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VALUE ORIENTATIONS AND INSTITUTIONAL CONSTRUCTION OF ARTIFICIAL INTELLIGENCE SYNTHETIC DATA GOVERNANCE IN CHINA 认领 引用
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作者 Zhang Zesong 《China Legal Science》 2026年第4期56-84,共29页
As an innovative solution,synthetic data provides a secure and efficient alternative data source for AI training by algorithmically simulating real data's statistical characteristics and distribution patterns.It r... As an innovative solution,synthetic data provides a secure and efficient alternative data source for AI training by algorithmically simulating real data's statistical characteristics and distribution patterns.It reduces data collection costs,mitigates privacy leakage risks,and resolves copyright issues,gradually becoming a key driver of AI development.However,widespread use also brings complex governance challenges.During generation and use,sensitive personal information may leak,and the opacity of the process may exacerbate the algorithmic'black box problem,making decisions difficult to trace and explain.Meanwhile,large technology enterprises,leveraging first-mover advantages in data and technology,may further consolidate market dominance through synthetic data,triggering new forms of data monopoly.Low-quality or biased synthetic data may also cause models to disconnect from reality,leading to algorithmic discrimination or even system collapse.Facing these challenges,China urgently needs a governance system adapted to synthetic data.Future governance should take standardization,transparency,socialization,and high quality as core values,guiding institutional design that balances innovation and risk prevention by enhancing privacy protection,breaking algorithmic'black boxes',preventing data monopolies,and avoiding data distortion.On this basis,synthetic data can be incorporated into data protection law frameworks with more relaxed standards,while relying on compliance audits open-source software,and quality control to build a forward-looking governance system with Chinese characteristics. 展开更多
关键词 governance privacy protection algorithmic transparency data source synthetic data data monopoly quality control algorithmically simulating real datas
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Explainable Credit Intelligence:A Unified SHAP-Based Framework for Interpretable Risk Scoring across Corporate and Retail Lending Domains 认领 引用
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作者 Omoshola Owolabi 《Journal of Data Analysis and Information Processing》 2025年第4期520-545,共26页
This study proposes a dual-architecture Explainable Artificial Intelligence(XAI)framework designed to unify risk scoring methodologies across corporate and retail lending domains.The framework leverages wavelet-based ... This study proposes a dual-architecture Explainable Artificial Intelligence(XAI)framework designed to unify risk scoring methodologies across corporate and retail lending domains.The framework leverages wavelet-based decomposition to extract multi-resolution features from corporate cash flow time series,while employing Bidirectional Long Short-Term Memory(Bi-LSTM)autoencoders to generate latent representations of retail transaction behaviors.These heter-ogeneous representations are integrated via a novel interpretability mechanism,CrossSHAP,which enables cross-domain attribution analysis and consistent ex-planation of model outputs.The proposed system is further distinguished by its alignment with regulatory standards,incorporating automated mappings into Basel III Pillar 3 disclosures and Equal Credit Opportunity Act(ECOA)adverse action codes to support regulatory transparency and compliance.To facilitate model validation and fairness assessments,the framework also incorporates a synthetic data generation module that preserves high-order financial depend-encies and inter-variable dynamics.Comprehensive evaluation following the SAFE ML paradigm demonstrates robust performance in all aspects of safety,accountability,fairness,and ethics.The proposed architecture contributes to the advancement of interpretable machine learning in financial risk modeling by enabling robust,transparent,and regulation-aware credit decisioning across diverse borrower segments. 展开更多
关键词 Explainable AI Credit Risk Assessment Financial Technology Credit Intelligence Machine Learning Interpretability Risk Management Algorithmic Transparency
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