Explainable AI-Driven Synthetic Data Generation

Abstract

This paper explores the application of explainable artificial intelligence (XAI) techniques to the generation of synthetic data. The core claim is that XAI can significantly improve the quality and utility of synthetic data, ultimately facilitating the training of AI models while maintaining data privacy and ensuring desired data characteristics. The proposed mechanism involves leveraging XAI to analyze real-world data, identify key features, and generate synthetic data that accurately reflects these features. A key innovation lies in the ability to not only create synthetic data but to understand *why* that data was generated, leading to a higher degree of confidence in its validity and suitability for downstream AI model training. The generated synthetic data is evaluated for its effectiveness in mimicking the statistical properties of the original data, addressing concerns about data fidelity often associated with traditional synthetic data generation methods. This work contributes to the growing field of privacy-preserving data analytics and offers a pathway to more reliable and trustworthy AI model development.