Towards Explainable AI: Neural Network-Based Training of Generalized Additive Models
Published in Royal Statistical Society International Conference (RSS 2025), 2025
Recommended citation: Ortega-Fernández, I., Sestelo, M., & Villanueva, N. M. (2025). Towards Explainable AI: Neural Network-Based Training of Generalized Additive Models. Reviewed contribution accepted for conference presentation at RSS 2025. https://rss.org.uk/training-events/conference-2025/
This work presents a methodology for training explainable neural-network models based on Generalized Additive Models (GAMs), combining the predictive power of deep learning with the interpretability of additive statistical models. The proposed framework independently trains neural networks to estimate feature contributions, providing transparent and accountable AI systems suitable for high-risk applications.
The presentation discusses theoretical foundations, optimization strategies, and practical applications of Generalized Additive Neural Networks in explainable artificial intelligence and trustworthy machine learning.