Research Bibliography

Key academic papers, regulatory documents, and industry references on synthetic data, AI governance, EU AI Act compliance, and AI certification.

Synthetic Data Generation

1.
Xu, L., Skoularidou, M., Cuesta-Infante, A., & Veeramachaneni, K. (2019). Modeling Tabular Data using Conditional GAN. Advances in Neural Information Processing Systems (NeurIPS 2019).
Introduces CTGAN — the most widely adopted method for tabular synthetic data generation.
2.
Jordon, J., Yoon, J., & van der Schaar, M. (2019). PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees. International Conference on Learning Representations (ICLR 2019).
Foundational work combining GANs with differential privacy for synthetic data.
3.
Assefa, S., Dervovic, D., Mahfouz, A., Tillman, R., Reddy, P., & Veloso, M. (2020). Generating Realistic Stock Market Order Streams. AAAI 2020.
Demonstrates synthetic data for financial time series applications.
4.
Fonseca, J., & Bação, F. (2023). Tabular and Relational Data Generation by GANs: A Survey. arXiv preprint.
Comprehensive survey of GAN architectures for structured data generation.

1.
Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006). Calibrating Noise to Sensitivity in Private Data Analysis. Theory of Cryptography Conference (TCC 2006).
The foundational paper defining differential privacy.
2.
Abadi, M., et al. (2016). Deep Learning with Differential Privacy. ACM SIGSAC Conference on Computer and Communications Security (CCS 2016).
DP-SGD — the standard algorithm for training neural networks with differential privacy.

1.
Doshi-Velez, F., & Kim, B. (2017). Towards a Rigorous Science of Interpretable Machine Learning. arXiv preprint.
Foundational framework for evaluating AI explainability and interpretability.
2.
Mitchell, M., et al. (2019). Model Cards for Model Reporting. ACM Conference on Fairness, Accountability, and Transparency (FAccT 2019).
Introduces model cards — now a baseline for model documentation and governance.
3.
Raji, I. D., et al. (2020). Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing. ACM FAccT 2020.
Defines internal audit frameworks for AI systems — relevant to decision logging and audit trails.
4.
Gebru, T., et al. (2021). Datasheets for Datasets. Communications of the ACM.
The standard reference for dataset documentation and provenance disclosure.

1.
European Parliament and Council of the European Union (2024). Regulation (EU) 2024/1689 on Artificial Intelligence (AI Act). Official Journal of the European Union.
The full text of the EU AI Act as enacted. Articles 9–16 cover high-risk AI obligations.
2.
High-Level Expert Group on Artificial Intelligence (AI HLEG) (2019). Ethics Guidelines for Trustworthy AI. European Commission.
Pre-Act framework that shaped the EU AI Act's trustworthiness requirements.
3.
Veale, M., & Zuiderveen Borgesius, F. (2021). Demystifying the Draft EU Artificial Intelligence Act. Computer Law Review International.
Accessible academic analysis of the AI Act's structure and key obligations.

1.
Huang, X., et al. (2020). A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability. Computer Science Review.
Broad survey of verification methodologies for neural networks.
2.
ISO/IEC (2023). ISO/IEC 42001:2023 — Artificial Intelligence Management System. International Organization for Standardization.
The ISO standard for AI management systems — establishes certification requirements for AI governance.

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