AI-native 6G networks promise to transform the telecom industry by enabling dynamic resource allocation, predictive maintenance, and ultra-reliable low-latency communications across all layers, which are essential for applications such as smart cities, autonomous vehicles, and immersive XR. However, the deployment of 6G systems results in severe data scarcity, hindering the training of efficient AI models. Synthetic data generation is extensively used to fill this gap; however, it introduces challenges related to dataset bias, auditability, and compliance with regulatory frameworks. In this regard, we propose the Synthetic Data Generation with Ethics Audit Loop (SEAL) framework, which extends baseline modular pipelines with an Ethical and Regulatory Compliance by Design (ERCD) module and a Federated Learning (FL) feedback system. The ERCD integrates fairness, bias detection, and standardized audit trails for regulatory mapping, while the FL enables privacy-preserving calibration using aggregated insights from real testbeds to close the reality-simulation gap. Results show that the SEAL framework outperforms existing methods in terms of Frechet Inception Distance, equalized odds, and accuracy. These results validate the framework's ability to generate auditable and bias-mitigated synthetic data for responsible AI-native 6G development.

翻译：AI原生6G网络通过实现跨所有层的动态资源分配、预测性维护及超可靠低延迟通信，有望变革电信行业，这对于智慧城市、自动驾驶和沉浸式扩展现实（XR）等应用至关重要。然而，6G系统的部署导致严重的数据稀缺问题，阻碍了高效AI模型的训练。合成数据生成技术被广泛用于填补这一空白，但由此引入了数据集偏差、可审计性和法规合规性等挑战。为此，我们提出基于伦理审计环路的合成数据生成框架（SEAL），该框架在基础模块化流水线上扩展了伦理与合规性设计（ERCD）模块及联邦学习（FL）反馈系统。ERCD集成了公平性、偏差检测及面向法规映射的标准化审计追踪功能，而FL则利用真实测试平台的聚合洞察进行隐私保护校准，以弥合现实-仿真差距。实验结果表明，SEAL框架在弗雷歇初始距离、等概率奇偶性和准确率指标上均优于现有方法。这些结果验证了该框架能够生成可审计且偏差缓解的合成数据，为负责任的AI原生6G开发提供支撑。