SEAL: An Open, Auditable, and Fair Data Generation Framework for AI-Native 6G Networks

Synthetic data is necessary but risky for 6G AI systems: The paper identifies a critical challenge in developing AI for next-generation networks—there isn't enough real data available to train AI models effectively. While synthetic (artificially generated) data fills this gap, it can introduce hidden biases and fairness problems that aren't immediately obvious.

The SEAL framework embeds ethics and compliance from the start: Rather than treating fairness and regulatory compliance as afterthoughts, SEAL integrates an "Ethical and Regulatory Compliance by Design" module directly into the data generation process. This includes built-in bias detection and standardized audit trails, making it easier for organizations to demonstrate they're following regulations and ethical guidelines.

Federated learning helps bridge the gap between simulations and reality: The framework uses a privacy-preserving feedback system that learns from real-world testbeds without centralizing sensitive data. This helps ensure that synthetic data remains realistic and representative, reducing the risk that AI models trained on synthetic data will fail when deployed in actual networks.

Measurable improvements in fairness and performance: The paper reports that SEAL outperforms existing methods on multiple metrics, including equalized odds (a fairness measure ensuring AI decisions don't discriminate across groups) and accuracy, suggesting that responsible AI design doesn't require sacrificing performance.

Auditability enables accountability: By maintaining transparent audit trails throughout the data generation process, SEAL makes it possible for regulators, auditors, and stakeholders to understand how synthetic data was created and verify that ethical standards were met—a key requirement for trustworthy AI deployment.