Blockchain Papers

Cory Campbell, Sridhar Ramamoorti, Thomas G. Calderon

ABSTRACT Blockchain technology appears to have unique characteristics such as inherent trust, immutability, transparency, and tighter security that might facilitate the audit process. Using a survey of AICPA members, we examine responses to the evaluation of audit evidence related to management assertions in a blockchain-enabled environment. We posit three possible effects pertaining to reliance on audit evidence in blockchain-enabled environments: (1) Goldilocks Effect (no automation bias) or the ideal scenario where auditors exhibit “just right” levels of professional skepticism and due care resulting in an optimal level of auditing; (2) Halo Effect or positive automation bias, which potentially results in over-reliance on evidence and, in turn, underauditing and; (3) Hype Effect or negative automation bias, which potentially results in under-reliance on evidence and, in turn, overauditing. Our results suggest that respondents to our survey are predominantly influenced by the Halo Effect, indicating a propensity for positive automation bias across management assertions.

Yue Liu, Qinghua Lu, Liming Zhu, Hye-Young Paik

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.

Peiyun Zhang, Song Ding, Qinglin Zhao

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.

Renjie Ji, Wansen Wang, Yan Xiong, Wenchao Huang

The popularity of smart contracts on Ethereum blockchain has completely changed the way people perceive decentralized applications. With the expansion of smart contracts in areas such as DeFi and NFTs, more and more fraudulent contracts are emerging, causing users to suffer huge losses. In most cases, malicious users create their own fraudulent projects and lure other users to participate. One significant security threat in maliciously developed smart contracts is permission backdoor, which enable bad actors to manipulate other users’ funds as super administrators.In this paper, we propose and implement SolScope, an automated analyzer for effectively hunting potential permission backdoor threats in smart contracts. SolScope adopts an iterative algorithm that integrates symbolic execution and value summary. We evaluate SolScope on a vulnerability dataset of 524 manually verified smart contracts. SolScope is capable of not only accurately and effienctly detecting backdoor threats, but also generating reports that describe state variables affected by the backdoor and a function sequence that triggers the backdoor.

Guo‐Qiang Zhang, Yueyue Dai, Jian Wu, Xiaojie Zhu · 5 authors

No abstract is available for this record.

Christopher-John Rogers

The emergence of blockchain technology created an entire industry of innovative new digital assets--or tokens--and diverse new fields of expertise founded on ideological aspirations of a new World Wide Web that reimagines digital value transfer through decentralization and disintermediation. Experimentation in the so-called "Web3" industry produces rich new fields of ethnographic study revealing the experiences of diverse individuals navigating novel technological capabilities which give way to new avenues of identity formation, community building, and ecosystem creation. These exciting new endeavors come with difficult challenges threatening the realization of ambitious visions for digital futures. Ethnographic research conducted through discourse analysis, participant observation, and formal and informal interviewing identified three key challenges stemming from Web3 builder experiences creating ecosystems through token-economic design: the prevalence of scams impedes productive development and mainstream perception, tokenomics--the design and study of token-based economies forming much of the Web3 industry--is highly complex and under-developed as a field lacking sufficient expertise to meet demand, and regulatory uncertainty prohibitively raises costs and risk for builders. As the industry continues to grow more social science research and interest is needed to shed light on human experiences with these novel technologies.

Xinzhe Shen, Wenyu Li, Hanchuan Xu, Xiao Wang · 5 authors

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.

Ludovico Rella

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.

Anzhelika Mezina, Aleksandr Ometov

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.

Haris Alibašić

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.

Jing Chen, Feng Bao, Chenxi Li, Yixun Lin

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.

Dorottya Zelenyanszki, Zhé Hóu, Kamanashis Biswas, Vallipuram Muthukkumarasamy

Non-fungible tokens (NFTs) have huge potential to be included in metaverse-related applications such as digital ownership management and asset trading. However, existing research identified that privacy-preserving techniques and methods are essential for NFTs for large-scale adoption in the metaverse. This paper conducted an analysis of several existing research works that mainly use zero-knowledge proofs (ZKPs) and/or commitments to protect privacy for blockchain applications. Based on the results of this comparative analysis, we deducted several assumptions. This paper identifies the potential next steps to design new privacy-preserving techniques that will enable privacy-aware metaverse users to leverage the maximal benefits of the NFTs.

Zhi Li, Fuhe Liang, Henan Hu

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.

Moez Krichen

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.

Eduardo Castelló Ferrer

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.

Indhumathi Chandrasekeran, A. Dharmaraj, Ashima Juyal, M. Shravan · 6 authors

Cryptocurrency and data privacy are two important considerations in human resource management. Cryptocurrency, such as Bitcoin, can be used to pay employees, provide incentives, and manage payroll, but it also poses risks such as price volatility and regulatory issues. Data privacy, on the other hand, is the protection of personal information from unauthorized access and use. The use of blockchain technology, which is the underlying technology behind many cryptocurrencies, can enhance data privacy in human resource management by providing a secure and tamper-proof record of all transactions. However, it’s important to comply with all applicable laws and regulations related to the use of cryptocurrency and data privacy in human resource management.

Luke Stark

The ethics of artificial intelligence (AI) have become a matter of public concern. According to a recent Stanford report, the number of research papers in the area given at major conferences such as the annual Conference on Neural Information Processing Systems has increased fivefold since 2014, and ethics officers now abound at global technology firms (Moss and Metcalf 2020). Such major institutions as the US government, the United Nations, and the Vatican have articulated visions for so-called ethical AI.By AI ethics here I mean the study of how human values both shape and are shaped by the development of AI technologies. This definition is capacious: it includes the design and deployment of these systems with human values in mind; assessments and activism around the societal impacts of said technologies and their imbrications within existing asymmetries of power, justice, and equality; and the wider relationship between computing technologies and humans as ethical and moral creatures, for instance, through such phenomena as human emotions. Work in these areas is done by trained “ethicists” only infrequently, rarely involves what a member of the public would first think of when asked to describe AI, and sounds outré yet is all too relevant to contemporary social policy and societal inequity.The definition I offer is expansive, perhaps too much so. However, any definition in this field is perilous. The term AI is a leaky discursive umbrella sheltering heterogeneous and often contradictory ideas and practices. It is a quintessential boundary object of the ideal type, “plastic enough to adapt to local needs and constraints of the several parties employing [it], yet robust enough to maintain a common identity across sites” (Star and Griesemer 1989: 393). Those identifying with the term AI ethics might be expected to at least signal some vague acknowledgment that the development and deployment of AI technologies involve normative stakes or impacts. However, a welter of methods, interests, and political positions operate uneasily within this shallow consensus; given its shortcomings, some scholars working on what would colloquially be understood as “AI ethics” eschew the word ethics entirely.Here, I aim to disaggregate AI ethics discourse through reviews of three recent books whose authors grapple in various ways with its rise and prominence. Those seeking an overview would benefit from consulting the first listed: The Alignment Problem: Machine Learning and Human Values, written for a general audience by Brian Christian. A science journalist, Christian grounds the book in dozens of interviews with academics and practitioners and frames it around the titular “alignment problem”: how to design machine learning (ML) systems “in alignment” with the intentions of their creators, ones which “capture our norms and values, understand what we mean or intend, and, above all, do what we want” (13). This “alignment problem” is presented as an engineering one, a framing that takes as a given the ongoing development and deployment of AI systems and implies it is possible to ameliorate these technologies sufficiently through various technical improvements.The Alignment Problem provides useful background on the contemporary technical landscape for those not already immersed in the field. When picturing an AI, the public might think of the psychotic HAL 9000 of Kubrick’s 2001: A Space Odyssey or Lt. Commander Data of Star Trek, but today’s AI systems are neither sentient or nor particularly charismatic. Christian points to the three main subfields of contemporary ML: unsupervised learning, in which an ML system is provided a mass of data and set to identify statistical patterns within it; supervised learning, in which an ML system takes a mass of already categorized data and uses the correlations it finds there to predict into which categories some new set of data should be sorted; and reinforcement learning, in essence a virtual Skinner box, an environment in which an artificial agent is assigned parameters for reward and punishment and then set to maximizing the former and minimizing the latter.Ready to command a starship, AI is not, but the field has always involved fantasy in search of a practical method. The computer scientists who participated in a now-famous inaugural seminar on the topic at Dartmouth College in 1956 were inspired by “the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it” (McCarthy et al. [1955] 2006). The human mind was a computer, their thinking went, and so a computer could be built to equal or surpass a human mind. These researchers spent the ensuing decades seeking the most effective computational means and methods to simulate intelligence and prove their conjecture correct. ML was developed in parallel but subordinate to other past technical paradigms in AI research, such as those built on logical symbols. As early as the 1950s, researchers developed computational pattern recognition: systems that could use cameras to identify repeating patterns in large amounts of data (Jones 2018; Mendon-Plasek 2020). Christian highlights one of the most famous of these early systems, Frank Rosenblatt’s Perceptron, based on a simple artificial network of simulated neurons, but efforts were rife in industrial, military, and other applied settings. Today’s ML systems, such as the Large Language Models (LLMs) powering products like Open AI’s ChatGPT, are built on “deep” neural nets with many layers of simulated neurons.An understanding of how AI technologies like deep learning work is critical to identifying which of these technologies’ societal impacts, present and future, are most pressing and problematic. In The Alignment Problem, Christian distinguishes between two groups. The first consists of scholars, practitioners, and activists concerned with the already existing impacts of ML-based automated decision-making systems, in areas such as policing and incarceration, hiring, and social assistance. The second consists mostly of technologists preoccupied with longer-term AI safety, a euphemism for the hypothetical dangers of a future “artificial general intelligence,” or a machine able to perform equally well as or superior to a human being in all respects. Despite being bundled together under the banner of AI ethics, these two groups have very different concerns and are frequently at odds. Since contemporary deep learning technologies are not remotely close to supporting artificial general intelligence, those concerned with AI safety would seem to be barking up the wrong tree. However, it is in the interest of these systems’ promoters both to give the prospective, future-focused gloss of science fiction to AI ethics and to the broader field of ML, and to imply subtly to the comfortable that the disruptive social impacts of AI systems are safely in the future. A focus on AI safety satisfies these ideological goals admirably, so the term is appearing more and more frequently in AI ethics contexts.Indeed, The Alignment Problem might focus more pointedly on the history of the term AI ethics itself, and the effects of bundling all contemporary public discussions about human social mores, values, and the societal impacts of AI systems under the banner of “ethics.” High-level overviews of the topic define ethics broadly, as “the rational and systematic study of the standards of what is right and wrong” (Kazim and Koshiyama 2021: 3). Computer ethics as a defined field developed out of engineering ethics in the 1980s and at its inception possessed many of the same fault lines as AI ethics discourse today (Moor 1985, 2001). Engineering ethics often prioritizes a focus on material problems and their solution through improved design. One of the first textbooks on the subject, Deborah G. Johnson’s Computer Ethics (1985), included intellectual property law as applied to software, the unique threat posed to human privacy by computing technologies, and the ethical responsibilities of computing professionals. Yet most scholarly references to the specific notion of AI ethics prior to around 2015 did not involve applying ethics as a branch of philosophy to studying the context of AI’s potential uses. Instead, AI ethics was most often invoked in metaphysical speculations about the status of machines as autonomous ethical agents (what today would be an “AI safety” topic).An article by well-known Silicon Valley journalist John Markoff, titled “How Tech Giants Are Devising Real Ethics for Artificial Intelligence” and published in the New York Times in early September 2016, signaled the discursive shift toward contemporary AI ethics talk. Markoff reported that industry researchers from several large Silicon Valley companies (including Microsoft, where this author was once employed) sought to develop “a standard of ethics around the creation of artificial intelligence,” one meant to “ensure that A.I. research is focused on benefiting people, not hurting them.” The article’s framing anticipates several of the elements that have characterized AI ethics discourse in the years since: statements of lofty humanitarian ambition used to justify industry aspirations to self-regulation, the contention that policy makers would inevitably lag in understanding AI systems, and an insistence that government oversight of AI would be both undesirable and ineffective. Perhaps most crucially, the piece suggested that the development of AI technologies was as inevitable as their effects would be widespread and disruptive: social scientists and philosophers needed to be put “in the loop” to help computer scientists manage the effects of AI’s undoubtedly epochal impacts.Business and professional ethics were two of the most direct antecedents for today’s AI ethics discourse as developed and propagated in corporate spaces (Greene, Hoffmann, and Stark 2019: 2124). The sociologist Gabriel Abend (2014) has developed the idea of the “moral background” to describe second-order assumptions about what problems or questions count as of ethical concern. Abend and others have noted that professional ethics codes in fields like engineering implicitly work to distinguish members of a particular profession from outsiders through recognition of their skills or expertise and by an emphasis on obligations to colleagues and clients, as well as the general welfare, and on enforcement based on public visibility (Abbott 1983). The implicit moral background of today’s professionalized AI ethics is latent in Markoff’s piece; it matches the analysis by my colleagues Daniel M. Greene, Anna Lauren Hoffmann, and myself (2019) of the then nascent genre of AI vision statements. This background presents a deterministic vision of AI’s development and deployment, in which the adoption of these technologies cannot be stopped and the ethics of which are best addressed through certain narrow kinds of technical and design expertise. More recently coined industry terms such as AI safety and responsible AI reflect this worldview, and many of the various existing or proposed mechanisms for the ethical oversight of AI systems are easily co-opted into broader forms of neoliberal governance and capitalist accumulation (Stark, Greene, and Hoffmann 2021).It is crucial, then, that AI ethics include as a possibility that some applications of deep learning never be designed, built, or used at all. One way to respond to the “alignment problem” is thus to interrogate exactly whose values technologists presume deserve alignment with AI systems. Such critique has been led by activists and scholars trained in critical race theory, race and technology studies, gender and sexuality studies, and related fields. In the academy, this work is grounded on informed refusal in justice-based bioethics (Benjamin 2016) and on recognition of the genealogical continuities between contemporary AI systems and white supremacy (Golumbia 2009; Benjamin 2019; Katz 2020), patriarchy and misogynoir (Browne 2015; Noble 2018), and binary gender norms (Scheuerman, Paul, and Brubaker 2019).The activist work of groups such as the Our Data Bodies collective, Data for Black Lives, and the Algorithmic Justice League, to name three American organizations among many hundreds worldwide, has been even more central to the advancement of critical AI discourse. These organizations support what the AI and social justice organizer, advocate, poet, and author Tawana Petty describes as “visionary resistance” (Petty 2014). Such resistance entails mobilizing and working with local communities, particularly racialized, low-income, or otherwise marginalized ones, to document the impacts that the deployment of AI systems are having today. 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The of ethics, “the of moral always be human are and or is possible only is and humans are understood to be able to reflect on and their Such is neither an notion nor a of used to justify the of and have often been through to who and who is not is enough to this a of to human in computer science from in large the with and in the history of to the marginalized was understood as and of an of at least for some of and, by for book with a for to the ethics of systems, are best by this of and implicit be I with when that is wrong with technologies that and with as Christian in The Alignment Problem, is not so much that a of like is it is as that such a is useful for the of AI’s contemporary deterministic moral for moral In and are like ones and any of ethics the of a working in the of and AI systems and their backers and humans to understand as the for AI all normative a matter of and with two that for their cannot be that all in the world can be to number and that can be with it a

Martin A. Lipman

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.

Bokolo Anthony

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.

Rameez Asif, Syed Raheel Hassan, Gerard Parr

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.

Leonardo Dias Menezes, Luciano Vieira de Araújo, Marislei Nishijima

No abstract is available for this record.

Pablo Santamaría, Llanos Tobarra, Rafael Vargas, Antonio Robles-Gómez

The digital revolution is renewing many aspects of our lives, which is also a challenge in judicial processes, such as the Chain-of-Custody (CoC) process of any electronic evidence. A CoC management system must be designed to guarantee them to maintain its integrity in court. This issue is essential for digital evidence’s admissibility and probative value. This work has built and validated a real prototype to manage the CoC process of any digital evidence. Our technological solution follows a process model that separates the evidence registry and any evidence itself for scalability purposes. It includes the development of an open-source smart contract under Quorum, a version of Ethereum oriented to private business environments. The significant findings of our analysis have been: (1) Blockchain networks can become a solution, where integrity, privacy and traceability must be guaranteed between untrustworthy parties; and (2) the necessity of promoting the standardization of CoC smart contracts with a secure, simple process logic. Consequently, these contracts should be deployed in consortium environments, where reliable, independent third parties validate the transactions without having to know their content.