Blockchain Papers

Yuan Chen, Yunting Feng, Kee‐hung Lai, Qinghua Zhu

No abstract is available for this record.

D. Vinodha, M. Buvana, S. Rajalakshmi, J. Jenefa · 6 authors

Agriculture plays an important part in most countries, such as India. A survey says that 54.6% of the total labor force of India is engaged in agriculture and its connected activities. The government is announcing many schemes to facilitate agriculture and support farmers. But most of the farmers are from poor families and are not able to reach the government schemes when they are really in need. Also, it is required to observe and measure the inter and intra-field variability in crops to enjoy the complete benefits of government schemes. This can be done with the advancements in the field of the Internet of Things. Information related to the impact of natural calamities on the agricultural field, malfunctions in the machinery used for cropping, yielding level, and health status of crops can be measured using the technology of IoT (Internet of Things) and analyzed using AI (Artificial Intelligence). Blockchain plays a critical role in replacing traditional means of data storage and exchanging agricultural data with a more trustworthy, immutable, transparent, and decentralized approach. By keeping all the transactions related to government schemes in blockchain, the possible crimes in the form of false data by the intermediate dealers acting between the farmers and the government can be addressed. This, in turn, allows useful government schemes to reach the farmer in time. We propose to develop a theoretical model using IoT, AI, and blockchain, which can assist the farmers in benefitting from the appropriate schemes announced by the government in time and achieving precise agriculture.

Eason Chen, Xinyi Tang, Zimo Xiao, Chuangji Li · 8 authors

The vision of Web3 is to improve user control over data and assets, but one challenge that complicates this vision is the prevalence of non-transparent, scam-prone applications and vulnerable smart contracts that put Web3 users at risk.While code audits are one solution to this problem, the lack of smart contracts source code on many blockchain platforms, such as Sui, hinders the ease of auditing.A promising approach to this issue is the use of a decompiler to reverse-engineer smart contract bytecode.However, existing decompilers for Sui produce code that is difficult to understand and cannot be directly recompiled.To address this, we developed the SuiGPT Move AI Decompiler (MAD), a Large Language Model (LLM)-powered web application that decompiles smart contract bytecodes on Sui into logically correct, human-readable, and recompilable source code with prompt engineering.Our evaluation shows that MAD's output successfully passes original unit tests and achieves a 73.33% recompilation success rate on real-world smart contracts.Additionally, newer models tend to deliver improved performance, suggesting that MAD's approach will become increasingly effective as LLMs continue to advance.In a user study involving 12 developers, we found that MAD significantly reduced the auditing workload compared to using traditional decompilers.Participants found MAD's outputs comparable to the original source code, improving accessibility for understanding and auditing non-open-source smart contracts.Through qualitative interviews with these developers and Web3 projects, we further discussed the strengths and concerns of MAD.MAD has practical implications for blockchain smart contract transparency, auditing, and education.It empowers users to easily and independently review and audit non-open-source smart contracts, fostering accountability and decentralization.Moreover, MAD's methodology could potentially extend to other smart contract languages, like Solidity, further enhancing Web3 transparency.

Kamran Eshghi, Samira Farivar

Despite the growing adoption of cryptocurrencies as a payment method, the literature lacks a comprehensive exploration of its performance outcome. To address this gap, authors examine the impact of firms’ adoption of cryptocurrencies as a payment method on shareholder value. Employing event study methodology, authors analyze the effect of cryptocurrency adoption announcements made by 27 U.S. firms between 2013 and 2020 on shareholder value. The results indicate that cryptocurrency adoption leads to positive abnormal returns, with an average of 0.65% on the announcement day. Further analysis reveals that firms’ advertising intensity amplifies the positive impact of cryptocurrency adoption on shareholder value. Additionally, non-retail firms tend to receive greater benefits from cryptocurrency adoption compared to retail firms.

Raj Maurya, M. Sanjoy Singh, Sukanta Kumar Baral, Kaushal Kumar

The emergence of the metaverse presents unprecedented opportunities for businesses, particularly within the accounting sector, by enabling immersive, interactive digital environments. This study explores the transformative role of metaverse technology in accounting business operations, addressing its potential and the associated challenges. Integrating metaverse solutions within accounting can revolutionize financial analysis, auditing, and client interactions. The metaverse offers a platform to enhance the goodwill of IT firms, manage NFT (non-fungible tokens), and sales expenses and improve customer experiences. However, practical implementation faces hurdles such as data security, technological adaptability, cost concerns, and a significant learning curve for professionals. This research investigates these obstacles and proposes strategic frameworks for adopting metaverse technology in accounting. The study also assesses the future impact of metaverse integration on business operations, suggesting that, despite the challenges, the metaverse can reshape traditional accounting practices. Ultimately, the findings contribute to a roadmap for accounting firms and professionals seeking to harness the benefits of metaverse technology in a rapidly evolving digital landscape. The study concludes that metaenvironment businesses' reporting structure uses accounting to access all significant financial activities virtually.

Zakaria El Rhadiouini, Zahra Oughannou, Nour El Houda Mejhed Chaoui, Habiba Chaoui

As the metaverse evolves into an all-encompassing digital ecosystem, ensuring the security and uniqueness of digital identities and assets becomes paramount. This paper critically examines the identity security challenges in the metaverse, particularly focusing on the unicity problem. We propose an integrated solution leveraging the synergistic capabilities of Artificial Intelligence (AI), blockchain technology, and Non-Fungible Tokens (NFTs). Our novel contribution lies in the multifaceted approach that combines AI for sophisticated identity verification and anomaly detection, blockchain for decentralized and immutable record-keeping, and NFTs for establishing verifiable ownership and authenticity of digital assets. This robust framework addresses the pressing need for enhanced security measures in the metaverse, not only mitigating current security risks but also paving the way for future advancements in digital identity management. Our comprehensive analysis and proposed methodology provide a significant contribution to the ongoing discourse on metaverse security, highlighting both the potential and challenges of integrating these emerging technologies. This paper serves as a foundational work for future research and development in the secure management of digital identities and assets within the metaverse.

Jayesh Rane, Ömer Kaya, Suraj Kumar Mallick, Nitin Liladhar Rane

This systematic literature review aims to discuss how digital transformation and digitalization have influenced businesses and management by synthesizing the latest research trends and findings. The digital transformation, defined as the integration of digital technologies in all business areas, has reshaped conventional business models, operational processes, and value propositions. On the other side, digitalization is a subcategory of digital transformation, referring to a process for the conversion of information from an analogue into a digital format for the automation and optimization of business processes. The review brings out that digital transformation is no longer a technological pursuit but a strategic compulsion impacting organizational culture, leadership, and customer engagement. It is found from emerging trends that only those businesses which are using sophisticated technologies such as Artificial Intelligence, Blockchain, Big Data Analytics, Cloud Computing, and Internet of Things are now gaining competitive advantage through resilience, innovation, and customer-centricity. This research calls for a holistic approach to the integration of technology into the strategic vision with organizational change management for successful digital transformation.

Victoria Vysotska, Kirill Smelyakov, А. В. Наумов, Valentina Shtanko

The object of the research is forecasting the exchange rate of cryptocurrencies using machine learning algorithms. This work aims to determine the most effective methods of machine learning among the selected ones for the best forecasting of the cryptocurrency exchange rate using the analysis of news sentiment estimates.

Shubhangi Gautam, Pardeep Kumar, Preeti Dahiya

Purpose : The current study examined the influence of neurotransmitters on cryptocurrency investment choices. Moreover, the research assessed the mediator risk tolerance (RT) and moderator investment experience on the connection between neurotransmitters and investment choices.Research Design/Methodology : The analysis of the data involved 504 responses from individuals in India’s Western and Northern regions who either invested in cryptocurrencies or had knowledge of such investments. The proposed theoretical model of cryptocurrency investment choices was examined in the study using “variance-based partial least square structural equation modeling” (PLS-SEM).Findings : The outcomes of this research specified that neurotransmitters play a substantial role in cryptocurrency investment choices, and they had a substantial impact on making investment choices. It was also noted that a significant moderator between neurotransmitters and Bitcoin investment decisions is RT. However, it was determined that investment experience had no moderating effect.Practical Implications : This study revealed that, in order to make better-informed investment decisions, businesses, governments, and investors should consider the impact of neurotransmitters.Value/Originality : The study was innovative since it is one of the first to examine how neurotransmitters, together with mediator RT and moderator investment experience, affected Bitcoin investment decisions. Additionally, the conceptual framework could be very helpful to cryptocurrency portfolio managers and investors in understanding how the brain functions during the decision-making process. They would then be better equipped to allocate their assets with knowledge and efficiency.

Nitin Liladhar Rane, Ömer Kaya, Jayesh Rane

Integration of Internet of Things (IoT) and blockchain combined with the power of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are transforming the sphere of smart industries, propagating a new era of boosted productivity, information assurance, and data-influenced deliberation. Our research looks into how these cutting-edge technologies flow together to enable smart industry breakthroughs. This offers conductive connectiveness and communication capabilities between devices and can create large pools of data, that are essential for making more informed decisions and finally, operating more sustainably. This data is then scaled and processed by the AI ML and DL algorithm to get the predictive insights; process optimization and to improve on automation. The security and immutability of data are critical in an IoT network, and this is something that blockchain technology excels at and ensures data exchanged within these networks is safe and unalterable. Thanks to recent developments in AI, ML, and DL, they can now better meet the challenges of industrial applications well beyond predictive maintenance and supply chain optimization and extend into real-time monitoring and autonomous operations. The perspective taken in this research is instead one of a practical, real-world implementations, illustrating some of the advantages as well as challenges when integrating these technologies. The results point to the enormous transformative capability of this integration and suggest a level of efficiency, security and innovation not seen before that will redefine intelligent industries today and possibly more importantly tomorrow, in effect defining the fourth industrial revolution and beyond.

A.F. Romero, Roberto Hernández

This article presents a Blockchain-based solution for the management of multipolicies in insurance companies, introducing a standardized policy model to facilitate streamlined operations and enhance collaboration between entities. The model ensures uniform policy management, providing scalability and flexibility to adapt to new market demands. The solution leverages Merkle trees for secure data management, with each policy represented by an independent Merkle tree, enabling updates and additions without altering existing policies. The architecture, implemented on a private Ethereum network using Hyperledger Besu and Tessera, ensures secure and transparent transactions, robust dispute resolution, and fraud prevention mechanisms. The validation phase demonstrated the model’s efficiency in reducing data redundancy and ensuring the consistency and integrity of policy information. Additionally, the system’s technical management has been simplified, operational redundancies have been eliminated, and privacy is enhanced.

K. Archana, V. Kamakshi Prasad, Maram Ashok

This chapter explores the intersection of Artificial Intelligence (AI), blockchain Technology, and cryptocurrency in the finance industry. The integration of AI in finance has revolutionized risk assessment, fraud detection, trading strategies, and customer service. Blockchain technology, coupled with cryptocurrencies, offers decentralized and transparent systems for secure and efficient transactions. The combination of AI and blockchain enables real-time transaction monitoring, fraud detection, and automated smart contracts. Furthermore, AI algorithms can analyze cryptocurrency market trends, predict price movements, and enhance investment strategies. However, challenges related to security, privacy, and regulatory compliance arise with this convergence. This chapter discusses specific use cases, such as AI-powered risk assessment models and blockchain-based identity verification, and analyzes the impact on traditional financial institutions and regulatory frameworks. The insights presented aim to guide stakeholders in harnessing the transformative potential of AI, blockchain, and cryptocurrency in the finance industry.

George Lăzăroiu, Tom Gedeon, Elżbieta Rogalska, Katarína Valášková · 17 authors

Research background: Generative artificial intelligence (AI) and machine learning algorithms support industrial Internet of Things (IoT)-based big data and enterprise asset management in multiphysics simulation environments by industrial big data processing, modeling, and monitoring, enabling business organizational and managerial practices. Machine learning-based decision support and edge generative AI sensing systems can reduce persistent labor shortages and job vacancies and power productivity growth and labor market dynamics, shaping career pathways and facilitating occupational transitions by skill gap identification and labor-intensive manufacturing job automation by path planning and spatial cognition algorithms, furthering theoretical implications for management sciences. Generative AI fintech, machine learning algorithms, and behavioral analytics can assist multi-layered payment and transaction processing screening with regard to authorized push payment, account takeover, and synthetic identity frauds, flagging suspicious activities and combating economic crimes by rigorous verification processes. Purpose of the article: We show that edge device management functionalities of cloud industrial IoT and virtual robotic simulation technologies configure plant production and route planning processes across cyber-physical production and industrial automation systems in multi-cloud immersive 3D environments, leading to tangible business outcomes by reinforcement learning and convolutional neural networks. Labor-augmenting automation and generative AI technologies can impact employment participation, increase wage and wealth inequality, and lead to potential job displacement and massive labor market disruptions. The deep learning capabilities of generative AI fintech in terms of adaptive behavioral analytics and credit scoring mechanisms can enhance financial transaction behaviors and algorithmic trading returns, identify fraudulent payment transactions swiftly, and improve financial forecasts, leading to customized investment recommendations and well-informed financial decisions. Methods: Machine learning-based study selection process and text mining systematic review management software and tools leveraged include Abstrackr, CADIMA, Colandr, DistillerSR, EPPI-Reviewer, JBI SUMARI, METAGEAR package for R, SluRp, and SWIFT-Active Screener. Such reference management systems are harnessed for methodologically rigorous evidence synthesis, study selection and characteristic extraction, predictive document classification, machine learning-based citation and record screening, bias assessment, article retrieval automation, and document classification and prioritization. Findings & value added: Industrial IoT and 3D augmented reality technologies can create business value by streamlining virtual product and remote asset management across extended reality-based navigation and robotic autonomous systems in smart factory environments by generative AI and machine learning algorithms, articulating business organizational level and theory of management implications. 3D simulation and operational modeling tools can execute and complete complex cognitive task-oriented and knowledge economy jobs, producing first-rate quality outputs swiftly while leading to unemployment spells, labor market disruptions, job displacement losses, and reduced earnings by machine learning clustering and spatial cognition algorithms. Generative AI decentralized finance, interoperable blockchain networks, cash flow management tools, and asset tokenization can mitigate fraud risks, enable digital fund and crypto investing servicing, and automate treasury operations by integrating real-time payment capabilities, routing and configurable workflows, and lending and payment technologies.

Shilpa Dhanaji Vishvas, Sarita Kumari

This chapter explores the impact of blockchain technology on consumers, delving beyond its association with cryptocurrencies. Blockchain, a distributed ledger shared across a computer network, securely stores digital data. While cryptocurrencies like Bitcoin popularized blockchain as a secure platform for transaction records, its potential extends far wider. Businesses and investors are recognizing blockchain's disruptive potential in various sectors, including finance, governance, smart contracts, the Internet of Things, and the sharing economy. However, for many consumers, blockchain remains synonymous with cryptocurrencies. These digital currencies are often seen as a subset of alternative currencies and, more specifically, digital currencies. Consulting firms predict that by 2025, blockchain will become a significant technological platform. Though research on blockchain is ongoing, with a focus in engineering and finance, few studies consider the consumer perspective. Neglecting this perspective creates an incomplete picture of such a revolutionary technology. After all, consumers are the lifeblood of the economy, making their experience crucial in understanding any business innovation.

Nida Shakeel, Prof. Shiva Prakash, Shagufta Shakeel

In traditional healthcare sceneries, real-time patient record monitoring and analyzing information for the prompt diagnosis of chronic illnesses under specific health conditions is an essential procedure. Failure to diagnose chronic diseases promptly can lead to severe consequences, including patient mortality. Regarding disease diagnosis and treatment, wearable device physiological data can be analyzed by artificial intelligence (AI) to produce intelligent recommendations. Contemporary medical and healthcare frameworks leverage Internet of Things (IoT) technologies, utilizing autonomous sensors to monitor and assess patients' health conditions while recommending suitable interventions. This paper introduces an innovative hybrid approach that integrates IoMT and Telemedicine to facilitate the early detection and ongoing monitoring of three distinct chronic diseases, including asthma, brain tumors, and Alzheimer’s. Furthermore, blockchain can enhance healthcare services by enabling decentralized data exchange, safeguarding user privacy, empowering data, and guaranteeing the dependability of data administration. Blockchain, wearable, and AI integration may improve current chronic illness management paradigms by moving away from hospital-centered care and toward patient-centered care. In this study, we further investigate the use of these integrated technologies in the management of chronic diseases and theoretically provide a patient-centric technical framework based on blockchain, artificial intelligence, and wearable technology.

Wid Alaa Jebbar, Mishall Al-Zubaidie

No abstract is available for this record.

Alessandro Vizzarri

The 6G wireless communication network will promise a high data rate and low latency more than the 5G standard. It introduces the Terahertz communications and sensing systems. Edge Cloud Computing (ECC) and AI/ML technologies are embedded and developed natively. Among the enabled use cases, the automotive and Connected and Autonomous Vehicle (CAV) sector is one of the most important. In the 6G contest where Terrestrial Networks (TN) and Non-Terrestrial Networks (NTN) are combined and a very huge amount of data is generated, a goal-oriented semantic approach can be advantageous to develop the AI/ML techniques more efficiently. The blockchain can be integrated into this scheme. It can be used not only to increase the security levels of the overall system but also to improve automation in the automotive and ITS context. The paper presents an intelligent smart contract based on goal-oriented semantic features in a 6G automotive context. The implementation through the Multichain platform is described.

Udayan Das Gupta, Md. Mokammel Haque, Ahmed Wasif Reza, Mohammad Shamsul Arefin

Delegated Proof of Stake (DPoS) is indeed a fascinating consensus algorithm used on various blockchain platforms. It was designed to address some of the scalability and energy consumption issues associated with Proof of Work (PoW) while maintaining decentralization to a certain extent. But, DPoS has some disadvantages in the distribution of profit share for stake forwarding nodes to delegate nodes. The profit share is controlled by the delegate nodes rather than the staking nodes or token holder voting nodes. Here, in Profit Demand Delegated Proof of Stake (PD-DPoS), we introduced a new mechanism to facilitate the token holder voting nodes to demand a rate of profit/profit share. PD-DPoS will encourage the delegate nodes to invest in infrastructure for high connectivity and fast computing crypto systems. As the system builds targeting fast and reliable computing by extending the opportunity of demanding profit share for token holder voter nodes will experimentally perform and prove better than the DPoS.

S.S. MIKHAIYLOVA, S.A. SABIROVA

This article presents the results of a study aimed at forecasting signals for buying and selling Bitcoin cryptocurrency using machine learning models. The conducted analysis included the study of cryptocurrency features and markets, technical analysis, development of trading strategies, application of mathematical methods based on moving averages, and building classification models for buy or sell signals. The results demonstrate the effectiveness of applying machine learning models in modern trading strategies in the cryptocurrency market.

Arun Kumar Banavara Ramaswamy, Komala Rangappa, Mahadeshwara Prasad, Shreyas Arun Kumar

The management of user data in the cloud is easily poised to become a giant issue for any business, with so much digital information floating around these days that open it up as an easy target when companies aren’t vigilant. In this paper, a novel cloud-based service is proposed that employs various advanced NLP and encryption methods with the use of blockchain. These techniques are combined to provide a solid solution to secure fundamental data such as credit card numbers, passports or any government identity cards etc. Using a hybrid NLP model, integrating Transformer Models and Named Entity Recognition (NER), to automatically catagorzing data as critical vs non-critical. Only the most important data is encrypted by a user’s cryptographic wallet before being divided into multiple chunks and stored on an exclusive cloud cluster; metadata then takes turns managing securely through blockchain to provide traceable means of retaining integrity. Smart contracts provide strict access control measures and change the cryptographic nonce if need be to prevent illegal entrance into a specific zone, thus create security. This all-inclusive strategy maintains well-known high security standards for protecting the confidentiality, availability and integrity of your sensitive data on a global scale—delivering you simple yet scalable secure world-class cloud-based data management. A proposed framework developed to fulfil the security requirements for current cloud services, which is a beneficial contribution in context of data protection and cloud Security.

Eranga Bandara, Peter Foytik, Sachin Shetty, Amin Hassanzadeh

The Metaverse is an integrated network of 3D virtual worlds accessible through a virtual reality headset. Its impact on data privacy and security is increasingly recognized as a major concern. There is a growing interest in developing a reference architecture that describes the four core aspects of its data: acquisition, storage, sharing, and interoperability. Establishing a secure data architecture is imperative to manage users' personal data and facilitate trusted AR/VR and AI/ML solutions within the Metaverse. This paper details a reference architecture empowered by Generative-AI, Blockchain, Federated Learning, and Non-Fungible Tokens (NFTs). Within this archi-tecture, various resource providers collaborate via the blockchain network. Handling personal user data and resource provider identities is executed through a Self-Sovereign Identity-enabled privacy-preserving framework. AR/NR devices in the Metaverse are represented as NFT tokens available for user purchase. Software updates and supply-chain verification for these devices are managed using a Software Bill of Materials (SBOM) and a Pipeline Bill of Materials (PBOM) verification system. Moreover, a custom-trained Llama2 LLM from Meta has been integrated to generate PBOMs for AR/NR devices' software updates, thereby preventing malware intrusions and data breaches. This Llama2-13B LLM has been quantized and fine-tuned using Qlora to ensure optimal performance on consumer-grade hardware. The provenance of AI/ML models used in the Metaverse is encapsu-lated as Model Card objects, allowing external parties to audit and verify them, thus mitigating adversarial learning attacks within these models. To the best of our knowledge, this is the very first research effort aimed at standardizing PBOM schemas and integrating Language Model algorithms for the generation of PBOMs. Additionally, a proposed mechanism facilitates different AI/ML providers in training their machine learning models using a privacy-preserving federated learning approach. Authorization of communications among AR/VR devices in the Metaverse is conducted through a Zero-Trust security-enabled rule engine. A system testbed has been implemented within a 5G environment, utilizing Ericsson new Radio with Open5GS 5G core.

Amar Johri, Umme Hani, Vasim Ahmad, Khushbu · 6 authors

The paper investigates the disruptive power of Artificial Intelligence and blockchain technology integrated into cash management. On its part, AI enables accuracy and efficiency in the form of deep data analysis, predictive analytics, and mechanization of routine tasks. At the same time, blockchain provides security, transparency, and immutability of financial transactions. To that extent, its technologies complement each other to offer added advantages such as real-time monitoring, enhanced fraud detection, and cost-cutting. Despite these huge advantages, challenges have to be faced regarding technological integration, regulatory compliance, and adoption barriers. The paper also puts forth some future trends about further developments of AI and blockchain; both technologies are already being increasingly used across a multitude of industries. This paper thus urges financial institutions to adopt new technological innovations in leading competition and improving financial operations. A strategic approach to AI and blockchain integration could enable an organization to offer optimized cash management in the short term and create a long-term foundation for success.

Iqra Hussain, Nazakat Ali, Hafiz Bilal Ahmad, Suhail Ashraf

This paper explores the volatility spillover effects between the cryptocurrency market and the Pakistan Stock Exchange (PSX). Utilising data from January 1, 2019, to April 5, 2024, sourced from Investing and Yahoo Finance, the study employs the Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) BEKK model to assess the dynamic interactions between these markets. Stationarity tests confirmed the non-stationarity of time series data at their levels, which became stationary after first differencing, ensuring robust econometric analysis. The results indicate significant volatility spillovers from major cryptocurrencies, such as Bitcoin and Ethereum, to the PSX, highlighting a solid interconnectedness between these markets. This suggests that digital asset volatility significantly influences traditional financial systems. The study concludes that integrating cryptocurrencies into global financial markets introduces risks and opportunities for investors and policymakers. The findings underscore the need for market participants to account for these volatility interactions in their risk management strategies. Additionally, policymakers must consider these interlinkages to maintain financial stability. This research contributes to the literature on financial market volatility by emphasising the importance of understanding the impact of emerging digital currencies on traditional stock markets.

Tanusree Sharma, Yujin Potter, Zachary Kilhoffer, Yun Huang · 6 authors

How AI models should deal with political topics has been discussed, but it remains challenging and requires better governance. This paper examines the governance of large language models through individual and collective deliberation, focusing on politically sensitive videos. We conducted a two-step study: interviews with 10 journalists established a baseline understanding of expert video interpretation; 114 individuals through deliberation using InclusiveAI, a platform that facilitates democratic decision-making through decentralized autonomous organization (DAO) mechanisms. Our findings reveal distinct differences in interpretative priorities: while experts emphasized emotion and narrative, the general public prioritized factual clarity, objectivity, and emotional neutrality. Furthermore, we examined how different governance mechanisms - quadratic vs. weighted voting and equal vs. 20/80 voting power - shape users' decision-making regarding AI behavior. Results indicate that voting methods significantly influence outcomes, with quadratic voting reinforcing perceptions of liberal democracy and political equality. Our study underscores the necessity of selecting appropriate governance mechanisms to better capture user perspectives and suggests decentralized AI governance as a potential way to facilitate broader public engagement in AI development, ensuring that varied perspectives meaningfully inform design decisions.