Amar Johri, Anu Sayal, N Chaithra, Janhvi Jha · 8 authors
The "Metaverse," a term popularized by Neal Stephenson's novel Snow Crash, has been discussed in the science fiction community for decades, but technological advancements have only recently made it a reality. The Metaverse is an all-encompassing, interconnected virtual environment where users can freely communicate with one another and digital content. This article examines how various technologies, primarily Virtual Reality (VR) and Augmented Reality (AR), have contributed to the development of the Metaverse (AR). These innovations have revolutionized the way we interact with digital media by enabling us to have genuine, realistic experiences. In addition, we examine the Metaverse technologies that make it possible to construct a fully realized, functional virtual world. Among these are recent advances in artificial intelligence (AI), cryptocurrencies, spatial and peripheral computing, and other fields. Our research investigates the advantages and disadvantages of these technologies, as well as how they may influence the future of the Metaverse. Furthermore, the article explores the darker aspects of the Metaverse, particularly the emergence of the "dark verse," which underscores the potential for organized illicit activities within the Internet due to insufficient oversight and governance of the Metaverse.
Artificial Intelligence (AI) models are increasingly integrated into high-stakes domains such as finance, healthcare, autonomous systems, and legal decision-making. As their influence expands, concerns about accountability, fairness, transparency, and regulatory compliance have become central to both researchers and practitioners. One of the key challenges is auditing AI models in a manner that is tamper-proof, verifiable, and compliant with evolving regulatory frameworks. Traditional auditing mechanisms rely heavily on centralized logs and organizational trust, which creates vulnerabilities in terms of manipulation, incomplete records, and opacity in data flows. Blockchain technologyâowing to its immutable, decentralized, and transparent natureâoffers a powerful paradigm for establishing data provenance in AI auditing. By ensuring traceability of datasets, model updates, training logs, and inference outcomes, blockchain can provide regulators, stakeholders, and organizations with reliable audit trails. This paper presents a comprehensive exploration of blockchain-powered data provenance for AI model audits. It analyzes the limitations of current audit systems, evaluates how distributed ledger systems can strengthen accountability, and proposes an integrated framework that combines blockchain with cryptographic verification, zero-knowledge proofs, and federated logging to ensure verifiability without exposing sensitive data. The study synthesizes contributions from literature, presents a methodology for deploying blockchain-based provenance systems in AI pipelines, and evaluates potential results in terms of efficiency, compliance traceability, and security. Simulation experiments suggest that blockchain-enabled audits improve transparency, reduce fraudulent activities in AI operations, and enhance compliance readiness by more than 50% compared to traditional audit approaches.
David Frempong, Chigozie Emmanuel Benson, Odunayo Oyasiji, Adeola Okesiji
The digitization of healthcare records and the proliferation of patient data across interconnected systems have raised significant concerns about privacy, regulatory compliance, and consent management. Traditional methods of obtaining and maintaining patient consent are often fragmented, static, and non-compliant with dynamic legal standards such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). This paper explores a blockchain-enabled framework for consent management in healthcare, focusing on enforcing patient privacy preferences and regulatory compliance. Through a comprehensive review of existing literature and frameworks, this paper proposes a decentralized consent management architecture leveraging smart contracts and distributed ledger technologies to provide secure, transparent, and tamper-proof consent enforcement. The study also outlines the potential of blockchain to automate compliance tracking, enhance interoperability, and empower patients with granular control over their health data. Recommendations for future research directions and technical challenges are also presented.
This article explores the ethical dilemmas propelled by a significant shift in the allocation of trust and intelligence due to blockchain technology and AI, resulting in a notable decrease in transaction costs. The ethical and political implications of democratizing the resulting productivity gains are noteworthy, and while the pie is expanding, how its slices are distributed remains an open question. Enter Worldcoin, an innovative worldwide initiative that creates an identity system based on proof of personhood and zero-knowledge proofs (ZKP) to provide everyone with a distinct and anonymous "World ID. Using the author's âcyberethics-mix" framework, this paper examines the possible implications of such a system concerning data's protection, ownership, accuracy, and accessibility, underscoring the ethical significance of a political approach emphasizing inclusivity and sustainability through digital decentralization.
Peter Ince, Xiapu Luo, Jiangshan Yu, Joseph K. Liu · 5 authors
In this paper, we test the hypothesis that although OpenAI's GPT-4 performs well generally, we can fine-tune open-source models to outperform GPT-4 in smart contract vulnerability detection. We fine-tune two models from Meta's Code Llama and a dataset of 17k prompts, Detect Llama - Foundation and Detect Llama - Instruct, and we also fine-tune OpenAI's GPT-3.5 Turbo model (GPT-3.5FT). We then evaluate these models, plus a random baseline, on a testset we develop against GPT-4, and GPT-4 Turbo's, detection of eight vulnerabilities from the dataset and the two top identified vulnerabilities - and their weighted F1 scores. We find that for binary classification (i.e., is this smart contract vulnerable?), our two best-performing models, GPT-3.5FT and Detect Llama - Foundation, achieve F1 scores of $0.776$ and $0.68$, outperforming both GPT-4 and GPT-4 Turbo, $0.66$ and $0.675$. For the evaluation against individual vulnerability identification, our top two models, GPT-3.5FT and Detect Llama - Foundation, both significantly outperformed GPT-4 and GPT-4 Turbo in both weighted F1 for all vulnerabilities ($0.61$ and $0.56$ respectively against GPT-4's $0.218$ and GPT-4 Turbo's $0.243$) and weighted F1 for the top two identified vulnerabilities ($0.719$ for GPT-3.5FT, $0.674$ for Detect Llama - Foundation against GPT-4's $0.363$ and GPT-4 Turbo's $0.429$).
This chapter investigates "data-opolies" within Web3 and Artificial Intelligence (AI), highlighting their implications for democracy and their impact on business and society. Data-opolies are defined as dominant entities, usually large tech corporations, that control vast amounts of data, affecting market competition and transparency. The chapter discusses how the monopolization of data by these entities creates power imbalances, challenging democratic values. It explores how AI, when controlled by data-opolies, amplifies their influence, raising concerns about privacy, ethical AI use, and equitable access to technology. The concentration of data and AI capabilities in the hands of a few exacerbates socioeconomic divides and threatens democracy by potentially manipulating public opinion and information flow. The chapter examines Web3 innovations as a decentralized, transparent, and user-empowering alternative to traditional data control models. These technologies are presented as tools for democratizing data ownership, enhancing individual autonomy, and fostering an inclusive digital economy. However, the chapter also questions these assumptions, especially in the context of crypto-libertarian maneuvers around the "Network State" paradigm. In conclusion, the chapter emphasizes the need for a balanced approach to leveraging AI and Web3 to mitigate democracy's erosion by data-opolies. It calls for collaborative efforts and a multistakeholder approach to developing regulatory frameworks and ethical guidelines that align with democratic values, ensuring the responsible use of AI and data in society.
The healthcare sector plays a pivotal role in both generating and relying on vast amounts of data, emphasizing the significance of collecting, managing, and sharing information. Technological advancements have facilitated the transformation of healthcare data into electronic health records (EHRs). These digital records are disseminated among various stakeholders, including patients, healthcare professionals, providers, insurance companies, and pharmacies. Given the sensitivity of healthcare information, the assimilation of new technologies is paramount. Blockchain technology, with its immutable nature and decentralized features, has emerged as a promising solution to instigate changes in the healthcare system. In the healthcare domain, where confidentiality is crucial, strict regulations are in place to safeguard patient privacy. Frameworks like the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) are designed to mitigate the risks associated with health data breaches. Although blockchain's characteristics, such as enhanced interoperability, anonymity, and access control, can improve the overall landscape of health data management, it is imperative for blockchain applications to adhere to existing regulatory frameworks for practical implementation. This paper delves into the examination of the compliance of blockchain-based EHR systems with regulations like HIPAA and GDPR. Additionally, it introduces a Blockchain-based EHR model specifically crafted to seamlessly align with regulatory requirements, ensuring its viability and effectiveness in real-world scenarios
There is no question about the innovation force and the economic potential of blockchain technology. As the basis for new currencies, financial services, and smart contracts, blockchain technology can be seen as the fifth disruptive computing paradigm, after mainframes, personal computers, the Internet, and mobile devices. However, there are questions about its ethical implications, which have the potential to also impact the economic success of blockchain technology. This article aims to provide ethical guidance on blockchain technology. In order to reach this goal, the focus of the ethical analysis will first concentrate on the unique characteristics of blockchain technology compared to other technology-based innovations. The unique nucleus of blockchain technology can be defined as a move from the trust in people to a trust in math, as a move from an internet of information to an internet of value, orâas I would proposeâa shift from an intermediated network to an immediate network. Second, the ethical opportunities (e.g., transparency, participation, global access to services) and risks (e.g., ecological impact, lack of legal monitoring and enforcement) associated with this unique nucleus of blockchain technology will be discussed. Third, an outlook on possible concrete solutions will be provided.
El artĂculo presenta al lector las caracterĂsticas y el contexto histĂłrico en el que surgieron los smart contracts, luego los clasifica y describe su incidenÂcia en la teorĂa general de los contratos, para lo cual distingue entre sistemas de automatizaciĂłn de la ejecuciĂłn contractual y contratos inteligentes en sentido estricto, afirmando que estos Ășltimos requieren del uso de inteligencia artificial. Lo anterior conduce al abordaje del concepto de consentimiento algorĂtmico y, finalmente, permite ofrecer algunas conclusiones acerca de su impacto en el derecho del consumo y el mercado financiero colombiano.
Abstract The use of computer technology to automate the enforcement of law is a promising alternative to simplify bureaucratic procedures. However, careless automation might result in an inflexible and dehumanized law enforcement system driven by algorithms that do not account for the particularities of individuals or minorities. In this article, we argue that hybrid smart contracts deployed to monitor rather than blindly enforce regulations can be used to add flexibility. Enforcement is a suitable alternative only when prevention is strictly necessary; however, we argue that in many situations a corrective approach based on monitoring is more flexible and suitable. To add more flexibility, the hybrid smart contract can be programmed to stop to request the intervention of a human or of a group of them when human judgment is needed.
As blockchain smart contracts become more widespread and carry more valuable digital assets, they become an increasingly attractive target for attackers. Over the past few years, smart contracts have been subject to a plethora of devastating attacks, resulting in billions of dollars in financial losses. There has been a notable surge of research interest in identifying defects in smart contracts. However, existing smart contract fuzzing tools are still unsatisfactory. They struggle to screen out meaningful transaction sequences and specify critical inputs for each transaction. As a result, they can only trigger a limited range of contract states, making it difficult to unveil complicated vulnerabilities hidden in the deep state space. In this paper, we shed light on smart contract fuzzing by employing a sequence-aware mutation and seed mask guidance strategy. In particular, we first utilize data-flow-based feedback to determine transaction orders in a meaningful way and further introduce a sequence-aware mutation technique to explore deeper states. Thereafter, we design a mask-guided seed mutation strategy that biases the generated transaction inputs to hit target branches. In addition, we develop a dynamic-adaptive energy adjustment paradigm that balances the fuzzing resource allocation during a fuzzing campaign. We implement our designs into a new smart contract fuzzer named MuFuzz, and extensively evaluate it on three benchmarks. Empirical results demonstrate that MuFuzz outperforms existing tools in terms of both branch coverage and bug finding. Overall, MuFuzz achieves higher branch coverage than state-of-the-art fuzzers (up to 25%) and detects 30 % more bugs than existing bug detectors.
Minh Vu Nguyen, Ngoc Thuy Le, Dung Hoang Duong, Yannan Li · 5 authors
This paper presents a comprehensive investigation into the role, functionalities, and complexities of blockchain oracles, focusing particularly on the implications for smart contracts in legal reasoning contexts. Oracles serve as a vital bridge to smart contractsâ inability to interact with external or âoff-chainâ data, enabling them to be used in a variety of real-world situations. Oracleâs integration, however, introduces a number of complexities, including security vulnerabilities, collectively referred to as the Oracle Problem. In addition to a review of existing literature, we also provide a mathematical analysis quantifying the computational complexity associated with automating legal reasoning and a novel design framework aimed at establishing oracles that are secure, efficient, and legally compliant. The paper aims to serve as a foundational text for researchers, legal practitioners, and blockchain developers, advancing the academic discourse surrounding blockchain oracles and their role in smart contracts.
This project embarks on a spatial inquiry into Web3. Often hailed as the next iteration of the internet, Web3 is more than a facelift; itâs a calculated unveil that prompts us to re-examine Web2âs participatory past. Importantly, Web3âs algorithmic architecture both expands the webâs horizons and reflexively delineates its own perceptual identity. As it rises alongside digital platformsâ hegemony, we must scrutinize the territories it foregrounds â the very âwheresââ and the underlying âwhysâ that sculpt its distinct vantage for vested agendas. Drawing insights from media studies, critical data studies, and STS, this project focuses on influential powers sculpting the interplay between corporate developers and the Web3 landscape. The theoretical framework is primarily organized around the concepts of news cartography, architectsâ gaze, and software performativity. Methodologically, this tripartite study leans on multiple ethnographic works to go beyond just studying tech structures, capturing both material and discursive forces that mold them. My empirical focus is grounded in specialist journalistic publications (in chapter 1), ethnographic observations, and aggregated data of sites (in chapters 2 and 3). Each chapter underscores its rationale for data collection, yet aliging with the ethos of infrastructure ethnography. My research pivots on the argument that Web3 gives rise to âalgorithmic spatiality.â It extends beyond softwareâs materiality, echoing geographersâ assertions that (digital) space is programmed, assembled, and arranged. Thus, I view Web3 not just as a deliberate construct, but also as a dialogical practice of shaping and organizing its very essence. Influenced by Masseyâs (1999) portrayal of power as spatial-relational dynamics, I employ âpower-geometriesâ as a foundational lens to discern the varied influences of Web3 on sociality. This juxtaposes with the pervasive power of existing digital platforms, often termed the Web2 status quo, awaiting transition. For new media research, approaching Web3 with an algorithmic and spatial lens invites us to see sociality as a dynamic reshaping, subtly directed by coded practices, often obscuring their corporate genesis. I argue that to truly fathom our unfolding digital landscape, itâs imperative to closely scrutinize the pivotal roles of key actors. Especially, the corporate-scripted agents, in all their forms, actively molding these emerging topographies.