Abstract Providing trust in machine learning (ML) systems and their fairness is a socio-technical challenge, and while the use of ML continues to rise, there is lack of adequate processes and governance practices to assure their fairness. In this paper, we propose FaaS, a novel privacy-preserving, end-to-end verifiable solution, that audits the algorithmic fairness of ML systems. FaaS offers several features, which are absent from previous designs. The FAAS protocol is model-agnostic and independent of specific fairness metrics and can be utilised as a service by multiple stakeholders. FAAS uses zero knowledge proofs to assure the well-formedness of the cryptograms and provenance in the steps of the protocol. We implement a proof of concept of the FaaS architecture and protocol using off-the-shelf hardware, software, and datasets and run experiments to demonstrate its practical feasibility and to analyse its performance and scalability. Our experiments confirm that our proposed protocol is scalable to large-scale auditing scenarios (e.g. over 1000 participants) and secure against various attack vectors.
Identity theft is one of the fastest-growing forms of cybercrime, driven by large-scale data breaches, phishing, and increasingly sophisticated impersonation attacks. Traditional identity verification methods such as passwords, PINs, and physical documents have proven inadequate in ensuring security at scale. Artificial Intelligence (AI) has emerged as a transformative enabler of next-generation identity verification by leveraging multimodal techniques, including facial recognition, voice biometrics, and document authentication. The paper discusses how AI- based verification systems can be used to prevent identity theft and how the system is used in real-time adaptive, and frictionless authentication over high-stakes areas, including banking, healthcare, e-commerce, and government services. We introduce a multi-layered verification system that combines the facial, voice and document verification modules in a single decision layer to minimize the false positives and negative but enhances the system resistance to spoofing and adversarial attacks. Practical implementations, advantages and governance are described using case studies of financial institutions, e-commerce websites and national identity programs. Nevertheless, there are still obstacles, such as demographic bias, privacy risks, adversarial vulnerability and lack of a coherent regulatory framework that makes it difficult to achieve mass adoption. In the future, we will address future directions in the area of decentralized identity, federated learning, zero-knowledge proofs, explainable AI, and international regulatory alignment. These innovations will work towards building trust, fairness and interoperability in digital identity ecosystems. Finally, this paper shows that AI-based identity verification is not merely a technological breakthrough but one of the essential needs to protect individuals, organizations, and governments against identity theft during the digital age.
Oct 7, 2023·Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing
José Ricardo Cedeño-García, Jesús Favela, Carlos Eduardo Sánchez-Torres
The significant increase in data production resulting from the widespread adoption of mobile and IoT technologies has revolutionized healthcare but also presents significant privacy and ethical challenges. The field of medical data collection is no exception and has limitations in terms of the source, variety and quantity of records from studies on healthcare and wellness. One way to address this dilemma is the use of the Blockchain for patient data collection and use. The anonymity of a centralized network allows the patient’s identity to be protected. The structure formed by nodes allows the information to be always available and does not depend on a main server. The immutability of records in the chain ensures unambiguous traceability of information flow by the healthcare provider. Finally, the network’s consensus and reward mechanisms could motivate new users to participate in active sensing. In this article we will expose the architecture of an application that relies on the Blockchain to meet the above information needs by leveraging the potential of the Ethereum network. In addition, we present a use case where consciously collected data from our platform is used to train a machine learning model automatically, using a P2P Browser-Based Computational Notebook as execution and distribution environment.
Federated learning (FL) is a distributed learning process that uses a trusted aggregation server to allow multiple parties (or clients) to collaboratively train a machine learning model without having them share their private data. Recent research, however, has demonstrated the effectiveness of inference and poisoning attacks on FL. Mitigating both attacks simultaneously is very challenging. State-of-the-art solutions have proposed the use of poisoning defenses with Secure Multi-Party Computation (SMPC) and/or Differential Privacy (DP). However, these techniques are not efficient and fail to address the malicious intent behind the attacks, i.e., adversaries (curious servers and/or compromised clients) seek to exploit a system for monetization purposes. To overcome these limitations, we present a ledger-based FL framework known as FLEDGE that allows making parties accountable for their behavior and achieve reasonable efficiency for mitigating inference and poisoning attacks. Our solution leverages crypto-currency to increase party accountability by penalizing malicious behavior and rewarding benign conduct. We conduct an extensive evaluation on four public datasets: Reddit, MNIST, Fashion-MNIST, and CIFAR-10. Our experimental results demonstrate that (1) FLEDGE provides strong privacy guarantees for model updates without sacrificing model utility; (2) FLEDGE can successfully mitigate different poisoning attacks without degrading the performance of the global model; and (3) FLEDGE offers unique reward mechanisms to promote benign behavior during model training and/or model aggregation.
Kawsalya Maharajan, A. V. Senthil Kumar, Ibrahiem M. M. El Emary, Priyanka Sharma · 9 authors
Blockchain encourages artificial intelligence towards intelligence while also increasing its autonomy and credibility. In this chapter, the authors examine the relationship between blockchain technology and artificial intelligence from a more thorough and three-dimensional standpoint. One of the greatest problems with blockchain implementations in IoV is that they cannot meet the computational and energy needs of conventional blockchain systems since IoV nodes are limited in their ability to use resources. A marketplace that enables stakeholders (CSPs, asset suppliers, service providers, regulators, etc.) to interact and exchange value with confidence based on smart provenance and governance may be developed using blockchain and distributed ledger technologies (DLT). These innovations offer a decentralised audit architecture that is safe. Such transactions (who uses what) can be kept on a distributed ledger marketplace in an immutable setting. A decentralised consensus process that does not need mining or incentivization in a permissionless architecture ensures data integrity.
Fair machine learning is a thriving and vibrant research topic. In this paper, we propose Fairness as a Service (FaaS), a secure, verifiable and privacy-preserving protocol to computes and verify the fairness of any machine learning (ML) model. In the deisgn of FaaS, the data and outcomes are represented through cryptograms to ensure privacy. Also, zero knowledge proofs guarantee the well-formedness of the cryptograms and underlying data. FaaS is model--agnostic and can support various fairness metrics; hence, it can be used as a service to audit the fairness of any ML model. Our solution requires no trusted third party or private channels for the computation of the fairness metric. The security guarantees and commitments are implemented in a way that every step is securely transparent and verifiable from the start to the end of the process. The cryptograms of all input data are publicly available for everyone, e.g., auditors, social activists and experts, to verify the correctness of the process. We implemented FaaS to investigate performance and demonstrate the successful use of FaaS for a publicly available data set with thousands of entries.
Chong Chen, Jianzhong Su, Jiachi Chen, Yanlin Wang · 10 authors
With the development of blockchain technology, smart contracts have become an important component of blockchain applications. Despite their crucial role, the development of smart contracts may introduce vulnerabilities and potentially lead to severe consequences, such as financial losses. Meanwhile, large language models, represented by ChatGPT, have gained great attention, showcasing great capabilities in code analysis tasks. In this article, we presented an empirical study to investigate the performance of ChatGPT in identifying smart contract vulnerabilities. Initially, we evaluated ChatGPT’s effectiveness using a publicly available smart contract dataset. Our findings discover that while ChatGPT achieves a high recall rate, its precision in pinpointing smart contract vulnerabilities is limited. Furthermore, ChatGPT’s performance varies when detecting different vulnerability types. We delved into the root causes for the false positives generated by ChatGPT, and categorized them into four groups. Second, by comparing ChatGPT with other state-of-the-art smart contract vulnerability detection tools, we found that ChatGPT’s F-score is lower than others for 3 out of the 7 vulnerabilities. In the case of the remaining 4 vulnerabilities, ChatGPT exhibits a slight advantage over these tools. Finally, we analyzed the limitation of ChatGPT in smart contract vulnerability detection, revealing that the robustness of ChatGPT in this field needs to be improved from two aspects: its uncertainty in answering questions; and the limited length of the detected code. In general, our research provides insights into the strengths and weaknesses of employing large language models, specifically ChatGPT, for the detection of smart contract vulnerabilities.
Pursuing “intelligent justice” necessitates an impartial, productive, and technologically driven methodology for judicial determinations. This scholarly composition proposes a framework that harnesses Artificial Intelligence (AI) innovations such as Natural Language Processing (NLP), ChatGPT, ontological alignment, and the semantic web, in conjunction with blockchain and privacy techniques, to examine, deduce, and proffer recommendations for the administration of justice. Specifically, through the integration of blockchain technology, the system affords a secure and transparent infrastructure for the management of legal documentation and transactions while preserving data confidentiality. Privacy approaches, including differential privacy and homomorphic encryption techniques, are further employed to safeguard sensitive data and uphold discretion. The advantages of the suggested framework encompass heightened efficiency and expediency, diminished error propensity, a more uniform approach to judicial determinations, and augmented security and privacy. Additionally, by utilizing explainable AI methodologies, the ethical and legal ramifications of deploying intelligent algorithms and blockchain technologies within the legal domain are scrupulously contemplated, ensuring a secure, efficient, and transparent justice system that concurrently protects sensitive information upholds privacy.
With the increasing popularity of cryptocurrencies and blockchain technologies, smart contracts have become a prominent feature in developing decentralized applications. However, these smart contracts are susceptible to vulnerabilities that hackers can exploit, resulting in significant financial losses. In response to this growing concern, various initiatives have emerged. Notably, the Smart Contract Weakness Classification (SWC) list plays an important role in raising awareness and understanding of smart contract weaknesses. However, the SWC list lacks maintenance and has not been updated with new vulnerabilities since 2020. To address this gap, this paper introduces the Smart Contract Weakness Enumeration (SWE), a comprehensive and practical vulnerability list up until 2023. We collect 273 vulnerability descriptions from 86 top conference papers and journal papers, employing the open card-sorting method to deduplicate and categorize these descriptions. This process results in the identification of 40 common contract weaknesses, which are further classified into 20 sub-research fields through thorough discussion and analysis. The SWE provides a systematic and comprehensive list of smart contract vulnerabilities, covering existing and emerging vulnerabilities in the last few years. Moreover, the SWE is a scalable and continuously iterative program. We propose two update mechanisms for the maintenance of the SWE. Regular updates involve the inclusion of new vulnerabilities from future top papers, while irregular updates enable individuals to report new weaknesses for review and potential addition to the SWE.
Foundation models including large language models (LLMs) are increasingly attracting interest worldwide for their distinguished capabilities and potential to perform a wide variety of tasks. Nevertheless, people are concerned about whether foundation model based AI systems are properly governed to ensure the trustworthiness and to prevent misuse that could harm humans, society and the environment. In this paper, we identify eight governance challenges of foundation model based AI systems regarding the three fundamental dimensions of governance: decision rights, incentives, and accountability. Furthermore, we explore the potential of blockchain as an architectural solution to address the challenges by providing a distributed ledger to facilitate decentralised governance. We present an architecture that demonstrates how blockchain can be leveraged to realise governance in foundation model based AI systems.
Artificial intelligence (AI) is a very powerful technology and can be a potential disrupter and essential enabler. As AI expands into almost every aspect of our lives, people raise serious concerns about AI misbehaving and misuse. To address this concern, international organizations have put forward ethics guidelines for constructing trustworthy AI (TAI), including privacy, transparency, fairness, robustness, accountability, and so on. However, because of the black-box characteristics and complex models of AI systems, it is challenging to translate these guiding principles and aspirations into AI systems. Blockchain, an important decentralized technology, can provide the capabilities of transparency, traceability, immutability, and secure sharing and hence can be used to make AI trustworthy. In this paper, we survey studies on blockchain-based TAI (BTAI) from a software development lifecycle view. We classify the lifecycle of BTAI into four stages: Planning, data collection, model development, and system deployment/use. Particularly, we investigate and summarize the trustworthy issues that blockchain can achieve in the latter three stages, including (1) data transparency, privacy, and accountability; (2) model transparency, privacy, robustness, and fairness; and (3) robustness, privacy, transparency, and fairness of system deployment/use. Finally, we present essential open research issues and future work on developing BTAI systems.
Multi-party interaction scenarios usually involve interaction and collaboration between multiple participants, such as business negotiation, teamwork, etc. In multi-party interaction scenarios, trust mechanisms are needed to ensure the use of rights and the fulfillment of obligations. The use of blockchain and smart contracts to establish a decentralized trust mechanism is a solution. The development process of smart contracts may require experts in different fields to design them together, and it is difficult for non-experts to understand smart contract code. The traditional method of smart contract development is for the developer to manually write the smart contract code, which may lead to low development efficiency and the inability of the scene participants to understand the smart contract code. To address this challenge, we propose a reuse-oriented visual smart contract generator to efficiently develop complex multi-party interaction scenarios. This paper proposes a model based on BPMN that uses smart contracts to express the process of multi-party interaction so that the parties in various domains understand the business requirements. Then, a multi-layer reusable library is proposed and used to generate a visual solidity code. We also integrate code compilation, security checking, and deployment into the target blockchain environment for testing. The function of the tool is verified in the case of flight delay insurance, which shows that it can aid the automated development of smart contracts for multi-party interaction scenarios. At the same time, the feasibility of the tool is verified by five cases selected from the literature, which shows that it can aid the development of smart contracts to achieve a high degree of reusability.
This paper investigates the role of the materiality of computation in two domains: blockchain technologies and artificial intelligence (AI). Although historically designed as parallel computing accelerators for image rendering and videogames, graphics processing units (GPUs) have been instrumental in the explosion of both cryptoasset mining and machine learning models. The political economy associated with video games and Bitcoin and Ethereum mining provided a staggering growth in performance and energy efficiency and this, in turn, fostered a change in the epistemological understanding of AI: from rules-based or symbolic AI towards the matrix multiplications underpinning connectionism, machine learning and neural nets. Combining a material political economy of markets with a material epistemology of science, the article shows that there is no clear-cut division between software and hardware, between instructions and tools, and between frameworks of thought and the material and economic conditions of possibility of thought itself. As the microchip shortage and the growing geopolitical relevance of the hardware and semiconductor supply chain come to the fore, the paper invites social scientists to engage more closely with the materialities and hardware architectures of 'virtual' algorithms and software.
The development of Distributed Ledger Technology (DLT) is pushing toward automating decentralized data exchange processes. One of the key components of this evolutionary step is facilitating smart contracts that, in turn, come with several additional vulnerabilities. Despite the existing tools for analyzing smart contracts, keeping these systems running and preserving performance while maintaining a decent level of security in a constantly increasing number of contracts becomes challenging. Machine Learning (ML) methods could be utilized for analyzing and detecting vulnerabilities in DLTs. This work proposes a new ML-based two-phase approach for the detection and classification of vulnerabilities in smart contracts. Firstly, the system’s operation is set up to filter the valid contracts. Secondly, it focuses on detecting a vulnerability type, if any. In contrast to existing approaches in this field of research, our algorithm is more focused on vulnerable contracts, which allows to save time and computing resources in the production environment. According to the results, it is possible to detect vulnerability types with an accuracy of 0.9921, F1 score of 0.9902, precision of 0.9883, and recall of 0.9921 within reasonable execution time, which could be suitable for integrating existing DLTs.
The rise in artificial intelligence (AI) and machine learning (ML) in cryptocurrency trading has precipitated complex ethical considerations, demanding a thorough exploration of responsible regulatory approaches. This research expands upon this need by employing a consequentialist theoretical framework, emphasizing the outcomes of AI and ML’s deployment within the sector and its effects on stakeholders. Drawing on critical case studies, such as SBF and FTX, and conducting an extensive review of relevant literature, this study explores the ethical implications of AI and ML in the context of cryptocurrency trading. It investigates the necessity for novel regulatory methods that address the unique characteristics of digital assets alongside existing legalities, such as those about fraud and insider trading. The author proposes a typology framework for AI and ML trading by comparing consequentialism to other ethical theories applicable to AI and ML use in cryptocurrency trading. By applying a consequentialist lens, this study underscores the significance of balancing AI and ML’s transformative potential with ethical considerations to ensure market integrity, investor protection, and overall well-being in cryptocurrency trading.
Ioannis Kourouklides, Novak I. Zukowski, Kleitos Alexandrou
This whitepaper presents the vision, mission, and overarching role of the GUT-AI Foundation. The Foundation aims to promote the research, development and eventually the deployment of user-friendly, human-centred and developer-friendly Artificial Intelligence (AI) systems for the betterment of humanity through an Ecosystem of Concepts and Implementations (ECI). The Foundation recognizes the potential of AI to revolutionize numerous industries, ranging from Healthcare and Education to Financial Services and Self-Driving Cars. However, the development of such AI systems poses significant challenges and impediments, such as multiple single points of failure, lack of interoperability, and lack of user adoption. Therefore, this whitepaper proposes a multidimensional approach to promote a whole ecosystem that has the ability to overcome such challenges. Primarily, the Foundation will encourage research into AI systems that are accessible, intuitive, and ready-to-use. The research will focus on proposing AI system architectures that meet the needs of the users, while they address their pain points in order to enhance both the User Experience (UX) and Developer Experience (DX). Furthermore, the Foundation will focus on promoting the adoption of best practices and Optional Open Standards for AI development and deployment that is automated, cost-effective, and scalable. For instance, these best practices will include the use of modular architectures, microservices, and containerization. By adopting these practices, the Foundation aims to enable interoperability and reuse of AI components. In addition, the Foundation envisions creating a marketplace, which will enable buyers to discover and adopt AI Solutions that meet their needs and preferences. The marketplace will also provide sellers with opportunities to showcase their AI Solutions, reach out to potential buyers and receive feedback from them. Finally, the Foundation will leverage emerging technologies such as Blockchain and Decentralized Autonomous Organizations (DAOs) to enhance the transparency, security, and trustworthiness of AI systems, while incentivizing innovation and collaboration among humans.
Julião Braga, Francisco Regateiro, Itana Stiubiener, Juliana Cristina Braga
Human-algorithm interaction is a crucial issue for humanity in light of the impacts of the recent release of ChatGPT3 and 4, among others. These advanced chatbots provoked a worldwide debate in March/2023, when a manifesto signed by several stakeholders was published and widely discussed in the media and academia. This work assumes that human-algorithm interactions are influenced by a context of diverse interests and perspectives, which adds high complexity to the problem. Therefore, this work proposes a solution to enable the effective participation of stakeholders from different domains and society in a constructive dialogue, using digital platforms as a medium. Inspired by the successful governance of the Internet infrastructure ecosystem, the proposal involves the creation of an Autonomous Decentralized Organization (DAO) implemented in the blockchain environment of the Ethereum network. However, before implementing the DAO, it is necessary to build a knowledge base, that is, an ontology, which guides its development in a safe and adequate way. A preliminary version of this knowledge base was manually built using Protégé with over 4,000 axioms.
AI-enabled blockchain refers to the use of AI to enable the analysis and decision-making processes based on data collected, shared, and stored by blockchain. This helps overcome some of the existing challenges in blockchain applications. Despite the growing number of review papers on blockchain and AI, there is a dearth of literature on AI-enabled blockchain in business scenarios. This study uses bibliometric-content analysis to (1) identify three stages of development of AI-enabled blockchain literature and point out the increasing diversity of technological applications; (2) identify the strongest foci of extant literature; (3) unveil the roles of AI-enabled blockchain in 10 application sectors, and identify the key roles of AI in enabling blockchain applications; (4) conclude the referred ethical issues from three levels and make further discussion. The findings present the trends of AI-enabled blockchain and could help developers and service providers better manage the use and ethical issues of AI in blockchain applications.
The traditional mode of cooperation between enterprises still suffers from major problems, including data privacy leakage, data falsification, and inefficient collaboration in data sharing. These challenges make it difficult for enterprises to ensure that their cooperative suppliers adopt sustainable practices in standards identification and operation processes. This paper proposes a “Value–Standard–Process” collaborative framework for blockchain-based enterprise data governance that helps ensure a high degree of data security, a high reliability of collaborative tasks, and a high transparency of value transformation. First, this paper proposes a new collaborative mode for blockchain-based manufacturing in the sharing economy, including the non-linear dynamic evaluation and value balancing mechanism of data with multiple attributes, a trusted data governance mechanism for blockchain-based manufacturing, and a smart contract generation mechanism for value-driven collaboration. Second, this paper explains these three components and the implementation of the overall framework. Third, this paper verifies the applicability and achievability of the proposed framework through experiments. Establishing the value-driven multi-level blockchain-based collaboration mode facilitates the effective flow of production factors and promotes trust in the digital economy of sustainability.
Smart contracts (SCs) are digital agreements that execute themselves and are stored on a blockchain. Despite the fact that they offer numerous advantages, such as automation and transparency, they are susceptible to a variety of assaults due to their complexity and lack of standardization. In this paper, we investigate the use of artificial intelligence (AI) to improve SC security. We provide an overview of Smart Contracts (SCs) and blockchain technology, as well as a discussion of possible SC-based attacks. Then, we introduce various AI categories and their applications in cybersecurity, followed by a thorough analysis of how AI can be used to enhance SC security. We also highlight the open questions and future directions of research in this field. Our research demonstrates that AI can provide an effective defense against assaults on SCs and contribute to their security and dependability. This article lays the groundwork for future research in the field of AI for SC security.
Robotics systems of all types are revolutionizing a wide variety of industries—transportation, manufacturing, and even healthcare—and yet, many essential ingredients for robotics systems in the real world are not technologically ready for deployment. Currently, robots lack the protocols and standards required to be safe and secure outside factories. In an attempt to close this gap, recent research has demonstrated the security benefits of combining robotics systems with blockchain-based and related technologies (e.g., smart contracts, zero-knowledge proofs, Merkle trees). In this perspective article, I argue that blockchain-based robotics is starting to provide innovative solutions (e.g., secure data sharing, consensus mechanisms, and new interaction methods) to urgent problems of robot security. I list the most important takeaways so far from this emerging field of research that I helped establish together with a growing community. I close the article by discussing the implications of the security challenges that the robotics research community is facing, and possible ways for us to move forward.
Abstract This essay is dedicated to the memory of Katherine Hawley.1 Bitcoin was invented to serve as a digital currency that demands no trust in financial institutions, such as commercial and central banks. This paper discusses metaphysical aspects of bitcoin, in particular the view that bitcoin is socially constructed, non-concrete, and genuinely exists. If bitcoin is socially constructed, then one may worry that this reintroduces trust in the communities responsible for the social construction. Although we may have to rely on certain communities, I argue that social construction doesn’t imply a demand for trust because the relevant communities don’t take on any relevant commitments. Bitcoin is indeed trust-free.
Abstract Industries are now deploying smart initiatives and innovative business models towards digital transformation. One of such initiatives is the adoption of distributed ledger technology (DLT) which promises to support smart industrial revolution. DLT facilitates non trusted entities to communicate and achieve a consensus in a fully distributed method through an immutable and cryptographically secure ledger. DLT ensures traceability and secure exchange of information while ensuring confidentiality and portability of data. However, only fewer studies have explored the extent to which DLT can support digitalization to achieve smart industrial process. Besides, the governance role of DLT in industrial sectors is still considered a nascent domain of research and DLT governance design and archetypes for smart industries are still in the early stage. Also, there are fewer studies in the literature that presents consensus mechanisms for integrating DLT for digitalization of smart industries. Grounded on the secondary data this study examines the practical benefits and challenges faced in achieving a smart industrial operation. Findings from this study identifies governance and security issues that influences DLT deployment in industrial sectors. More importantly, several factors that impacts the deployment of DLT for smart industries are presented. Implications from this study will be useful for industrial regulators, practitioners and researchers interested in gaining innovative insights about how smart industries can leverage DLT to create value for competitive advantage.
This research paper reviews the potential of smart contracts for responsible AI with a focus on frameworks, hardware, energy efficiency, and cyberattacks. Smart contracts are digital agreements that are executed by a blockchain, and they have the potential to revolutionize the way we conduct business by increasing transparency and trust. When it comes to responsible AI systems, smart contracts can play a crucial role in ensuring that the terms and conditions of the contract are fair and transparent as well as that any automated decision-making is explainable and auditable. Furthermore, the energy consumption of blockchain networks has been a matter of concern; this article explores the energy efficiency element of smart contracts. Energy efficiency in smart contracts may be enhanced by the use of techniques such as off-chain processing and sharding. The study emphasises the need for careful auditing and testing of smart contract code in order to protect against cyberattacks along with the use of secure libraries and frameworks to lessen the likelihood of smart contract vulnerabilities.