THEORETICAL AND PRACTICAL LIMITS OF EXPLAINABILITY IN DEEP NEURAL NETWORK DECISION PROCESSES
Keywords:
Deep Neural Networks, Explainability, Interpretability, Transparency, Accountability, Trust in AI, Black-box Models, XAI, Ethical AISynopsis
Deep neural networks (DNNs) have achieved unprecedented performance across domains, yet their opaque nature raises critical challenges for interpretability, accountability, and trust. This paper explores the theoretical and practical limits of explainability in DNN decision processes. It highlights inherent constraints due to model complexity, non-linearity, and information-theoretic bounds. Practical limitations, such as scalability, computational cost, and mismatched user expectations, are critically evaluated. We classify methods into post-hoc explainability and inherently interpretable models, and analyze their trade-offs between accuracy and transparency. The paper proposes a layered framework to balance performance with ethical and regulatory requirements. Diagrams, mind maps, and comparative tables illustrate how interpretability interacts with trust, ethics, and real-world deployment.
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