This work explores the integration of neural networks and Generalized Additive Models to develop explainable artificial intelligence methodologies that maintain high predictive performance while improving interpretability and transparency.
The proposed framework independently models feature contributions through neural-network architectures, enabling interpretable deep-learning systems suitable for trustworthy AI applications, cybersecurity, and statistical modeling tasks.