Explainable Artificial Intelligence for Deep Synthetic Data Generation Models
Luís Valina, Brígida Teixeira, Arsénio Reis, Zita Vale, Tiago de Oliveira Pinto · 2024
Artificial intelligence encapsulates a "black box" of undiscovered knowledge, propelling the exploration of Explainable Artificial Intelligence (XAI) in generative data synthesis and deep learning. Focused on unveiling these "black box" areas, pointed into interpretability and validation in synthetic data generation, shedding light on the intricacies of generative processes. XAI techniques illuminate decision-making in complex algorithms, enhancing transparency and fostering a comprehensive understanding of non-linear relationships. Addressing the complexity of explaining deep learning models, this paper proposes an XAI solution for deep synthetic data generation explanation. The model integrates a clustering approach to identify similar training instances, reducing interpretation time for large datasets. Explanations, available in various formats, are tailored to diverse user profiles through integration with language models, generating texts with different technical detail levels. This research contributes to ethically deploying AI, bridging the gap between advanced model complexities and human interpretability in the dynamic landscape of artificial intelligence.