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Ensuring AI Compliance through Synthetic Data Innovation

In the rapidly changing landscape of artificial intelligence (AI), ensuring compliance with regulatory frameworks is critical. Synthetic data is emerging as a vital tool, offering significant advantages for model validation and regulatory compliance.
Einat Aviv
July 14, 2024
In the rapidly changing landscape of artificial intelligence (AI), ensuring compliance with regulatory frameworks is critical. Synthetic data is emerging as a vital tool, offering significant advantages for model validation and regulatory compliance.
On May 21, the EU Council formally adopted the EU AI Act. The EU AI Act is designed to ensure AI systems are safe, transparent, and respect fundamental rights, imposing strict requirements for data privacy, bias mitigation, and fairness, similar to regulatory requirements imposed on the banking industry. Traditional data sources often fall short in meeting these demands, making synthetic data increasingly important.
Synthetic data is artificially created to replicate the statistical properties of real-world data without including actual personal information. This characteristic makes it an excellent tool for model validation and regulatory compliance. By eliminating the risk of data breaches and privacy violations, synthetic data enhances privacy and security, a key focus of the EU AI Act.
Traditional datasets can introduce bias, leading to unfair AI outcomes. Synthetic data allows for the creation of balanced datasets, addressing biases and promoting fairness. This ensures models are tested against diverse and unbiased scenarios, aligning with the EU AI Act's fairness standards.
Synthetic data is versatile and scalable, making it ideal for robust model validation. It can be generated in large quantities and tailored to specific needs, allowing for extensive testing under various scenarios, thereby enhancing the reliability and robustness of AI models.
At Proov.ai, we are utilizing our proprietary synthetic data to streamline model validation processes. For example, our fairness dashboard and automated model validation documentation leverage synthetic data to ensure compliance with regulatory standards.
By embracing synthetic data, companies can improve the reliability and fairness of their AI models, ensuring compliance and building trust with users and regulators alike. Exploring industry resources and case studies can provide valuable insights into how synthetic data can transform model validation processes and help achieve compliance with the EU AI Act.