EXPLAINABLE AI: NEW APPROACHES TO INTERPRETABILITY OF DEEP NEURAL NETWORKS

Abstract

The relevance of this research is determined by the need to enhance the interpretability of deep learning models to ensure transparency and user trust in critical domains such as healthcare, finance, and autonomous systems. Despite the high performance achieved by deep neural networks, their «black-box» nature remains a significant obstacle to their widespread adoption. Increasing regulatory requirements and societal interest in the ethics of artificial intelligence underscore the need to develop explainable AI solutions.This study aims to analyse current methods of deep neural network interpretability, identify their key limitations, and propose recommendations to improve the efficiency of model applications in real-world settings. A systematic approach was applied, encompassing literature review, comparative analysis of interpretation methods, and evaluation of their effectiveness in practical tasks. The study used theoretical and empirical methods to comprehensively address the issue.The impact of interpretability on user trust has been examined in critical domains such as healthcare, where AI decision explanations facilitate diagnostic decision-making, and finance, where transparency reduces conflicts between clients and organisations. Model-agnostic approaches (e.g., SHAP, LIME), attention mechanisms, and rule-based explanations were identified as key tools for achieving interpretability. It was found that the main challenges include high computational costs, difficulty in adapting explanations for non-specialists, and risks associated with data confidentiality.Recommendations were developed, including the integration of hybrid interpretation methods, adaptation of models to specific industry requirements, implementation of interpretability monitoring systems, and the creation of user-friendly explanations. It was demonstrated that such an approach facilitates the effective implementation of deep learning models in practical systems while maintaining their accuracy.The prospects for further research lie in the development of new interpretation tools tailored to industry-specific needs and the creation of standards for assessing the quality of explanations. This will promote the integration of explainable AI into practical applications and ensure increased user trust in these technologies.