INTERPRETABILITY, EXPLAINABILITY AND CAUSALITY IN DEEP LEARNING
DOI: https://doi.org/10.65725/JCISE/1/2/001
JOURNAL OF COMPUTATIONAL INTELLIGENCE SCIENCE AND ENGINEERING (JCISE)
ISSN: 3107-8168
Volume 1 Issue 2, October 2025

Abstract
Despite revolutionizing sectors like medicine, finance, natural language processing, and autonomous technologies, the intricate and non-transparent designs of deep learning architectures restrict confidence and clarity. Existing post-hoc explanation methods (e.g., saliency maps, SHAP, and counterfactuals) yield insights that are often incomplete and inconsistent, frequently uncovering statistical correlations instead of definitive causal factors. Consequently, establishing interpretability and causality is now crucial for successfully deploying AI in critical applications. Causal inference provides a more solid framework by identifying authentic cause-and-effect relationships and boosting model reliability when data distributions change. Nevertheless, incorporating robust causal structures into neural networks is challenging due to data availability and high computational demands. This paper examines prominent interpretability and causal methodologies, discusses their drawbacks, and proposes future research avenues aimed at engineering deep learning models that are simultaneously precise, transparent, and trustworthy.

Authors: Dr.Seshaiah Merikapudi, Chinmyi T C, Amulya A, Aqsa Firdose and Deeksha S

Keywords: Deep Learning; Explainable AI (XAI); Interpretability; Explainability; Causality; Post-hoc Explanations; Causal Inference; Model Transparency; Robust AI