions, this work emphasizes how the synergy between blockchain and AI can revolutionize clinical trials through process automation, improved stakeholder trust, and robust transparency.
Open access
Artificial Intelligence in Healthcare and Education
Technologies like Bluetooth and WiFi enable more and more devices to become interconnected, forming the IoT ecosystem. However, this growing connectivity brings significant security risks, especially in terms of access management. Blockchain technology, alongside smart contracts, has introduced novel ways to address trust concerns in decentralized networks. Traditional internet of Things (IoT) access control models rely on centralized authorities, making them vulnerable to single points of failure. Additionally, many smart contracts deployed today have exploitable weaknesses that can be leveraged to steal digital assets. To address this issue, we propose a decentralized approach, leveraging blockchain technology and smart contracts to ensure security and trust. Our methodology formalizes smart contract behavior using Transition Systems (TS) and Computation Tree Logic (CTL), allowing for the verification of key properties. Solidity code is translated into the Verds model-checking tool, which validates the correctness and security of the contracts. Our experiments demonstrate that the method is both scalable and effective, ensuring secure execution of smart contracts in dynamic IoT environments. This approach not only mitigates security risks but also highlights the potential of formal verification in improving the robustness of smart contracts, making them suitable for deployment in real-world applications.
Tehseen Mazhar, Sunawar Khan, Tariq Shahzad, Muhammad Amir Khan · 7 authors
This article discusses Blockchain and Generative AI in healthcare, including their uses, difficulties, and solutions. Blockchain technology improves EHR security, privacy, and interoperability, while smart contracts streamline supply chain management and administrative procedures. Blockchain verifies and secures IoT data, improving medical care and treatment, according to case studies. Generative AI systems like ChatGPT have transformed healthcare by personalizing therapy, diagnostics, and predictive analytics. AI systems can examine massive databases to diagnose diseases early, anticipate dangers, and personalize therapies. By providing timely information, boosting treatment adherence, and giving continuous support, AI-powered virtual health assistants have enhanced patient involvement. Generative AI has additionally enhanced medical research and drug development, cutting the time and expense of introducing new medicines. Generative AI and Blockchain provide safe patient data storage, high-quality AI training datasets, and efficient healthcare operations. Scalability, energy usage, and interoperability issues remain. Scalable Blockchain designs and standardized data integration and exchange protocols are suggested by this study. These technologies could improve medical research and therapy by making them safer, more effective, and more individualized.
Open access
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
The advancement of artificial intelligence systems across industries introduces opportunities, critical risks, including lack of transparency, resource mismanagement, autonomy risks, and malicious exploitation. This paper presents novel process and techniques, Decentralized AI Governance Networks, which addresses these challenges through a robust, blockchain-based governance model with Tokenized Power Control mechanisms. DAGN ensures human-centric AI operation by dynamically monitoring compliance, enforcing ethical and operational rules and linking energy or computational resource access to realtime compliance metrics. Key components includes Power Access Tokens which regulates energy and resource usage, issued and revoked based on adherence to governance policies. Distributed Ledger for Governance, Immutable blockchain records that enhance transparency, accountability, and trust in AI operations. Sentinel Systems autonomous agents that monitor AI behavior, flag violations, and ensure non-compliant systems. Stakeholder Voting Mechanisms is Transparent, weighted voting for policy updates and violation resolution. Applications span critical infrastructure, healthcare, finance, cybersecurity, military domains, ensuring AI cannot harm humans.
Olanrewaju Oluwaseun Ajayi, Chisom Elizabeth Alozie, Olumese Anthony Abieba, Joshua Idowu Akerele · 5 authors
Blockchain technology has emerged as a transformative force within the financial technology (Fintech) sector, offering unprecedented opportunities for efficiency, transparency, and security. However, its adoption also brings forth new challenges and vulnerabilities, particularly in the realm of cybersecurity. This review explores the dynamic landscape of Blockchain Technology and Cybersecurity in Fintech, highlighting both the opportunities it presents and the vulnerabilities it introduces. Blockchain technology, most notably recognized as the underlying framework for cryptocurrencies like Bitcoin and Ethereum, operates on a decentralized ledger system, enabling secure and immutable transactions. In Fintech, this technology promises enhanced transactional speed, reduced costs, and increased transparency, revolutionizing traditional banking and payment systems. Nevertheless, the decentralized nature of blockchain networks, while offering resilience against single points of failure, also poses unique cybersecurity risks. Smart contracts, self-executing contracts with the terms of the agreement directly written into code, introduce vulnerabilities such as code bugs and exploits. Moreover, the anonymity associated with blockchain transactions has raised concerns regarding illicit activities, money laundering, and terrorist financing. In response to these challenges, the intersection of Blockchain Technology and Cybersecurity in Fintech offers opportunities for innovation. Advanced cryptographic techniques, such as multi-signature authentication and zero-knowledge proofs, are being leveraged to enhance security and privacy in blockchain-based systems. Additionally, regulatory frameworks are evolving to address the emerging risks associated with Fintech innovations, ensuring compliance and consumer protection. While Blockchain Technology presents promising opportunities for revolutionizing Fintech, its integration must be accompanied by robust cybersecurity measures to mitigate vulnerabilities and safeguard against potential threats. Collaborative efforts between industry stakeholders, regulators, and cybersecurity experts are imperative to foster a secure and resilient ecosystem for blockchain-based financial services.
The convergence of humans and artificial intelligence systems introduces new dynamics into the cultural and intellectual landscape. Complementing emerging cultural evolution concepts such as machine culture, AI agents represent a significant techno-sociological development, particularly within the anthropological study of Web3 as a community focused on decentralization through blockchain. Despite their growing presence, the cultural significance of AI agents remains largely unexplored in academic literature. Toward this end, we conceived hybrid netnography, a novel interdisciplinary approach that examines the cultural and intellectual dynamics within digital ecosystems by analyzing the interactions and contributions of both human and AI agents as co-participants in shaping narratives, ideas, and cultural artifacts. We argue that, within the Web3 community on the social media platform X, these agents challenge traditional notions of participation and influence in public discourse, creating a hybrid marketplace of ideas, a conceptual space where human and AI generated ideas coexist and compete for attention. We examine the current state of AI agents in idea generation, propagation, and engagement, positioning their role as cultural agents through the lens of memetics and encouraging further inquiry into their cultural and societal impact. Additionally, we address the implications of this paradigm for privacy, intellectual property, and governance, highlighting the societal and legal challenges of integrating AI agents into the hybrid marketplace of ideas.
The integration of artificial intelligence (AI) into smart contracts holds the potential to both enhance and exacerbate consumer protection challenges. Since the AI system embedded within the contract’s code enables a high degree of contractual personalisation – by tailoring the legal agreement to the unique characteristics of the targeted individual consumer, thanks to its capacity to process large amounts of personal and behavioural data in real time – it opens the door not only to scenarios of AI-powered consumer manipulation, but also to the promising opportunity of a consumer-centric AI. Such an AI would serve the consumer’s best interests by adapting the contract to their specific needs and preferences, while protecting them from – rather than exploiting – their information, cognitive, and digital vulnerabilities. This research aims to assess whether the EU legal framework – particularly the UCPD, UCTD, AI Act, GDPR, and DSA – adequately ensures that these technologies are designed and deployed with the consumer’s well-being at their core. The paper explores AI-related risks such as digital manipulation, personal data exploitation, and the black-box problem inherent in algorithmic opacity, while also addressing the liability challenge in cases of consumer harm. Ultimately, it seeks to answer whether AI-driven smart contracts can truly foster a high level of consumer protection in the AI era, by offering novel interpretations of the existing legal framework and advancing proposals for reform aligned with the fairness-by-design approach and informed by behavioural science insights.
This paper tackles a low earth satellite governance paradox beyond the Kármán Line (100 kilometers above sea level): the same proprietary AI that keeps satellites safe also hides the reasoning states need to supervise private actors and assign responsibility. AI black-box compliance is now routine—operators disclose maneuvers but not the internal signals, thresholds, or telemetry transformations—leaving due regard, peaceful-use expectations, and fault analysis to operate on conjecture rather than evidence. The result is an accountability gap across core space-law instruments: Article VI of the Outer Space Treaty presumes continuing supervision; the Liability Convention relies on reconstructable causation; the LTS Guidelines anticipate demonstrable prevention measures. Terrestrial approaches offer partial assistance. The EU’s qualified transparency and the U.S. post-incident auditing travel unevenly off-Earth, and neither framework reliably reaches proprietary on-orbit autonomy. This paper shows with concrete operational scenarios (e.g., dynamic conjunction-thresholding, autonomous servicing approaches), provides an inevitable loss of public-law legitimacy and lack of protection for intellectual property. To address this, the paper proposes a dual-layer disclosure regime that protects legitimate trade secrets while restoring verifiable oversight. Layer 1—Regulatory Safe Rooms: accredited neutral venues conduct confidential code/model/telemetry review under treaty-backed non-disclosure, enabling certification, adversarial stress-testing, and forensic replay without commercial expropriation. Layer 2—Explainability Without Exposure: operators supply functional evidence—validated performance envelopes, adversarial test outcomes, decision bounds—augmented by privacy-preserving attestations (e.g., zero-knowledge proofs) in lieu of source disclosure. Implementation follows a “pressure-valve” path: condition launch licensing, frequency assignments, and mission approvals on participation now; seek UNCOPUOS endorsement later through a model protocol that harmonizes Artemis practices with non-signatories and codifies a TRIPS-compatible IP-Transparency Equilibrium Clause. The payoff is pragmatic rather than utopian: traceability sufficient to make due regard and liability doctrines workable again; incentives preserved for R&D; and a template that can translate to other thin-sovereignty domains (deep-sea, Antarctic, high-altitude autonomy) where algorithmic opacity currently outruns public law.
Digital identity has become one of the most pressing governance challenges of the 21st century. This paper argues that digital identity is not optional but inevitable, driven by four converging forces: privacy leakage, AI synthesis, corporate capture, and geopolitical vulnerability. Drawing on political philosophy (Rousseau, Rawls, Foucault, Habermas), comparative case analysis (Estonia, India, China), and emerging technical frameworks (zero-knowledge proofs, decentralized identity), the paper analyzes the opportunities and perils of digital ID systems. It proposes a Digital Social Contract as the normative and institutional framework for governing them. The paper concludes that the decisive question is not whether digital IDs will exist, but how they will be governed — and that only a robust Digital Social Contract, grounded in democratic legitimacy, institutional accountability, and adaptive governance, can ensure that digital identity serves citizens rather than controls them.
Emerging evidence suggests a declining labor share alongside rising markups, profits, and rents in parts of advanced economies, and artificial intelligence (AI) may intensify these dynamics by increasing the importance of capital and intangible assets. This paper examines whether broad employee ownership can help workers share in AI related surplus and mitigate distributional risks. First, it synthesizes competing perspectives on factor share measurement and the roles of technology and market structure, and it reviews evidence on employee ownership and profit sharing for wages, productivity, and firm performance. Second, it develops transparent simulation exercises in which AI adoption shifts surplus toward profits under alternative ownership trajectories. In a stylized high adoption scenario with no institutional change, the combined wage plus capital income accruing to workers falls by roughly 5% points of value added. Under expanded employee ownership, workers receive additional capital income on the order of 5% points, largely offsetting the decline in their overall claim on output. The paper concludes by assessing legal and financial architectures, including tokenization and institutional decentralized finance, that could reduce frictions in scaling employee ownership.
Smart contracts on the Ethereum blockchain continue to revolutionize decentralized applications (dApps) by allowing for self-executing agreements. However, bad actors have continuously found ways to exploit smart contracts for personal financial gain, which undermines the integrity of the Ethereum blockchain. This paper proposes a computer program called SADA (Static and Dynamic Analyzer), a novel approach to smart contract vulnerability detection using multiple Large Language Model (LLM) agents to analyze and flag suspicious Solidity code for Ethereum smart contracts. SADA not only improves upon existing vulnerability detection methods but also paves the way for more secure smart contract development practices in the rapidly evolving blockchain ecosystem.
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.
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.