International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Demystifying the Black Box: A Foundational Survey and Comparative Analysis of Basic Explainable AI (XAI) Techniques |
|---|---|
| ABSTRACT | The unprecedented performance of complex Artificial Intelligence (AI) models, particularly deep neural networks (DNNs), has been accompanied by a critical opacity problem—their decision-making processes are often inscrutable "black boxes." This lack of transparency erodes trust, impedes debugging, and prevents deployment in high-stakes domains like healthcare, finance, and criminal justice, where accountability is paramount. Explainable AI (XAI) has emerged as a vital subfield dedicated to making AI systems more interpretable and understandable to human stakeholders. This research paper provides a comprehensive, pedagogical exploration of fundamental, post-hoc XAI techniques, designed to demystify the core concepts for researchers and practitioners entering the field. We systematically categorize and describe essential methods spanning feature importance techniques (e.g., Permutation Feature Importance, SHAP, LIME), example-based explanations (e.g., Counterfactual Explanations, Prototypes/Criticisms), and visualization techniques for deep learning (e.g., Saliency Maps, Grad-CAM). The study employs a structured methodology of theoretical exposition followed by a consistent empirical evaluation across three benchmark datasets (tabular, image, text) using three common model types (Random Forest, Convolutional Neural Network, BERT). We implemented and compared eight basic XAI techniques, analyzing their outputs not just on predictive accuracy but on key explainability criteria: fidelity (how well the explanation reflects the model's true reasoning), stability (consistency for similar inputs), comprehensibility (ease of human understanding), and actionability. Results revealed a fundamental trade-off: global, model-agnostic methods like SHAP provided robust, consistent feature importance scores but at high computational cost, while local, model-specific methods like Grad-CAM offered intuitive visual explanations for DNNs but were less stable under input perturbations. A critical finding was that no single technique dominated across all criteria, emphasizing the need for a portfolio approach to XAI. Furthermore, we demonstrate that even "basic" techniques, when applied correctly, can reveal model biases, identify spurious correlations, and guide model improvement. The paper concludes that foundational XAI techniques are not merely diagnostic tools but essential components for responsible AI development and deployment. Their mastery is a prerequisite for advancing towards more sophisticated, causally-grounded explanations and for fostering the necessary human-AI collaboration in critical applications. |
| AUTHOR | VEENA MORE, BHARATI H NAIKAWADI, KELLA SOWMYA, SUMITRA M MUDDA Assistant Professor, Department of BCA, A.S.Patil College of Commerce (Autonomous),Vijayapura, Karnataka, India Assistant Professor, Department of Computer Science, A.S.Patil College of Commerce (Autonomous),Vijayapura, Karnataka, India Lecturer, Department of Computer Science, Vijayanagar College, Hosapete, Karnataka, India Assistant Professor, Department of MCA, Guru Nanak Dev Engineering College, BIDAR, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312094 |
| pdf/94_Demystifying the Black Box A Foundational Survey and Comparative Analysis of Basic Explainable AI (XAI) Techniques.pdf | |
| KEYWORDS | |
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