Self Explaining Neural Network

Self-explaining neural networks (SENNs) aim to overcome the "black box" nature of deep learning models by providing readily interpretable explanations alongside predictions. Current research focuses on developing SENN architectures that generate faithful and robust explanations, often incorporating techniques like prototype-based reasoning, contrastive learning, and probabilistic modeling to improve both accuracy and explainability. These advancements are crucial for building trust in AI systems across diverse applications, from medical diagnosis (e.g., tuberculosis screening) to risk assessment (e.g., survival analysis), where understanding the model's reasoning is paramount for responsible deployment. The development of reliable methods for evaluating the faithfulness of these explanations remains an active area of investigation.