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1,518 results · page 19 of 64
Deepak Dhillon, Diksha Diksha, Deepti Mehrotra
No abstract is available for this record.
Hakan Yilmazkuday
Abstract This study examines how global geopolitical risks , threats , and acts impact the daily returns of 10 major cryptocurrencies (BTC, ETH, USDT, XRP, BNB, USDC, BCH, DOGE, LTC, and ADA). The statistically significant results that are robust to the consideration of alternative model specifications and control variables suggest that there is strong evidence for (i) ETH, XRP, BNB and BCH responding negatively to the shocks of geopolitical risks , (ii) BTC, ETH, BNB, BCH, LTC and ADA responding negatively to the shocks of geopolitical threats , and (iii) all 10 cryptocurrencies not responding to the shocks of geopolitical acts . As these 10 cryptocurrencies do not respond positively to any of the three shocks in a robust and statistically significant way either, it is implied that none of them offer a reliable hedge against geopolitical risks.
Jason Scharfman
No abstract is available for this record.
Rebecca Rettig, Michael Mosier, Katja Gilman
Combating illicit financial activity in permissionless blockchain-based financial systems — referred to as “decentralized finance” or “DeFi” — has challenged regulators and policymakers. Traditional financial integrity laws and regulations, comprised of antimoney laundering (“AML”)/countering the financing of terrorism (“CFT”) and sanctions, attach to intermediaries, including, with respect to AML/CFT obligations, those intermediaries the Bank Secrecy Act (“BSA”) defines as “financial institutions.” The current laws, however, are not amenable to intermediary-less systems like DeFi. This paper proposes a framework (see Section III) to effectively detect, deter and prevent illicit financial activity in DeFi, while preserving the technology as permissionless, neutral infrastructure. The three-part proposal (1) sets forth a definition of “independent control” in order to identify smart-contract based financial protocols that do not constitute DeFi; (2) seeks to classify genuine DeFi protocols — neutral, decentralized software — as “critical infrastructure,” subject to oversight and security coordination by the Treasury Department’s Office of Cybersecurity and Critical Infrastructure Protection (“OCCIP”); and (3) suggests that new laws could require certain businesses that are (a) necessary to the transmittal of communications about DeFi transactions, (b) transmit a material portion of such communications and (c) offer the service as a business to take on additional illicit finance risk management practices, without becoming “financial institutions” subject to the BSA. This paper is intended to begin a meaningful conversation about how to achieve the policy goals of combating illicit financial activities while allowing for continued innovation in DeFi, a nascent technological sector.
Lee Song Haw Colin, P. Mohan, Jonathan Pan, Peter K. K. Loh
Smart contract vulnerabilities have led to substantial disruptions, ranging from the DAO attack to the recent Poolz Finance. While initially, the smart contract vulnerability definition lacked standardization, even with the advancements in Solidity, the potential for deploying malicious contracts to exploit legitimate ones persists. The Abstract syntax tree (AST), opcodes, and control flow graph (CFG) are the intermediate representations for Solidity contracts. In this paper, we propose an integrated and efficient smart contract vulnerability detection algorithm based on Multi-layer perceptron (MLP). We use feature vectors from the Opcodes and CFG for the machine learning (ML) model training. The existing ML-based approaches for analyzing the smart contract code are constrained by the vulnerability detection space, significantly varying Solidity versions, and no unified approach to verify against the ground truth. The primary contributions in this paper are (i) a standardized pre-processing method for smart contract training data, (ii) introducing bugs to create a balanced dataset of flawed files across Solidity versions using AST, and (iii) standardizing vulnerability identification using the Smart Contract Weakness Classification (SWC) registry. The ML models employed for benchmarking the proposed MLP, and a multi-input model combining MLP and Long short-term memory (LSTM) in our study are Random forest (RF), XGBoost (XGB), Support vector machine (SVM). The performance evaluation onreal-timesmart contracts deployed on the Ethereum Blockchain show an accuracy of up to 91% using MLP with the lowest average False Positive Rate (FPR) among all tools and models, measuring at 0.0125.
Burcu BAYTEMİR KONTACI
The phenomenon of crypto money stands before us as a new type of asset that has emerged in parallel with the development of technology in recent years. The asset type in question covers all forms of currency that exist digitally or virtually and uses cryptography to secure transactions. It is possible to list the features of cryptocurrencies as decentralization, anonymity, protection with the blockchain system, fast transaction ability, irreversibility of the transaction, and difficulty in analyzing the code chain of the transactions among others. Since cryptocurrency is a digital asset designed to be used as a means of exchange and especially due to its features such as real-world identities being concealed and value transfer being possible through different methods, it is a tool that has various advantages and disadvantages for those who commit complex crimes such as money laundering and financing of terrorism. There are various difficulties arising from the nature of cryptocurrencies in detecting and revealing crimes in which cryptocurrencies are used, identifying the perpetrators of these crimes, and then ensuring that these perpetrators are convicted with legally obtained evidence. Issues such as decentralization, anonymity, and difficulty in cracking the code are the main reasons for these difficulties. In addition, the inadequacy of national legislation regarding the search and seizure of cryptocurrencies and the knowledge and technical deficiencies of police forces, prosecutors and judges also disrupts the investigation and prosecution processes. However, it is known that through investigations and trials carried out in states such as the United States of America, various European states and Israel, cases in which cryptocurrency was used have been revealed and trials are ongoing. In the context of all these developments, this study will focus on the problem of using cryptocurrencies as a terrorist financing tool. The use of cryptocurrencies for terrorist purposes, and especially to finance terrorist organizations, their members, or actions, is an issue that is increasingly encountered on an international scale and is being tried to be solved. In this context, the study will draw attention to the processes and methods related to the financing of terrorism, the settlement of cryptocurrencies, the acquisition, transfer, and conversion of cryptocurrencies into fiat money, and the issues related to their investigation and prosecution. In the study, the question of how ISIS, HAMAS and Al Haqiqa, organizations included in the terror lists of many countries and judicially recognized as such, use cryptocurrencies in financing terrorism will be discussed, and the current situation in Turkey, possible risks and solution suggestions will be discussed in the light of data available within the scope of research. In addition, the regulations regarding the financing of terrorism and cryptocurrencies in Turkish law will be evaluated all together and an evaluation will be made in the light of international and comparative examples.
Mohammad Mustafa Ibrahimy, Alex Norta, Peeter Normak
Corruption and lack of transparency remain critical challenges in governance systems around the world. These issues are often perpetuated by centralized systems and their manipulation by system administrators. Furthermore, the lack of data ownership and the monetization of user data by tech companies further increase concerns about transparency. In light of these concerns, this study aims to review existing blockchain-based governance models and identify best-practice governance models focusing on corruption transparency, their characteristics, and components. The research will also examine the role of a token economy in addressing trusted third-party issues related to asset ownership management. Furthermore, we discuss the effect of smart contracts, blockchain, decentralized autonomous organizations (DAO), Web 3.0, and multifactor challenge set self-sovereign identity authentication (MFSSIA) as modern technologies to combat corruption and achieve transparency in the public sector. To achieve this, we conduct a systematic literature review (SLR) comprising peer-reviewed journals, proceedings, and book chapters published between 2012 and 2023. Using the SLR methodology, 45 primary and supporting studies have been selected for result extraction and analysis. Finally, we discovered seven blockchain-based governance models with their characteristics and primary components.
Kuldeep Singh, K P Premalatha, Seema Benakatti, Vasudha Srivatsa
Blockchain technology stands out for its cryptographic connections between chronologically ordered records, forming the core of integrity within the expansive blockchain ecosystem. Leading the charge in this blockchain revolution are digital currencies, reshaping the financial landscape with seamless peer-to-peer transactions. As trailblazers in the realm of blockchain, digital currencies have catalyzed a shift towards decentralized, trust-based financial systems, fundamentally reimagining the global financial stage. This research embarks on an exploration at the intersection of Google Trends analytics and blockchain-based smart contracts. Its objective is to transform and fortify financial transactions within the digital banking sector. Moreover, the study offers a third-person perspective, unveiling valuable insights and recommendations for key stakeholders, such as financial institutions, regulators, and policymakers. These policy directives aim to promote greater financial inclusion, bolster security measures, and cultivate trust within the digital banking realm.
Ismail Alarab, Simant Prakoonwit
Abstract Money laundering has urged the need for machine learning algorithms for combating illicit services in the blockchain of cryptocurrencies due to its increasing complexity. Recent studies have revealed promising results using supervised learning methods in classifying illicit Bitcoin transactions of Elliptic data, one of the largest labelled data of Bitcoin transaction graphs. Nonetheless, all learning algorithms have failed to capture the dark market shutdown event that occurred in this data using its original features. This paper proposes a novel method named recurrent graph neural network model that extracts the temporal and graph topology of Bitcoin data to perform node classification as licit/illicit transactions. The proposed model performs sequential predictions that rely on recent labelled transactions designated by antecedent neighbouring features. Our main finding is that the proposed model against various models on Elliptic data has achieved state-of-the-art with accuracy and $$f_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>f</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> -score of 98.99% and 91.75%, respectively. Moreover, we visualise a snapshot of a Bitcoin transaction graph of Elliptic data to perform a case study using a backward reasoning process. The latter highlights the effectiveness of the proposed model from the explainability perspective. Sequential prediction leverages the dynamicity of the graph network in Elliptic data.
Jiajing Wu, Dan Lin, Qishuang Fu, Shuo Yang · 7 authors
With the overall momentum of the blockchain industry, financial crimes related to blockchain crypto-assets are becoming increasingly prevalent. After committing a crime, the main goal of cybercriminals is to obfuscate the source of the illicit funds in order to convert them into cash and get away with it. Many studies have analyzed money laundering (ML) in the field of the traditional financial sector. However, in terms of the emerging blockchain crypto-asset ecosystem, there is currently only one public anti-money laundering (AML) dataset for Bitcoin– the Elliptic dataset, whose binary labels (licit vs. illicit transactions) cannot cover the ML behaviors in the evergrowing crypto-asset market. To fill this gap, in this paper, we propose a framework named XBlockFlow which identifies ML addresses starting from Ethereum heist incidents and obtains the first detailed Ethereum ML dataset named$\textit {EthereumHeist}$, and then conducts a comprehensive feature and evolution analysis on the$\textit {EthereumHeist}$dataset according to the three main phases of ML. We first search for the source cybercriminal accounts including exchange hackers, DeFi exploiters, and scammers. Then, employing the idea of taint analysis, we track the diverse downstream transactions and addresses layer by layer. At the end of tracking, we identify and categorize service providers, and go a step further to investigate advanced ML methods that do not exist in the Bitcoin scenario, e.g. token swap and counterfeit token creation. Based on the ML identification results, we obtain many interesting findings about crypto-asset money laundering, observing the escalating money laundering methods such as creating counterfeit tokens and masquerading as speculators.
Sergio Luis Náñez Alonso, Miguel Ángel Echarte Fernández, David Sanz Bas, Cristina Pérez Pérez
ABSTRACT The objective of this article is to analyze the two most important monetary laws that have been implemented in El Salvador, namely the Monetary Integration Law (MIL) and the Bitcoin Law. The most important articles of both laws will be analyzed, as well as the consequences of dollarization, and the possible advantages and risks associated with the adoption of Bitcoin as legal tender. Although this measure may have some positive aspects by encouraging financial innovation and facilitating remittances, the macroeconomic risk is very high due to the volatility of this cryptocurrency. So far no positive results have been achieved as the acceptance has been very low and there has been a depreciation of the asset in recent months.
Zixuan Liu, Yirui Bai, Tiankai Xu, Haoyu Gao · 5 authors
Due to the rapid growth of Non-Fungible Tokens (NFTs), the current total market value of worldwide NFTs is at 1.05 billion dollars. The massive trading market increases the issue of blockchain supervision — transactions employing NFT to money laundering occur on a regular basis. However, most of the current domestic platforms do not support trading in the secondary market of digital collectibles, while there is a lack of foreign research on the regulation of NFT trading. The economic benefits of NFT have considerable potential to greatly enliven the market. The main purpose of this paper is to study the regulation of NFT ecology. Therefore, we provide a regulatory framework for NFT that realizes ex-ante regulation, and study two major modules of money laundering detection and regulatory control for NFT. In addition, to enhance compatibility, we also provide alternative ways to the proposed framework, aiming to provide new standards and guidance on the regulation of NFTs.
Chia-Cheng Tsai, Cheng-Chieh Lin, Shih-Wei Liao
Decentralized Autonomous organizations (DAOs) have emerged as blockchain technology evolves beyond cryptocurrencies. Despite being the first project in this ecosystem, The DAO encountered a significant exploit due to inadequate implementation; nevertheless, it still paved the way for future projects. While decentralized autonomous organizations continue to thrive, there is a shortage of academic papers analyzing the associated risks. Therefore, this paper aims to comprehensively examine the current vulnerabilities in these organizations by systematically analyzing past attack incidents. 54 real-world events spanning from 2016 to July 2023 have been collected for identifying and summarizing major attack vectors. The results showcase that flash loan attacks, oracle manipulation, governance takeovers, and reentrancy issues are the critical vulnerabilities within this field. For further protection, this research also provides both general and specific countermeasures against each vulnerability, serving as an evaluation framework for both existing and future projects.
Yunmei Yu, Jiajing Wu, Dan Lin, Qishuang Fu
With the continuous evolution of blockchain technology, cryptocurrency platforms such as Ethereum have emerged as centers for digital asset transactions and smart contracts deployment. However, this nascent financial ecosystem also introduces potential money laundering risks. The traditional financial industry has accumulated significant antimoney laundering (AML) experience and technical means for monitoring and detecting money laundering activities. Yet, the adaptability of these established AML algorithms in the context of blockchain remains unclear. This paper aims to investigate the practical adaptability of traditional AML algorithms on Ethereum data through empirical experiments. We gather eight real-world money laundering case datasets collected from Ethereum and conduct experiments using three traditional AML algorithms on these datasets. We evaluate the performance of these algorithms from various angles, including precision, recall, and the distribution of detected accounts' labels in comparison to the original datasets. It turns out algorithms demonstrate distinct performance in diverse money laundering cases, indicating that the adaptability of traditional AML algorithms on Ethereum data presents certain adaptability and limitations. Holoscope's accuracy demonstrates the value of dense subgraph properties in Ethereum money laundering detection, and further research can be conducted based on this model framework combined with the money laundering characteristics of Ethereum. Our study provides valuable insights for strengthening AML mechanisms on blockchain platforms and offers guidance for further research on detecting money laundering accounts in blockchain environments.
Anastasia Kassiani Blitsi, Georgios Stavropoulos, Konstantinos Votis
This research delves into the dual nature of cryptocurrencies, offering financial opportunities while addressing the surge in digital criminal activities, especially in illegal firearms trafficking. The decentralized nature of blockchain technology presents unique challenges for law enforcement, necessitating innovative approaches to uncover and prevent criminal transactions. The study utilizes advanced data mining, analytics techniques, and machine learning models to analyze transactional graphs of prominent cryptocurrencies, aiming to identify and thwart transactions linked to illegal firearms trafficking.Additionally, the paper provides an overview of the current state of blockchain technology research and introduces the ambitious Ceasefire project. This initiative outlines a systematic approach to combat illegal firearms trading within the cryptocurrency domain, leveraging cutting-edge techniques and strategic partnerships with leading blockchain analysis platforms.By proposing a novel method, this paper enhances the ability to detect illicit firearms trading in cryptocurrencies, specifically focusing on Bitcoin and Ethereum networks. The approach combines predictive modeling with rule-based matching to identify potentially suspicious addresses in both ecosystems. This empowers authorities to track individuals attempting to conceal their transactional activities by transitioning between Bitcoin and Ethereum, thus bolstering efforts to maintain the integrity of decentralized financial systems.
Hou-Wan Long, Xiongfei Zhao, Yain‐Whar Si
Decentralized Finance (DeFi), propelled by Blockchain technology, has revolutionized traditional financial systems, improving transparency, reducing costs, and fostering financial inclusion. However, transaction activities i n these systems fluctuate significantly and the throughput can be effected. To address this issue, we propose a Dynamic Mining Interval (DMI) mechanism that adjusts mining intervals in response to block size and trading volume to enhance the transaction throughput of Blockchain platforms. Besides, in the context of public Blockchains such as Bitcoin, Ethereum, and Litecoin, a shift towards transaction fees dominance over coin-based rewards is projected in near future. As a result, the ecosystem continues to face threats from deviant mining activities such as Undercutting Attacks, Selfish Mining, and Pool Hopping, among others. In recent years, Dynamic Transaction Storage (DTS) strategies were proposed to allocate transactions dynamically based on fees thereby stabilizing block incentives. However, DTS’ utilization of Merkle tree leaf nodes can reduce system throughput. To alleviate this problem, in this paper, we propose an approach for combining DMI and DTS. Besides, we also discuss the DMI selection mechanism for adjusting mining intervals based on various factors.
Xueqing Li, Junjie Liu, Xiarun Chen, Qingfeng Zhang
Before deploying smart contracts to Ethereum, a crucial step is to review the contract code for potential security vulnerability. Symbolic execution is currently a prevalent method for detecting vulnerability in smart contracts, but it lacks robust support for arbitrary modifications by owners. Therefore, this paper, leveraging symbolic execution technology, investigates detection methods specifically addressing this type of vulnerability, and presents concrete implementation and experimental validation. In the initial phase, this paper conducts an in-depth study of vulnerable contracts by debugging the source code and Ethereum Virtual Machine (EVM) opcode instructions. The analysis encompasses opcode instructions and the contract’s global state, summarizing vulnerability characteristics, extracting crucial opcode instructions. Subsequently, based on symbolic execution technology, the paper proposes corresponding detection methods. Real-world smart contracts are employed in this study, categorized into a dataset of vulnerable contracts susceptible to attacks and a dataset of normal contracts. Experimental evaluations are conducted to assess the effectiveness and accuracy of the system’s detection capabilities. The results indicate that the system implemented in this paper achieves the intended design goals and enhances the efficiency of vulnerability detection.
Seden Akcinaroglu, Moyan Shi
The nexus between economic motivations and terrorist activities has been extensively theorized, but existing explanations often overlook the contemporary shifts in terrorist financing ushered in by technological advancements. The advent of cryptocurrencies, with their hallmarks of anonymity, decentralization, liquidity, usability, and profitability, has bestowed upon terrorist groups new avenues for raising funds while remaining largely clandestine. This exploratory research delves into the repercussions of this digital financial realm, examining how Bitcoin popularity, price volatility, and regulatory frameworks influence the operational latitude of terrorist groups. Drawing on the Global Terrorism Database (GTD) for terrorist attack data, coupled with Google Trends data on “bitcoin” searches from 2009 to 2020 as an indicator of cryptocurrency popularity in each country, the study uncovers a nuanced dynamic. While the rising prominence of cryptocurrencies subtly amplifies the operational sphere for terrorist outfits, the uncertain nature of Bitcoin prices as well as the legal and regulatory landscape act as deterrents.
E. L. Sidorenko
Objective : to increase the effectiveness of countering the use of typical criminal money laundering schemes by identifying the technological and legal vulnerabilities of the DeFi infrastructure: decentralized exchanges, blockchain bridges, decentralized wallets, and privacy-enhanced currencies. Methods : general scientific (analysis and synthesis, induction and deduction, theoretical modeling, legal interpretation) and special methods of scientific cognition (structural-functional, constructive, situational, innovative, target-oriented, program-target, and risk-oriented). Results : the main trends in the development of money laundering using decentralized finance were outlined; the determinative significance of DeFi technological characteristics in the genesis of money laundering was revealed; the main types of money laundering using decentralized finance were identified; the schemes of committing crimes were studied and the criminogenic potential of DeFi infrastructure (decentralized exchanges, blockchain bridges, mixers, privacy-enhanced tokens, etc.) was assessed. Scientific novelty : it is proposed to consider money laundering using DeFi as a special type of digital financial crime. The article proposes the author’s typology of money laundering, substantiates the idea that the matrix of traditional financial regulation and AML standards cannot be applied to decentralized finance. It is argued that prevention of money laundering using decentralized finance should be carried out in close connection with the identification of risk indicators and the development of effective control measures at the points of entry of criminal incomes to centralized exchanges. Practical significance : the analysis of typical mechanisms of money laundering using DeFi allows a systematic approach to the organization of early crime prevention and can potentially become the basis to develop recommendations for financial intelligence and monitoring services.
Alper Uyumaz, Enku Tensay Woldemaryam
Abstract The article analyses the current issues contributing to the volatility of Bitcoin as the reliability of this new technology diminishes, leading to increased unpredictability of its value. Legal efforts and literature regarding Bitcoin have primarily focused on protecting society from the illegal use of this digital technology, with little emphasis on integrating it as an asset. However, this article proposes that countries adopt Bitcoin-related legislation, incorporating recognition and regulation clauses to transform Bitcoin into a stable, less volatile and functional digital asset. In the context of legal history, primary legal domains, such as contracts, family, trade and others, have been integrated through recognition and regulation processes. Therefore, we argue that adopting Bitcoin-specific legislation that recognizes this new technology while comprehensively regulating the associated risks would enhance the coin's stability and reduce volatility, ultimately increasing trust among digital investors and users.
Anatolii Movchan, Oleksandr Shliakhovskyi, Vasyl Kozii, Ihor Fedchak
The article is devoted to the study of the problems of investigating crimes of financing terrorism and armed aggression with cryptocurrency, which is relevant considering the attack on Ukraine by the Russian Federation, as well as in connection with the significant spread and use of cryptocurrency for financing both terrorism and armed aggression. The purpose of the article is to study the problems of investigating crimes of cryptocurrency financing of terrorism and armed aggression and finding ways and means of solving problematic issues, because cryptocurrency financing of terrorism and armed aggression is an encroachment on national security. The methods of system analysis and technical- legal analysis, as well as the formal-logical method, were used in the research process. Thanks to this, approaches to understanding the way of committing crimes of the researched category have been determined. The shortcomings in the legal regulation of the circulation and use of cryptocurrency in Ukraine, as well as in the legal regulation of the investigation of crimes related to the illegal acquisition and use of cryptocurrency for criminal purposes, including for the financing of terrorism and armed aggression, are highlighted. Jurisdictional problems of criminal prosecution of persons who committed crimes of this category, their high latency due to the lack of proper legal procedures and methods of investigation, have been determined. The need to create specialized units in law enforcement agencies, whose competence will include the detection and investigation of the specified crimes, their active interaction with the Cyber Police, is substantiated. The attention and necessity of introducing a system of constant monitoring of social networks, the Internet, and media and conducting OSINT-intelligence from open sources with the aim of detecting and stopping such criminal activities, tracking and arresting and eventually seizing cryptocurrency, if such an opportunity is available, was emphasized. Practical recommendations for the investigation of crimes of cryptocurrency financing of terrorism and armed aggression have been formulated. The need for international legal cooperation in this area was emphasized; the need to involve specialists in the field of information technologies, programming, and blockchain engineering in the investigation process in general and in specific investigative actions. The requirements for the recording of evidence in the protocols of investigative (search) actions during the investigation of crimes of this category are formulated, in particular, the need for hashing of files is specified. The practical significance of the study is that the obtained results can be used during the investigation of crimes of the studied category
Yu Zhou, Shang Gao, Weiwei Qiu, Kai Lei · 5 authors
Though designed with security in mind, blockchains are vulnerable to various kinds of attacks, especially when the network computational power is low. Selfish mining is one of the most rudimentary and notorious attacks, which maliciously renders blocks found by honest miners orphaned by strategically withholding and revealing the found blocks. In this paper, we analyze the profitability of selfish mining under the checkpoint mechanism—a mechanism that has been adopted as a finality gadget by many blockchains like Ethereum and Bitcoin Cash. We develop a rigorous analysis method and conduct quantitative evaluations in various scenarios to explore the mechanism's suppression effect on selfish mining. The results illustrate that the checkpoint mechanism can restrict the profit of selfish mining and increase the threshold of computational power that makes selfish mining profitable, suggesting that it is a practical defense mechanism against selfish mining.
Qin Wang, Shange Fu, Shiping Chen, Jiangshan Yu
No abstract is available for this record.
Javier Sandoval Archila
Este artículo explora los desafíos de la gobernanza algorítmica utilizando el estudio de caso de The DAO, una efímera tentativa de crear una organización autónoma descentralizada en la plataforma de blockchain Ethereum. A pesar de su breve existencia y la significativa pérdida de inversión debido a una explotación de seguridad, The DAO ofrece ideas críticas sobre las formas emergentes de autoridad algorítmica, la gobernanza práctica de sistemas autónomos y descentralizados, y las posibles fallas en el diseño de incentivos y la modelización de acciones. El artículo también profundiza en el problema de agencia en economía y la gobernanza corporativa, ilustrando cómo estos conceptos se entrelazan con la gobernanza algorítmica.