AI compliance and bias mitigation with synthetic data

As Italy’s largest distribution network, Poste Italiane was exploring synthetic data to enhance AI development and data management. With challenges such as data scarcity, imbalance, and privacy concerns, the company aims to adopt a secure, scalable approach that ensures fairness, transparency, and compliance with AI regulations.

To address this, a synthetic data sandbox is being developed, a controlled environment where AI models can be trained and tested on privacy-safe, high-fidelity datasets. This solution enables risk-free experimentation, improves model performance, and accelerates innovation while maintaining data security and regulatory compliance.

The challenge

Poste Italiane faced data limitations, including scarcity, imbalance, and sensitivity concerns, while needing to comply with upcoming AI regulations emphasizing fairness and explainability. The challenge was to preserve data value while ensuring privacy protection and regulatory compliance.

The solution

To address these challenges, we developed a synthetic data sandbox, a secure, controlled environment where AI models can be trained and tested using high-fidelity synthetic datasets. These datasets retain statistical properties of real data while eliminating privacy risks, allowing teams to experiment freely without exposing sensitive information. The solution also aligns with the AI Act recommendations on data quality and model interpretability.

The result

With the synthetic data sandbox, Poste Italiane now has a safe space for AI experimentation. Teams can enhance model training with statistically valid synthetic data, improving performance and accuracy. The reduced reliance on real data mitigates privacy risks, ensuring compliance with EU regulations on data protection and model fairness. This framework also fosters faster innovation, enabling the exploration of new AI applications without compromising security or compliance. By integrating synthetic data, Poste Italiane is embracing a future-proof approach to scalable, privacy-conscious AI development.

This collaboration highlights the transformative potential of synthetic data in modern AI projects, demonstrating how enterprises can innovate responsibly and efficiently with scalable, privacy-preserving solutions.