Propagating Transparency: A Deep Dive into the Interpretability of Neural Networks

Abstract

In the rapidly evolving landscape of deep learning (DL), understanding the inner workings of neural networks remains a significant challenge. This need for transparency and accountability from DL models assumes particular importance as DL models become increasingly prevalent in decision-making processes. Interpreting these models is key to addressing this challenge. This paper offers a comprehensive overview of interpretable deep learning methods. It emphasizes gradient-based propagation techniques that shed light on the complex mechanisms driving neural network predictions. Through a systematic review, we categorize gradient-based interpretability approaches, delve into the theory of notable methods, and compare their strengths and weaknesses. Furthermore, we investigate various evaluation metrics for interpretable systems, often generalized under the term eXplainable Artificial Intelligence (XAI). We highlight their significance in assessing the faithfulness, robustness, localization, complexity, randomization, and adherence to the axiomatic principles of XAI methods. We aim to help researchers and practitioners work towards a more transparent future for artificial intelligence by providing an overview of the most recent developments in the field.