Electronic medical information is becoming increasingly popular. Electronic medical information includes sensitive and private information. The medical information must be protected from intruders during the communication with patients. During the exchange of information, blockchain technology can secure information from intruders. There are several traditional methods for protecting electronic medical information. These approaches can communicate data accurately but are vulnerable to collective and coherent attacks. These protocols also require higher communication and computation costs. This study proposes a Lightweight Quantum Blockchain-based Framework to safeguard patient medical information across multiple hospitals. This framework can withstand quantum computer attacks. Additionally, the proposed protocol requires less communication and computation costs as compared to the existing protocols.
Blockchain Technology Applications and Security
Neuroethics, Human Enhancement, Biomedical Innovations
Abstract: Almost a century ago, the philosopher, cultural critic, and essayist Walter Benjamin grappled with the evolution of "The Work of Art in the Age of Mechanical Reproduction." Although art had experienced imperfect imitations and reproductions throughout history, advancements like photography and film in Benjamin's time drastically heightened the efficiency and fidelity of replication. This shift raised profound questions about the notions of "originality" and "authenticity," distancing reproduced works from the unique "aura" of their originals. Fast forward to our present digital age, where a few clicks or lines of code can effortlessly generate flawless replicas, improved duplicates, or even entirely fabricated "deep fakes." However, the advent of immutable blockchain ledgers, pioneered by Bitcoin, Ethereum, and other cryptocurrencies, and harnessed by non-fungible tokens (NFTs), is ushering in a new era of originality. Crucially, this new era encompasses provable originality and authenticity, paired with indisputable ownership and robust programming capabilities. Similar to how Bitcoin resolved the "double spending" predicament in our digital age, NFTs are now initiating a transformative shift in conventional notions of ownership and provenance while introducing novel forms of originality. Despite existing solely in digital form, crafted through programmable code, smart contracts, and technological protocols, an NFT can maintain its distinctive aura and original essence.
Open access
Blockchain Technology Applications and Security
Neuroethics, Human Enhancement, Biomedical Innovations
Building on Pietz’s speculation about the construction of a “history of the fetish’ that might be related to, yet stand apart from the fetish as a historical construct, this paper asks if there might be novel yet unmarked contemporary fetish-formations. Taking the later chapters of Pietz’s volume, which focuses on capital and techno-political infrastructures, this essay suggests that non-fungible tokens, or “NFTs,” might be thought of as failed fetishes, objects that work to occlude the material infrastructure that supports blockchain, yet do not cohere because due to contemporary technology, they lack the sensuous materiality required by Pietz’s phenomenologically-shaped understanding of Marxism. Regardless of the correctness of this speculative claim about NFTs, this discussion suggests that Pietz’s original, groundbreaking argument on the fetish still has relevance to, and a great deal of creative promise for, analysis in the current day.
Cybernetics and Technology in Society
Blockchain Technology Applications and Security
Neuroethics, Human Enhancement, Biomedical Innovations
Distributed ledger technologies (DLT) usage is currently limited to a single platform as they do not have design-based interoperability capabilities. In general, it's challenging for a DLT to communicate with another one. Although several DLT solutions have been proposed and applied in specific application areas, building a general-purpose interoperability solution for any DLT remains a challenge. In previous work, we proposed a tailor-made interoperability solution between Hyperledger Fabric and Corda. This paper extends that work to enable interoperability between Hyperledger Fabric and Ethereum. The main contribution of this paper is to provide a new approach to enable interoperability between these two DLT. The approach follows a new request-response interaction model and future payments to enable the payments of services. A prototype was developed and evaluated through a case scenario, performance tests and cost analysis. Performance tests showed bottlenecks under heavy load scenarios due to Ethereum's design. Costs analysis showed that the approach is suitable for purchasing high-priced services. These promising results constitute a step forward in developing a general-purpose solution for DLT interoperability.
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. Resisting these technologies and their backers on all fronts also entails advancing a positive vision of justice and equality, doing “the work of creating the world we wish we live in” (Lewis et al. 2018: 83).The Alignment Problem is of the of ML systems and to the through improved technical are given and often such technical are both possible and in the Christian to the implicitly is enough to the social impacts of the ML applications not to the that are not easily or do not easily into our AI ethics that focus on technical and for values like are not well in to support such or broader about AI’s social Such technical work is but not to be at the of our about or AI systems should be and technical are often as the to AI’s by the same companies and from these systems in the first AI alignment is the wrong way to think about the broader questions at AI technologies are with the values and of and often Silicon Valley what is there for an AI ethics the of can live of in social and moral the AI ethics The of the term about the potential dangers of computing machines and of their by the and the In the out as a to the and Black to The AI who in developed one of the first computer the simulated in the book Computer and Human that of to be only by (13). In the fields such as the philosophy of technology and science and technology the social impacts of computing technologies, those used to human organizations like Computer for in the and Computer for in the 1980s the dangers of computing technologies used by the American and for and use of computing in public and policy The book The of on and Ethics to has never been possible to the uses of computing machines from their technical design. In an early computer and described three categories of defined as systematic and in computer its in social and technical from technical constraints or and from of These categories into one in the of and a and do a a to be As ML-based automated decision-making systems are used today to to social a of or predict an in the context of or hiring, where the and the ethical to this in and and the A to a aim is to help grapple with the ethical impacts of technologies. As suggested by its and the to describe a ethics for with like that humans should to our of in a and world as a branch of contemporary has several or ethics entails the or of the number of or ethics a to a normative of or ethics ethical or moral by of to a particular ethics involves of the of an and that as a of (Kazim and Koshiyama 2021: and have of ethics, in one of Johnson’s Computer Ethics the for to justice, and in ethical the ethical amounts to the or that be is more all and on their the that contemporary AI ethics discussions often on professional ethics codes or the of capitalist this critique of the field analysis of the of and ethics, to ethics to on other global ethical might be to the such as the philosophy of of 2020). the of these to develop a common for “the of moral This which terms is one that can to a for or practical in both the design and use of technologies. The elements of this include of moral and the of moral development “moral through ethical understanding and as existing within a of and interrogate their within it; and moral and of moral to others In these support through such as and of ethics in the context of technologies with in science and technology and critical how to design with human values in mind. et design for instance, is grounded in critical to to in their and design was in inspired by critical technical an to design AI developed by AI More and for to understand human values as to a to design. These scholars are of what might described as an implicit ethics within the philosophy of technology and critical design book has much to to these and is a of for around technologies, values, and the second of and the for to several areas of social and fields in which automated systems are that not all questions AI are ethical or ones about to live with AI, are for from or concerns Yet the of work values in technical design the of this about how to live well are to some by general of is in its cannot be from the and the collective, the under which social takes or ethical can have effects on the of to the broader as to live with AI perhaps more with the and of AI’s and normative an of that to that their machines different kinds of but that the social forms of and use of them.” and the with a on what of we to that technical are but not be in the for the of the can easily within uneasily and neoliberal capitalist is perhaps the most posed by and the both the for and in for in moral and for However, it is the of computing can human as agents of moral in the way the of AI systems human central to both and provides a study for the of ethics with today’s Computer science for many AI researchers did not human as an of or a of this The of is one of the books that even this emphasis on AI has been on and this for the research paradigms which both and philosophy of mind for much of the was focused on the of not ethics have with at the past two systems that data about human and to simulate it have become of recognition systems and automated like in AI is to this technical landscape and these technologies’ the of AI systems have sought to as many for as possible in their of AI on and virtual all these technologies use a particular or and and to about a The of human by researchers what can from this of This the of human terms this these technologies adapt and their The computational of should be understood in its as of both and of in of data by the or describes the of system as “a of which as understand by means of what we and Such simulated can by this definition of with in and the of or with with at least one of alignment presents or can it be the dangers of human “in terms that technology that the posed at the of that different from human on from have the but not the is grounded on what the to understanding what human described as this has for and that there a of in our and way to understand this is as or in this that our out into the world even as we to Such an with a second of on human which as or In this are grounded in a of and and in the of the sociologist a and can be and shaped to and the of on contemporary recognition technologies. that is in the to or not Yet do not, the of are human are or this to and to present as of an are or about our much more and industry the “the of by means The seem to be in a to a recent for the of systems grounded in et al. 2019).The of human to the ethics of and AI technologies. 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
Ethics and Social Impacts of AI
Neuroethics, Human Enhancement, Biomedical Innovations
Spanish Abstract: La presente aportación examina la tecnología blockchain y sus retos legales desde sus principales plataformas en atención a sus usuarios: Blockchain y Ethereum. Por ello, examina la adhesión en los contratos B2C mediante la plataforma Blockchain, y el papel de los consumidores 2.0 en esta plataforma. Asimismo, analiza la plataforma Ethereum, más especializada, y que permite personalizar un smart contract y sus prestaciones. Seguidamente, trata la responsabilidad civil por los errores de programación en la blockchainización de las prestaciones, y finaliza mediante un análisis de la protección de datos y la encriptación de datos de carácter personal desde una perspectiva jurídica. English Abstract: This contribution examines blockchain technology and its legal challenges from its main platforms for its users: Blockchain and Ethereum. It therefore examines adhesion in B2C contracts through the Blockchain platform, and the role of consumers 2.0 on this platform. It also analyses the more specialised Ethereum platform, which allows the customisation of a smart contract and its services. It then deals with civil liability for programming errors in the blockchainisation of services, and ends with an analysis of data protection and the encryption of personal data from a legal perspective.
Open access
2 source records
Blockchain Technology Applications and Security
Neuroethics, Human Enhancement, Biomedical Innovations
Cryptocurrencies, introduced in 2009 with the first cryptocurrency, Bitcoin, have grown significantly in recent years and attracted attention globally. One of the main characteristics of cryptocurrencies and their key innovation is that they are underpinned by distributed ledger technology (DLT) or blockchain as a type of DLT. This technology enables cryptocurrencies to be transferred, stored or traded electronically within DLT-based systems in a peer-to-peer manner among (pseudonymous) system participants across the world without the involvement of the usual central trusted authorities or intermediaries such as banks. This raises the question of if, and how, one should ascertain internationality for cryptocurrency transfers taking place within truly global systems underpinned by DLT for private international law purposes. This article aims to raise awareness of and address the question of internationality in the context of cryptocurrency transfers in DLT-based systems. It considers internationality in private international law, potential factors that might be relevant in ascertaining internationality for cryptocurrency transfers through a comparison to that for electronic funds transfers (EFTs), and the approaches of the International Institute for the Unification of Private Law (UNIDROIT) and the Hague Conference for Private International Law (HCCH) on internationality in their current projects concerning digital assets and digital economy respectively.
This paper explores the contemporary aesthetics of one of the latest forms of digital art known as non-fungible tokens, aka NFTs, and how and why they are affecting and shaping today’s society. Using Manovich’s theory of metamedium, Lanza’s theory of biocentrism, blockchain technology, and NFTs can be theorized as a form of medium which represents many other media while augmenting them with many new properties. The main theoretical problem with NFTs in the domain of art theory is the question of their originality. When put to use through various digital art collections and online tech platforms, the backend section of the blockchain smart contract code also becomes part of art, hence it can sometimes be difficult to define NFTs’ originality of the art itself. Due to the repetitive nature of NFTs, it can be argued that the most unique component of the NFT metamedium is not art, or blockchain smart contract, but the owner of the digital art piece. With the collection of 10,000 similar pieces of art, the originality of art is evolving through the ideology of cultural groups associated with desired collections. It is shifting from the art itself to the owner. Throughout the unification of technology, software, and art, artists have a new way to extend their creations, while actively participating in the shaping of the cultural landscape. This gives both creators and collectors of the NFT metamedium a brand-new transcending meta experience beyond the art itself that gives a unique point to the originality of the art piece. Article received: May 8, 2022; Article accepted: July 15, 2022; Published online: October 15, 2022; Original scholarly paper
Open access
Neuroethics, Human Enhancement, Biomedical Innovations
Ki Young Huh, Seol Ju Esther Moon, Sang-Un Jeong, Minji Kim · 10 authors
Blockchain is a novel data architecture characterized by a chronological sequence of blocks in a decentralized manner. We aimed to evaluate the real-world feasibility of a blockchain-based dynamic consent platform (METORY) in a decentralized and multicenter trial. The study consisted of three visits (i.e., screening and 2 follow-up visits) with a 2-week interval. Each subject was required to report the self-measured body temperatures and take a virtual investigational drug by entering the unique drug code on the application. To simulate real-world study settings, two major (i.e., changes in the schedule of body temperature measurement) and three minor protocol amendments (i.e., nonsignificant changes without any changes in the procedures) were set. Overall study completion rates, proportion of consent, and response time to each protocol amendment and adherence were evaluated. A total of 60 subjects (30 in each center) were enrolled in two study centers. All subjects completed the study, and the overall proportion of consent to each protocol amendment was 95.7 ± 13.7% (mean ± SD), with a median response time of 0.2 h. Overall, subjects took 90.8% ± 19.2% of the total drug, whereas compliance with the schedule was 69.1% ± 27.0%. Subjects reported 96.7% ± 4.2% of the total body temperature measurements whereas the adherence to the schedule was 59.0% ± 25.0%, which remarkably decreased after major protocol amendments. In conclusion, we evaluated a blockchain-based dynamic consent platform in real clinical trial settings. The results suggested that major changes should be avoided unless subjects' proper understanding is warranted.
Open access
Blockchain Technology Applications and Security
Neuroethics, Human Enhancement, Biomedical Innovations
In recent years, the development of digital educational resources has advanced by leaps and bounds, but the problem of copyright protection of educational resources has become increasingly prominent. Based on the phenomenon that it is difficult to solve the problems of copyright traceability, platform monopoly and transaction protection by existing digital resource protection methods, using the characteristics that it is difficult to tamper with the blockchain achieves the purpose of copyright traceability, using the decentralization feature realizes the resource antitrust function, and using the smart contract creates a secure trading environment. It is based on Spring Boot framework, Fabric distributed ledger, asymmetric encryption technology, IPFS and other technologies putting forward the design scheme of education platform based on blockchain technology. From the technical level, let the creators of educational resources be willing to share, dare to share educational resources, and let more learners obtain the knowledge they want.
Blockchain Technology Applications and Security
Neuroethics, Human Enhancement, Biomedical Innovations
This piece explores algorithmic governance as a strength and a vulnerability in the experience of building participatory communities known as "Decentralized Autonomous Organizations". The Cypherpunks were terrified of surveillance. They envisaged the combination of cryptography and computer technology fundamentally altering the nature of trust and reputation and built cryptographically secure blockchain-based infrastructure to counter this threat. Now, not just on chain transactions are being tracked but every move of participants in blockchain communities. Reputation in blockchain systems could become the new algorithmic authoritarianism if mis-used for social control. This piece analyzes the ways in which decentralization efforts can be a threat to themselves by exploring the question, 'Are "Decentralized Autonomous Organizations" (DAOs) the next panopticon of algorithmic governance or a different panacea, and what does this mean for human autonomy in "autonomous" systems?'. By employing ethnographic methods and case study analysis, this piece provides an important qualitative contribution to the early dynamics of the aspirations and problems of decentralized, autonomous organizations.
Open access
Blockchain Technology Applications and Security
Ethics and Social Impacts of AI
Neuroethics, Human Enhancement, Biomedical Innovations
2021 has witnessed a surge of interest in non-fungible tokens or “NFTs”. However, there is considerable misunderstanding surrounding what an NFTs is, what it may or may not represent, how rights attach, how it may be enforced or used for enforcement of rights, and how it is connected to the asset it purportedly represents. This is self-evident in discussions between the technology community, implementers, and the legal profession. This is compounded by a lack of clarity in how current legal frameworks work with or are applied to such tokens.
Digital Transformation in Law
Blockchain Technology Applications and Security
Neuroethics, Human Enhancement, Biomedical Innovations
Marielle S. Gross, Amelia Hood, Robert C Miller Jr
Henrietta Lacks' deidentified tissue became HeLa cells (the paradigmatic learning health platform). In this article, we discuss separating research on Ms Lacks' tissue from obligations to promote respect, beneficence, and justice for her as a patient. This case illuminates ethical challenges for the secondary use of biospecimens, which persist in contemporary learning health systems. Deidentification and broad consent seek to maximize the benefits of learning from care by minimizing burdens on patients, but these strategies are insufficient for privacy, transparency, and engagement. The resulting supply chain for human cellular and tissue-based products may therefore recapitulate the harms experienced by the Lacks family. We introduce the potential for blockchain technology to build unprecedented transparency, engagement, and accountability into learning health system architecture without requiring deidentification. The ability of nonfungible tokens to maintain the provenance of inherently unique digital assets may optimize utility, value, and respect for patients who contribute tissue and other clinical data for research. We consider the potential benefits and survey major technical, ethical, socioeconomic, and legal challenges for the successful implementation of the proposed solutions. The potential for nonfungible tokens to promote efficiency, effectiveness, and justice in learning health systems demands further exploration.
Open access
Ethics in Clinical Research
Biomedical Ethics and Regulation
Neuroethics, Human Enhancement, Biomedical Innovations
Abstract In this article, we identify three key design dimensions along which cryptocurrencies differ – privacy, censorship‐resistance, and consensus procedure. Each raises important normative issues. Our discussion uncovers new ways to approach the question of whether Bitcoin or other cryptocurrencies should be used as money, and new avenues for developing a positive answer to that question. A guiding theme is that progress here requires a mixed approach that integrates philosophical tools with the purely technical results of disciplines like computer science and economics.
Blockchain Technology Applications and Security
Ethics and Social Impacts of AI
Neuroethics, Human Enhancement, Biomedical Innovations
Panayiotis Christodoulou, Andreas S. Andreou, Z. Zinonos
Our epoch is continuously disrupted by the rapid technological advances in various scientific domains that aim to drive forward the Fourth Industrial Revolution. This disruption resulted in the introduction of fields that present advanced ways to train students as well as ways to secure the exchange of data and guarantee the integrity of those data. In this paper, a decentralized application (dApp), namely skillsChain, is introduced that utilizes Blockchain in educational robotics to securely track the development of students' skills so as to be transferable beyond the confines of the academic world. This work outlines a state-of-the-art architecture in which educational robotics can directly execute transactions on a public ledger when certain requirements are met without the need of educators. In addition, it allows students to safely exchange their skills' records with third parties. The proposed application was designed and deployed on a public distributed ledger and the final results present its efficacy.
Open access
2 source records
Blockchain Technology Applications and Security
IoT and Edge/Fog Computing
Neuroethics, Human Enhancement, Biomedical Innovations