Search Results (37)

Fairness reputation refers to the perception of others’ adherence to fair norms based on their behaviors. However, previous studies often rely on simple correlation and regression analyses without comparing cognition across roles in the ultimatum game (UG) and the dictator game (DG). Our study measured the categorical and two-dimensional cognitions (warmth-competence) of participants with different social value orientations toward proposers, responders, and dictators with varying fairness reputations. We found that proposers and dictators with fairness reputations were perceived more positively, and individualists could better distinguish between them. Regarding responders with fairness reputations, they were perceived as more fair, trustworthy, and competent, but less altruistic, cooperative, and warm. The social cognitive network of responders differed from those of proposers and dictators, with warmth cognition being central to three roles, supporting the warmth–competence model. This study highlighted the differential impact of fairness reputation in shaping social cognitions, providing insights into understanding social interactions. Full article

Federated learning (FL) is a foundational technology for enabling collaborative intelligence in vehicular edge computing (VEC). However, the volatile network topology caused by high vehicle mobility and the profound security risks of model poisoning attacks severely undermine its practical deployment. This paper introduces DTB-FL, a novel framework that synergistically integrates digital twin (DT) and blockchain technologies to establish a secure and efficient learning paradigm. DTB-FL leverages a digital twin to create a real-time virtual replica of the network, enabling a predictive, mobility-aware participant selection strategy that preemptively mitigates network instability. Concurrently, a private blockchain underpins a decentralized trust infrastructure, employing a dynamic reputation system to secure model aggregation and smart contracts to automate fair incentives. Crucially, these components are synergistic: The DT provides a stable cohort of participants, enhancing the accuracy of the blockchain’s reputation assessment, while the blockchain feeds reputation scores back to the DT to refine future selections. Extensive simulations demonstrate that DTB-FL accelerates model convergence by 43% compared to FedAvg and maintains 75% accuracy under poisoning attacks even when 40% of participants are malicious—a scenario where baseline FL methods degrade to below 40% accuracy. The framework also exhibits high resilience to network dynamics, sustaining performance at vehicle speeds up to 120 km/h. DTB-FL provides a comprehensive, cross-layer solution that transforms vehicular FL from a vulnerable theoretical model into a practical, robust, and scalable platform for next-generation intelligent transportation systems. Full article

In vehicular networks, inter-vehicle data sharing and collaborative computing improve traffic efficiency and driving experience. However, centralized processing faces challenges with privacy, communication bottlenecks, and real-time performance. This paper proposes a trust assessment mechanism for vehicular federated learning based on graph neural network (GNN) edge weight similarity. An explainable asynchronous federated learning data sharing framework is designed, consisting of permissioned asynchronous federated learning and a locally verifiable directed acyclic graph (DAG). The GNN connection weights perform reputation assessment on edge devices through DAG-based verification, while deep reinforcement learning (DRL) enables explainable node selection to improve asynchronous federated learning efficiency. The proposed explainable incentive mechanism based on GNN edge weight similarity and DAG can not only effectively prevent malicious node attacks but also improve the fairness and explainability of federated learning. Extensive experiments across different participant scales (30–200 nodes), various asynchrony degrees ($\alpha$ = 1–5), and malicious node attack scenarios (up to 50% malicious nodes) demonstrate that our method consistently outperforms state-of-the-art approaches, achieving up to 99.2% accuracy with significant improvements of 1.3–3.1% over existing trust-based federated learning methods and maintaining 95% accuracy even under severe attack conditions. The results show that the proposed scheme performs well in terms of learning accuracy and convergence speed. Full article

The rapid integration of artificial intelligence (AI) in content generation has encouraged the development of AI detection tools aimed at distinguishing between human- and AI-authored texts. These tools are increasingly adopted not only in academia but also in sensitive decision-making contexts, including candidate screening by hiring agencies in government and private sectors. This extensive reliance raises serious questions about their reliability, fairness, and appropriateness for high-stakes applications. This study evaluates the performance of six widely used AI content detection tools, namely Undetectable AI, Zerogpt.com, Zerogpt.net, Brandwell.ai, Gowinston.ai, and Crossplag, referred to as Tools A through F in this study. The assessment focused on the ability of the tools to identify human versus AI-generated content across multiple domains. Verified human-authored texts were gathered from reputable sources, including university websites, pre-ChatGPT publications in Nature and Science, government portals, and media outlets (e.g., BBC, US News). Complementary datasets of AI-generated texts were produced using ChatGPT-4o, encompassing coherent essays, nonsensical passages, and hybrid texts with grammatical errors, to test tool robustness. The results reveal significant performance limitations. The accuracy ranged from 14.3% (Tool B) to 71.4% (Tool D), with the precision and recall metrics showing inconsistent detection capabilities. The tools were also highly sensitive to minor textual modifications, where slight changes in phrasing could flip classifications between “AI-generated” and “human-authored.” Overall, the current AI detection tools lack the robustness and reliability needed for enforcing academic integrity or making employment-related decisions. The findings highlight an urgent need for more transparent, accurate, and context-aware frameworks before these tools can be responsibly incorporated into critical institutional or societal processes. Full article

This paper proposes a collaborative framework to support task offloading in connected vehicular environments. The approach relies on the dynamic formation of temporary teams of connected vehicles in a mobile edge computing scenario. A novel trust model is introduced, which integrates both quality of service and quality of results into a unified reliability score, and combines this score with distributed reputation to build a comprehensive trust metric. This trust metric is then exploited to guide a decentralized team formation algorithm, ensuring lightweight, interpretable, and scalable decision-making processes. Simulation results demonstrate that the proposed framework improves task execution quality and fairness, especially for low-performing vehicles. These contributions highlight the novelty and strengths of our collaborative model, positioning it as a promising solution for enhancing cooperation in vehicular edge systems. Full article

Federated Learning (FL) enables privacy-preserving model training across distributed clients. However, its reliance on voluntary client participation makes it vulnerable to strategic behaviors—actions that are not overtly malicious but significantly impair model convergence and fairness. Existing defense methods primarily focus on explicit attacks, overlooking the challenges posed by economically motivated “pseudo-honest” clients. To address this gap, we propose a Reputation-Aware Defense Framework to mitigate strategic behaviors in FL. This framework introduces a multi-dimensional dynamic reputation model that evaluates client behaviors based on gradient alignment, participation consistency, and update stability. The resulting reputation scores are incorporated into both aggregation and incentive mechanisms, forming a behavior-feedback loop that rewards honest participation and penalizes opportunistic strategies. We theoretically prove the convergence of reputation scores, the suppression of low-quality updates in aggregation, and the emergence of honest participation as a Nash equilibrium under the incentive mechanism. Experiments on datasets such as CIFAR-10, FEMNIST, MIMIC-III demonstrate that our approach significantly outperforms baseline methods in accuracy, fairness, and robustness, even when up to 60% of clients act strategically. This study bridges trust modeling and robust optimization in FL, offering a secure foundation for federated systems operating in open and incentive-driven environments. Full article

The logistics industry faces challenges from evolving consumer expectations, technological advances, sustainability demands, and market disruptions. Logistics collaboration is in theory perceived as one of the most promising solutions to solve these issues, but here are still a lot of challenges that needs to be better understood and addressed. While vertical collaboration among supply chain actors is well advanced, horizontal collaboration among competing service providers remains under-explored. This study developed a novel methodology based on the COM-B behaviour-change framework to better understand the main challenges, opportunities, capabilities and drivers that would motivate competing companies to exploit the potential of horizontal logistics collaboration. A survey was designed and conducted among 71 logistics service providers in Slovenia, chosen for its fragmented market and low willingness to collaborate. Statistical analysis reveals cost reduction (M = 4.21/5) and improved vehicle utilization (M = 4.29/5) as the primary motivators. On the other hand, maintaining company reputation (M = 4.64/5), fair resource sharing (M = 4.20/5), and transparency of logistics processes (M = 4.17/5) all persist as key enabling conditions. These findings underscore the pivotal role of behavioural drivers and suggest strategies that combine economic incentives with targeted trust-building measures. Future research should employ experimental designs in diverse national contexts and integrate vertical–horizontal approaches to validate causal pathways and advance theory. Full article

Bias and fairness issues in artificial intelligence (AI) algorithms are major concerns, as people do not want to use software they cannot trust. Because these issues are intrinsically subjective and context-dependent, creating trustworthy software requires human input and feedback. (1) Introduction: This work introduces an interactive method for mitigating the bias introduced by machine learning models by allowing the user to adjust bias and fairness metrics iteratively to make the model more fair in the context of undergraduate student admissions. (2) Related Work: The social implications of bias in AI systems used in education are nuanced and can affect university reputation and student retention rates motivating a need for the development of fair AI systems. (3) Methods and Dataset: Admissions data over six years from a large urban research university was used to create AI models to predict admissions decisions. These AI models were analyzed to detect biases they may carry with respect to three variables chosen to represent sensitive populations: gender, race, and first-generation college students. We then describe a method for bias mitigation that uses a combination of machine learning and user interaction. (4) Results and Discussion: We use three scenarios to demonstrate that this interactive bias mitigation approach can successfully decrease the biases towards sensitive populations. (5) Conclusion: Our approach allows the user to examine a model and then iteratively and incrementally adjust bias and fairness metrics to change the training dataset and generate a modified AI model that is more fair, according to the user’s own determination of fairness. Full article

With the rapid advancement in artificial intelligence, autonomous driving has emerged as a prominent research frontier. Autonomous vehicles rely on high-precision high-definition map data, necessitating timely map updates by map companies to accurately reflect road conditions. This paper proposes an efficient and fair map-data-sharing mechanism for vehicular networks. To encourage vehicles to share data, we introduce a reputation unit to resolve the cold-start issue for new vehicles, effectively distinguishing legitimate new vehicles from malicious attackers. Considering both the budget constraints of map companies and heterogeneous data collection capabilities of vehicles, we design a fair incentive mechanism based on the proposed reputation unit and a reverse auction algorithm, achieving an optimal balance between data quality and procurement costs. Furthermore, the scheme has been developed to facilitate mutual authentication between vehicles and Roadside Unit(RSU), thereby ensuring the security of shared data. In order to address the issue of redundant authentication in overlapping RSU coverage areas, we construct a Merkle hash tree structure using a set of anonymous certificates, enabling single-round identity verification to enhance authentication efficiency. A security analysis demonstrates the robustness of the scheme, while performance evaluations and the experimental results validate its effectiveness and practicality. Full article

Federated learning (FL) is a decentralized approach that aims to establish a global model by aggregating updates from diverse clients without sharing their local data. However, the approach becomes complicated when Byzantine clients join with arbitrary manipulation, referred to as malicious clients. Classical techniques, such as Federated Averaging (FedAvg), are insufficient to incentivize reliable clients and discourage malicious clients. Other existing Byzantine FL schemes to address malicious clients are either incentive-reliable clients or need-to-provide server-labeled data as the public validation dataset, which increase time complexity. This study introduces a federated learning framework with an evaluator-based incentive mechanism (FedEach) that offers robustness with no dependency on server-labeled data. In this framework, we introduce evaluators and participants. Unlike the existing approaches, the server selects the evaluators and participants among the clients using model-based performance evaluation criteria such as test score and reputation. Afterward, the evaluators assess and evaluate whether a participant is reliable or malicious. Subsequently, the server exclusively aggregates models from these identified reliable participants and the evaluators for global model updates. After this aggregation, the server calculates each client’s contribution, prioritizing each client’s contribution to ensure the fair recognition of high-quality updates and penalizing malicious clients based on their contributions. Empirical evidence obtained from the performance in human activity recognition (HAR) datasets highlights FedEach’s effectiveness, especially in environments with a high presence of malicious clients. In addition, FedEach maintains computational efficiency so that it is reliable for efficient FL applications such as sensor-based HAR with wearable devices and mobile sensing. Full article

Decentralized blockchains have grown into massive and Internet-scale ecosystems, collectively securing hundreds of billions of dollars in value. The complex interplay of technology and economic incentives within blockchain systems creates a delicate balance that is susceptible to significant shifts even from minor changes. This paper underscores the importance of conducting thorough, data-driven studies to monitor and understand the impacts of significant shifts in blockchain systems, particularly focusing on Ethereum’s groundbreaking builder–proposer separation (PBS) as a pivotal innovation reshaping the ecosystem. PBS revolutionizes Ethereum’s block production, entrusting builders with block construction and proposers with validation via blockchain consensus, with significant impacts on Ethereum decentralization, fairness, and security. Our empirical study reveals key insights, including the following: (a) A substantial 261% increase in proposer revenue underscores the effectiveness of PBS in promoting widespread adoption, significantly enhancing block rewards and proposer incomes. (b) The small profits garnered by builders, comprising only a 3.5% share of block rewards, raise concerns that the security assumptions based on builder reputation may introduce new threats to the system. (c) PBS promotes a more equitable distribution of resources among network participants by reducing proposer centralization and preventing centralization trends among builders and relays, thereby significantly enhancing fairness and decentralization in the Ethereum ecosystem. This study provides a comprehensive analysis of the dynamics of Ethereum PBS adoption, exploring its effects on revenue redistribution among various participants and highlighting its implications for the Ethereum ecosystem’s decentralization. Full article

Clarifying stakeholder demands and establishing an efficient quality governance system are key to geographical indication development. Current frameworks focus on government oversight, neglecting industry self-governance through associations. A four-party evolutionary game model—production organizations, governments, associations, and consumers—was developed to explore the impact of self-governance on quality. Results show association-led self-governance reduces government burdens and improves efficiency. Its success depends on government support and fair interest distribution. Additionally, the evolutionary system exhibits two optimal equilibrium points at different stages of geographical indication development. Even under relatively relaxed supervision by local governments, the governance system remains functional during the mature development phase. Lastly, a reputation mechanism incorporating consumer participation can effectively shape the decision-making processes of production organizations, while the costs associated with governance participation and complaints play a critical role in influencing consumer strategy choices. Full article

This study aimed to identify patterns of sustainability engagement based on circular economy (CE) strategy implementation, CE-oriented collaborative relationships, and environmental, social, and governance (ESG) performance, as well as to investigate whether these dimensions predict corporate reputation. Data were collected through a survey of 235 upper-level managers in the Brazilian agribusiness sector. A two-step analytical approach was applied, with cluster analysis identifying groups exhibiting distinct patterns regarding sustainability engagement (“Very Sustainable” and “Low-Sustainable”), followed by logistic regression, which singled out six key predictors among 28 variables, namely avoiding non-sustainable materials, repurposing by-products, fostering a shared CE vision, adhering to ethical guidelines, ensuring financial transparency, and fair labor practices. The final model achieved 83.4% accuracy, underscoring how an integrated approach to sustainability enhances corporate reputation. Considering its theoretical contributions, this study extends the NRBV and RV theories by demonstrating that CE strategies, CE-oriented collaborative relationships, and ESG performance strengthen pollution prevention initiatives, sustainable product development efforts, and trust among partners, among other achievements, thereby enhancing firms’ reputation and sustainable performance. Methodologically, the study integrates cluster analysis and predictive modeling to assess sustainability’s impact on reputation. From a managerial perspective, findings emphasize that corporate reputation benefits from circularity, governance integrity, and stakeholder engagement. However, the cross-sectional design, industry-specific sample, and reliance on self-reported data limit generalizability. Future research should adopt longitudinal and cross-industry approaches, examining regulatory shifts, technological advances, and evolving stakeholder demands in the sustainability–reputation nexus while incorporating external data sources to assess variations across institutional and cultural settings. Full article

Small and medium-scale enterprises (SMEs) need a platform that actively enables collaboration with research institutions and consultants as SMEs lack the financial resources to conduct independent research. Such a platform will require a verifiable manipulation-free system to enable, execute, and record collaboration activities and to track reputations among the organizations and individuals that use the platform. Blockchain provides an opportunity to build such a collaborative platform by enabling the verifiable recording of the results of the collaborations, aggregating the resulting reputation of the collaborating parties, and offering tokenized incentives to reward positive contributions to the platform. Cryptocurrencies from which blockchain tokens are derived are volatile, thereby reducing business organizations’ interest in blockchain applications. Hence, there is a need to design a self-sustaining valuable token model that incentivizes user behaviours that positively contribute to the platform. This paper explores the application of game theory in analyzing token-based economic interactions between various groups of users in an implemented blockchain-based collaboration platform to design and simulate a token distribution system that provides a fair reward mechanism for users while also providing a dynamic pricing model for the utility value provided by platform tokens. To achieve this objective, we adopted the design science research method, a running case of a blockchain collaboration platform that enables research collaboration, and extensive form games in game theory, first to analyze and simulate token outcomes of users of the collaboration platform. Secondly, the research used a logarithmic model to show the dynamic utility pricing property of the developed token model where the self-sustainability of the token is backed by the availability of an internal resource within the platform. Thirdly, we applied a qualitative approach to analyze potential risks in the designed token model and proposed risk mitigation strategies. Thus, the resulting models and their simulations, such as token distribution models and a dynamic token utility model, as well as the identified token risks and their mitigation strategies, represent the main contributions of this work. Full article

Encouraging environmentally friendly innovation in energy companies is an essential way to stop global warming. Through ingenious integration of reputation and fairness preference, this research develops an evolutionary game model between the government and energy companies. This research investigates the dynamic evolution of green innovation strategy selection by energy firms operating under government supervision, using an evolutionary game model as a basis. This study examines how government regulations, including their subsidies and penalties, reputation, and fairness preference, affect the green innovation behavior of energy enterprises. The research shows that without considering the fairness preference, the subsidy and punishment of government regulation can improve the tendency of energy enterprises to choose green innovation behavior. At the same time, considering the reputation of energy enterprises to assume social responsibility can improve the tendency of energy enterprises to choose green innovation behavior. In the case of considering fairness preference, energy companies with strong fairness preference are more likely not to adopt green innovation and need more subsidies and penalties to choose green innovation; energy enterprises with weak fairness preference are more likely to adopt green innovation; green innovation will take place with fewer subsidies and penalties; reputation plays a stronger role in energy companies with weak fairness preferences. The study can give the government a theoretical foundation on which to build precise regulatory plans for various energy firms and encourage green innovation in those enterprises. Full article