A Novel Methodology to Enhance Explainability in AI Systems for Trust and Transparency Through Brain MRI Data

Explainable AI (XAI) stands as a crucial facet within artificial intelligence systems, facilitating comprehension of their functioning. This work explores novel methodologies aimed at enhancing the explanatory capabilities of AI systems, leveraging advanced machine learning algorithms. Existing XAI methods often struggle to produce accurate results due to inherent limitations, thereby complicating trust in AI systems. To address this challenge, this research introduces a new approach that describes the initial parameters and workings of the model, facilitating a deeper understanding of how predictions are generated. Through the utilization of brain MRI data, both custom and pre-trained models are trained, elucidating their feature extraction, and learning processes. By visualizing the model's workings, our approach aims to provide insights into how it interprets data to make predictions, ultimately fostering trust and transparency in AI systems.