AI Ethics on Blockchain: Topic Analysis on Twitter Data for Blockchain Security

Fu, Yihang; Zhuang, Zesen; Zhang, Luyao

doi:10.1007/978-3-031-37963-5_7

Cited by 11 publications

(3 citation statements)

References 9 publications

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“…At the same time, there are ethical questions which have arisen in the context of blockchain technology, requiring more attention due to their complexity [4]. Ethical issues like security and equity have gained attention [5]. The question has been posed as to whether there is a need for a "blockchain code of conduct" [6].…”

Section: Blockchain Technology-from An Intermediated Network To An Im...mentioning

confidence: 99%

Blockchain Ethics

Kirchschlaeger

2023

Philosophies

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There is no question about the innovation force and the economic potential of blockchain technology. As the basis for new currencies, financial services, and smart contracts, blockchain technology can be seen as the fifth disruptive computing paradigm, after mainframes, personal computers, the Internet, and mobile devices. However, there are questions about its ethical implications, which have the potential to also impact the economic success of blockchain technology. This article aims to provide ethical guidance on blockchain technology. In order to reach this goal, the focus of the ethical analysis will first concentrate on the unique characteristics of blockchain technology compared to other technology-based innovations. The unique nucleus of blockchain technology can be defined as a move from the trust in people to a trust in math, as a move from an internet of information to an internet of value, or—as I would propose—a shift from an intermediated network to an immediate network. Second, the ethical opportunities (e.g., transparency, participation, global access to services) and risks (e.g., ecological impact, lack of legal monitoring and enforcement) associated with this unique nucleus of blockchain technology will be discussed. Third, an outlook on possible concrete solutions will be provided.

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Section: Blockchain Technology-from An Intermediated Network To An Im...mentioning

confidence: 99%

Blockchain Ethics

Kirchschlaeger

2023

Philosophies

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“…Expanding the Dataset with Additional Network Attributes: While the current daily indices offer a robust foundation for empirical analysis within the DeFi ecosystem, it is evident that the breadth of our dataset can be broadened to include more comprehensive variables. These variables should encapsulate diverse facets of DeFi network systems, as referenced in existing literature, 31,33 including advanced network features, 4 intricate transaction fee mechanisms, 34,35 and the nuanced social sentiment dynamics [36][37][38] prevalent in DeFi networks. A particular area of interest for future investigations is the security dimension within these networks, as outlined in existing research.…”

Section: Future Research Directionsmentioning

confidence: 99%

Uniswap Daily Transaction Indices by Network

Chemaya,

Cong,

Joergensen

et al. 2023

Preprint

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Decentralized Finance (DeFi) is revolutionizing traditional financial services by enabling direct, intermediary-free transactions, thereby generating a substantial volume of open-source transaction data. This evolving DeFi landscape is particularly influenced by the emergence of Layer 2 (L2) solutions, which are poised to enhance network efficiency and scalability significantly, surpassing the existing capabilities of Layer 1 (L1) infrastructures. However, the detailed impact of these L2 solutions has been somewhat obscured due to a dearth of transaction data indices that can provide in-depth economic insights for empirical research. This study seeks to address this critical gap by conducting a comprehensive analysis of raw transactions sourced from Uniswap, a central decentralized exchange (DEX) within the DeFi ecosystem. The dataset encompasses an extensive collection of over 50 million transactions from both L1 and L2 networks. Additionally, we have curated a wide-ranging repository of daily indices derived from transaction trading data across prominent blockchain networks, including Ethereum, Optimism, Arbitrum, and Polygon. These indices shed light on crucial network dynamics, such as adoption trends, evaluations of scalability, decentralization metrics, wealth distribution patterns, and other key aspects of the DeFi landscape. This rich dataset serves as an invaluable tool, enabling researchers to dissect the complex interplay between DeFi and Layer 2 solutions, thus enhancing our collective understanding of this rapidly evolving ecosystem. Its notable contribution to the data science pipeline includes the implementation of a flexible, open-source Python framework, enabling the dynamic calculation of decentralization indices, customizable to specific research requirements. This adaptability makes the dataset particularly suitable for advanced machine learning applications, including deep learning, thereby solidifying its role as a critical asset in shaping Blockchain as the foundational infrastructure for the intelligent Web3 ecosystem.

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“…For example, Liu et al [4] and Zhang and Zhang [5] discussed the role of transaction fees in influencing cryptocurrency prices and wait times. Besides, the unregulated practice of front-running in decentralized exchanges, highlighted by Fu et al [6], is nonexistent in traditional markets. Furthermore, centralized and decentralized exchanges, with differing levels of decentralization, may exhibit different trading and risk profiles.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Cryptocurrency Market Risk on the Blockchain: An Empirical Study Using the ARMA-GARCH-VaR Model

Huang,

Wang,

Chen

et al. 2024

IEEE Open J. Comput. Soc.

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Cryptocurrency, a novel digital asset within the blockchain technology ecosystem, has recently garnered significant attention in the investment world. Despite its growing popularity, the inherent volatility and instability of cryptocurrency investments necessitate a thorough risk evaluation. This study utilizes the Autoregressive Moving Average (ARMA) model combined with the Generalized Autoregressive Conditionally Heteroscedastic (GARCH) model to analyze the volatility of three major cryptocurrencies-Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB)-over a period from January 1, 2017, to October 29, 2022. The dataset comprises daily closing prices, offering a comprehensive view of the market's fluctuations. Our analysis revealed that the value-at-risk (VaR) curves for these cryptocurrencies demonstrate significant volatility, encompassing a broad spectrum of returns. The overall risk profile is relatively high, with ETH exhibiting the highest risk, followed by BTC and BNB. The ARMA-GARCH-VaR model has proven effective in quantifying and assessing the market risks associated with cryptocurrencies, providing valuable insights for investors and policymakers in navigating the complex landscape of digital assets.

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AI Ethics on Blockchain: Topic Analysis on Twitter Data for Blockchain Security

Cited by 11 publications

References 9 publications

Blockchain Ethics

Blockchain Ethics

Uniswap Daily Transaction Indices by Network

Evaluating Cryptocurrency Market Risk on the Blockchain: An Empirical Study Using the ARMA-GARCH-VaR Model

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