Explainable AI in Deep Neural Networks: Bridging the Gap between Performance and Interpretability

Abstract

DNNs have successfully revolutionized several disciplines, including the recognition of images, natural language processing, and autonomous decision-making systems. These complex architectures, however, also come with multiple parameters that complicate the models, causing many issues regarding trust, accountability, and regulatory compliance. XAI seeks to reduce these problems by clarifying the DNNs decision-making process while ensuring that this increased transparency does not compromise predictive accuracy.

This paper addresses the role of explainability in AI and risks associated with black-box models in high-stakes applications such as healthcare and finance. The paper distinguishes between intrinsic and post-hoc interpretability methods, describing the merits and demerits of each category. Intrinsic methods relate to the design of inherently interpretable models from scratch. Techniques for such design include attention mechanisms, self-explaining architectures, and prototype-based learning. The post-hoc methods rather try to employ pre-trained models and the technique is feature attribution, saliency maps, rule extraction, and concept-based interpretation that helps provide meaningful explanation through the extraction end.

This challenge remains in the delicate balance between performance and interpretability: more transparent models often compromise with predictive power. Researchers explore ways in which one may improve transparency and efficiency with hybrid models, regularization techniques, human-in-the-loop systems, or other methods. The future directions include new architectures that explain, causality-based explanations, automated explanation systems, and evaluation frameworks.

The current paper establishes a critical role that explainability assumes in the promotion of AI while underlining the fact that there must be a hand-in-glove relationship of transparency with performance for AI to be widely trusted and adopted within high-stakes decision-making domains.