Explainability in Modern Artificial Intelligence: A Review of Techniques, Applications, and Limitations

Abstract

As the use of deep learning and complex machine learning models, whose decision-making procedures are frequently opaque, increases, Explainable Artificial Intelligence (XAI) has become a crucial field of study. Even while these models are highly predictive, their opaque nature raises fundamental questions about accountability, transparency, justice, and trust, especially in high-stakes industries like healthcare, banking, law, and autonomous systems. This essay offers a thorough analysis of Explainable Artificial Intelligence, methodically examining its basic ideas, definitions, and taxonomy. We evaluate popular approaches including LIME, SHAP, Grad-CAM, Integrated Gradients, surrogate models, and counterfactual explanations and classify XAI strategies according to model dependencies, explanation processes, and explanation scope. Additionally, we examine real-world XAI applications in a variety of fields, emphasizing interpretability requirements unique to each domain. Important issues are rigorously evaluated, such as the trade-off between interpretability and accuracy, the absence of established evaluation measures, subjective human judgment, and ethical considerations.

Introduction

The text explains the rapid rise of artificial intelligence (AI) and deep learning and highlights a major challenge: despite strong predictive performance, many models operate as “black boxes” with limited transparency. This lack of interpretability raises concerns about trust, accountability, fairness, and safety, especially in high-stakes domains like healthcare, finance, law, and autonomous systems. To address this issue, the field of Explainable AI (XAI) has emerged, focusing on making AI decisions more transparent and understandable without significantly reducing performance. However, the diversity of methods and evaluation approaches has made the field fragmented, motivating the need for structured reviews and taxonomies.

The paper provides a comprehensive overview of XAI, distinguishing between interpretability (inherent model transparency) and explainability (post-hoc explanations for complex models). It also clarifies related concepts such as transparency, trust, and accountability, and contrasts black-box models with inherently interpretable ones, highlighting the trade-off between accuracy and interpretability.

A key contribution is a taxonomy of XAI methods, which includes feature-attribution techniques (e.g., SHAP, saliency maps), surrogate models (e.g., LIME, decision trees), example-based explanations (e.g., k-NN reasoning), counterfactual explanations (what-if changes), and visualization-based methods (e.g., Grad-CAM). The paper also reviews major XAI techniques such as LIME, SHAP, gradient-based methods, surrogate models, counterfactual explanations, and inherently interpretable models, explaining their principles and limitations.

Conclusion

This study presented a comprehensive review of Explainable Artificial Intelligence (XAI), including its concepts, methods, evaluation techniques, applications, challenges, and future research directions. A structured taxonomy was introduced to categorize XAI approaches based on explanation type, model dependency, and scope of interpretation. The review showed that no single XAI method is universally suitable, as effectiveness depends on user needs and application domains. The paper also emphasized the importance of balancing interpretability, accuracy, stability, and usability. Applications in healthcare, finance, law, and autonomous systems demonstrated the significance of XAI in building trustworthy AI. Future research should focus on human-centered and real-time explainability solutions.

References

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Copyright

Copyright © 2026 Nisha Rani, Dr. Gaurav Aggarwal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.