Regulatory initiatives on algorithmic accountability now oblige organisations to reveal decision logic,data provenance,and safety controls,yet the same systems embody proprietary assets whose exposure can nullify comp...Regulatory initiatives on algorithmic accountability now oblige organisations to reveal decision logic,data provenance,and safety controls,yet the same systems embody proprietary assets whose exposure can nullify competitive advantage.This study engineers and empirically validates a governance framework that reconciles those apparently contradictory imperatives.A three-tier disclosure architecture is combined with lattice-based cryptographic watermarking and policy-driven smart-contract gates,then exercised in two high-stakes domains:a regulated credit-scoring engine and a 1.1-billion-parameter text-to-video generator.Across 18,000 simulated disclosure transactions,the framework achieves a statutory-coverage score of 0.927±0.018 while suppressing parameter-exfiltration entropy to 1.37 bits·kg^(-1),a 63.8%reduction relative to a full-disclosure baseline.Median inference latency rises only 3.6 ms,and predictive accuracy remains statistically unchanged(ΔAUC=0.0007,p=0.746;ΔFID=0.09,p=0.532).Sensitivity analyses confirm that compliance quality varies by less than 2.4%under±20%weight perturbations,evidencing robustness.Findings demonstrate that calibrated transparency and vigorous IP protection are jointly attainable,providing quantitative benchmarks for emerging legislation and standardisation efforts.展开更多
Risk-based governance has become the dominant regulatory paradigm for artificial intelligence,yet existing scholarship largely treats risk categories as neutral assessments of technological harm that precede regulator...Risk-based governance has become the dominant regulatory paradigm for artificial intelligence,yet existing scholarship largely treats risk categories as neutral assessments of technological harm that precede regulatory intervention.This article challenges that assumption by arguing that risk categories function less as assessments of harm than as social architectures—institutional boundary-making devices that reorganize innovation capacity,compliance burdens,and inequality across jurisdictions and organizational types.Drawing on Beck’s risk society thesis,Jasanoff’s co-production framework,and Gieryn’s concept of boundary work,the article develops a diagnostic framework—Risk as Social Architecture—and formalizes a causal loop mechanism linking category definition,innovation channeling,compliance burden,perception lock-in,and inequality reproduction.A Cobb–Douglas formalization and three testable propositions operationalize the framework’s core variables.Applying this framework to three jurisdictional cases—the European Union’s AI Act(2021–2024),China’s Algorithmic Governance Provisions(2022–2023),and Singapore’s Model AI Governance Framework—demonstrates that identical AI systems receive fundamentally different risk classifications,with variance explained by political negotiation rather than technical properties.The article contributes to governance scholarship by reframing risk classification as a primary site of institutional power rather than a secondary policy tool.More broadly,it demonstrates how governance through categories reshapes inequality not only in artificial intelligence but across regulatory domains—including biotechnology,finance,and climate risk—wherever classification systems mediate access to markets,legitimacy,and innovation.展开更多
The rapid evolution of international trade necessitates the adoption of intelligent digital solutions to enhance trade facilitation.The Single Window System(SWS)has emerged as a key mechanism for streamlining trade do...The rapid evolution of international trade necessitates the adoption of intelligent digital solutions to enhance trade facilitation.The Single Window System(SWS)has emerged as a key mechanism for streamlining trade documentation,customs clearance,and regulatory compliance.However,traditional SWS implementations face challenges such as data fragmentation,inefficient processing,and limited real-time intelligence.This study proposes a computational social science framework that integrates artificial intelligence(AI),machine learning,network analytics,and blockchain to optimize SWS operations.By employing predictive modeling,agentbased simulations,and algorithmic governance,this research demonstrates how computational methodologies improve trade efficiency,enhance regulatory compliance,and reduce transaction costs.Empirical case studies on AI-driven customs clearance,blockchain-enabled trade transparency,and network-based trade policy simulation illustrate the practical applications of these techniques.The study concludes that interdisciplinary collaboration and algorithmic governance are essential for advancing digital trade facilitation,ensuring resilience,transparency,and adaptability in global trade ecosystems.展开更多
Artificial intelligence(AI)technology is profoundly reshaping the global management ecosystem,transforming its role from a tool for efficiency to a structural force driving organizational change.This study,grounded in...Artificial intelligence(AI)technology is profoundly reshaping the global management ecosystem,transforming its role from a tool for efficiency to a structural force driving organizational change.This study,grounded in the context of China's modernization,systematically explores the multidimensional applications of AI technology in management research and the challenges it faces.The study finds core challenges in the current management field,including a crisis of adaptability between the industrial-era paradigm and the intelligent ecosystem,the dissipation of governance effectiveness caused by algorithmic black boxes,and cognitive barriers to human-machine collaboration.These issues stem from the conflict between mechanistic cognition and complex systems,the imbalance between instrumental and value rationality,and the paradigmatic differences between biological and machine intelligence.To address these challenges,the study proposes three solutions:building an AI-enabled distributed dynamic knowledge network,establishing a hierarchical and transparent governance system,and developing cognitive coupling interfaces.This research not only provides new perspectives for innovation in management theory but also offers practical paths for AI management practice in the Chinese context.展开更多
Amid the rapid evolution of the digital economy,big data technologies are reshaping the foundations of traditional credit reporting by expanding data sources,refining modeling methods,and enhancing risk response capac...Amid the rapid evolution of the digital economy,big data technologies are reshaping the foundations of traditional credit reporting by expanding data sources,refining modeling methods,and enhancing risk response capacity.From the integrated perspective of the“technology–institution–ethics”triad,this paper systematically reviews 33 studies published between 2012 and 2025,supplemented by representative case analyses.The review follows PRISMA 2020 guidelines,covering both international literature and China-specific practices.The analysis shows that while big data enables more dynamic,precise,and intelligent credit evaluation,it also generates systemic risks,including privacy infringement,algorithmic bias,model opacity,and regulatory lag.To address these dilemmas,a comprehensive governance framework is proposed that combines explainable artificial intelligence,privacy-preserving computation,cross-sector regulatory coordination,and ethical algorithmic norms.The study acknowledges its limitations as a review-based work—particularly in terms of proprietary data accessibility,interpretability of complex models,and empirical cross-platform validation-and suggests future research directions involving realworld experimentation,interpretable deep models,and multi-institutional governance mechanisms.Overall,this research aims to provide theoretical foundations and policy insights for building an open,transparent,and sustainable digital credit ecosystem.展开更多
文摘Regulatory initiatives on algorithmic accountability now oblige organisations to reveal decision logic,data provenance,and safety controls,yet the same systems embody proprietary assets whose exposure can nullify competitive advantage.This study engineers and empirically validates a governance framework that reconciles those apparently contradictory imperatives.A three-tier disclosure architecture is combined with lattice-based cryptographic watermarking and policy-driven smart-contract gates,then exercised in two high-stakes domains:a regulated credit-scoring engine and a 1.1-billion-parameter text-to-video generator.Across 18,000 simulated disclosure transactions,the framework achieves a statutory-coverage score of 0.927±0.018 while suppressing parameter-exfiltration entropy to 1.37 bits·kg^(-1),a 63.8%reduction relative to a full-disclosure baseline.Median inference latency rises only 3.6 ms,and predictive accuracy remains statistically unchanged(ΔAUC=0.0007,p=0.746;ΔFID=0.09,p=0.532).Sensitivity analyses confirm that compliance quality varies by less than 2.4%under±20%weight perturbations,evidencing robustness.Findings demonstrate that calibrated transparency and vigorous IP protection are jointly attainable,providing quantitative benchmarks for emerging legislation and standardisation efforts.
文摘Risk-based governance has become the dominant regulatory paradigm for artificial intelligence,yet existing scholarship largely treats risk categories as neutral assessments of technological harm that precede regulatory intervention.This article challenges that assumption by arguing that risk categories function less as assessments of harm than as social architectures—institutional boundary-making devices that reorganize innovation capacity,compliance burdens,and inequality across jurisdictions and organizational types.Drawing on Beck’s risk society thesis,Jasanoff’s co-production framework,and Gieryn’s concept of boundary work,the article develops a diagnostic framework—Risk as Social Architecture—and formalizes a causal loop mechanism linking category definition,innovation channeling,compliance burden,perception lock-in,and inequality reproduction.A Cobb–Douglas formalization and three testable propositions operationalize the framework’s core variables.Applying this framework to three jurisdictional cases—the European Union’s AI Act(2021–2024),China’s Algorithmic Governance Provisions(2022–2023),and Singapore’s Model AI Governance Framework—demonstrates that identical AI systems receive fundamentally different risk classifications,with variance explained by political negotiation rather than technical properties.The article contributes to governance scholarship by reframing risk classification as a primary site of institutional power rather than a secondary policy tool.More broadly,it demonstrates how governance through categories reshapes inequality not only in artificial intelligence but across regulatory domains—including biotechnology,finance,and climate risk—wherever classification systems mediate access to markets,legitimacy,and innovation.
文摘The rapid evolution of international trade necessitates the adoption of intelligent digital solutions to enhance trade facilitation.The Single Window System(SWS)has emerged as a key mechanism for streamlining trade documentation,customs clearance,and regulatory compliance.However,traditional SWS implementations face challenges such as data fragmentation,inefficient processing,and limited real-time intelligence.This study proposes a computational social science framework that integrates artificial intelligence(AI),machine learning,network analytics,and blockchain to optimize SWS operations.By employing predictive modeling,agentbased simulations,and algorithmic governance,this research demonstrates how computational methodologies improve trade efficiency,enhance regulatory compliance,and reduce transaction costs.Empirical case studies on AI-driven customs clearance,blockchain-enabled trade transparency,and network-based trade policy simulation illustrate the practical applications of these techniques.The study concludes that interdisciplinary collaboration and algorithmic governance are essential for advancing digital trade facilitation,ensuring resilience,transparency,and adaptability in global trade ecosystems.
基金the key achievement of the 2025 Guangxi Higher Education Undergraduate Teaching Reform Project"Research on the Characteristic Iterative Practice of'Four-Chain Integration'in AI Modeling and Decision-Making for Economics and Management Courses in Guangxi Universities under New Quality Productivity"(Project No.:2025JGB455).
文摘Artificial intelligence(AI)technology is profoundly reshaping the global management ecosystem,transforming its role from a tool for efficiency to a structural force driving organizational change.This study,grounded in the context of China's modernization,systematically explores the multidimensional applications of AI technology in management research and the challenges it faces.The study finds core challenges in the current management field,including a crisis of adaptability between the industrial-era paradigm and the intelligent ecosystem,the dissipation of governance effectiveness caused by algorithmic black boxes,and cognitive barriers to human-machine collaboration.These issues stem from the conflict between mechanistic cognition and complex systems,the imbalance between instrumental and value rationality,and the paradigmatic differences between biological and machine intelligence.To address these challenges,the study proposes three solutions:building an AI-enabled distributed dynamic knowledge network,establishing a hierarchical and transparent governance system,and developing cognitive coupling interfaces.This research not only provides new perspectives for innovation in management theory but also offers practical paths for AI management practice in the Chinese context.
基金funded by 2025 College Student Innovation and Entrepreneurship Training Program Project(No.:202513988003)。
文摘Amid the rapid evolution of the digital economy,big data technologies are reshaping the foundations of traditional credit reporting by expanding data sources,refining modeling methods,and enhancing risk response capacity.From the integrated perspective of the“technology–institution–ethics”triad,this paper systematically reviews 33 studies published between 2012 and 2025,supplemented by representative case analyses.The review follows PRISMA 2020 guidelines,covering both international literature and China-specific practices.The analysis shows that while big data enables more dynamic,precise,and intelligent credit evaluation,it also generates systemic risks,including privacy infringement,algorithmic bias,model opacity,and regulatory lag.To address these dilemmas,a comprehensive governance framework is proposed that combines explainable artificial intelligence,privacy-preserving computation,cross-sector regulatory coordination,and ethical algorithmic norms.The study acknowledges its limitations as a review-based work—particularly in terms of proprietary data accessibility,interpretability of complex models,and empirical cross-platform validation-and suggests future research directions involving realworld experimentation,interpretable deep models,and multi-institutional governance mechanisms.Overall,this research aims to provide theoretical foundations and policy insights for building an open,transparent,and sustainable digital credit ecosystem.
