Explainable AI (XAI): A Survey of Techniques for Transparent and Trustworthy Machine Learning

Abstract

Explainable Artificial Intelligence (XAI) addresses the opacity of complex machine learning models, ensuring transparency, trust, and accountability in critical applications. This survey reviews XAI techniques, categorized into model- agnostic and model-specific approaches, alongside tools, frame- works, stakeholder perspectives, and emerging technologies. It explores their theoretical foundations, practical applications in healthcare, finance, autonomous systems, legal systems, edu- cation, cybersecurity, smart cities, robotics, agriculture, IoT systems, human-AI collaboration, ethical AI, and environmental monitoring, and recent case studies (20232025). The paper examinesevaluationmetrics,frameworks,ethicalconsiderations, standardization efforts, implementation challenges, and future directions,emphasizingthebalancebetweenperformanceandin- terpretability.Bysynthesizingadvancementsandidentifyingopen problems,thisworkservesasavitalresourceforresearchersand practitioners advancing trustworthy AI systems at institutions like PES University.

Introduction

Machine Learning (ML) has revolutionized various industries, but complex models like deep neural networks often operate as black boxes, raising issues of trust, fairness, and regulatory compliance, especially in critical fields such as healthcare, finance, and smart cities. Explainable Artificial Intelligence (XAI) aims to make ML models interpretable, ensuring transparency while maintaining performance.

XAI is driven by the need for trust, fairness, and compliance with regulations like GDPR, enabling stakeholders—including end-users, developers, regulators, and policymakers—to understand and validate AI decisions. For example, XAI has improved diagnostic accuracy in healthcare and enhanced crop yield predictions in agriculture.

The text surveys XAI techniques, categorized into model-agnostic methods (like LIME, SHAP, Anchors) and model-specific methods (like decision trees, attention mechanisms, Grad-CAM). It provides a comparative analysis of these methods based on faithfulness, runtime, and user comprehension, noting trade-offs between accuracy and computational cost.

Open-source XAI tools such as SHAP, LIME, Captum, and AIX360 support practical applications across diverse domains including healthcare, finance, autonomous systems, education, cybersecurity, and smart cities.

Stakeholders have different needs, from intuitive explanations for end-users to compliance needs for regulators and fairness concerns for ethicists. Thirteen recent case studies demonstrate XAI’s positive impact on trust, fairness, accuracy, and efficiency.

Evaluation of XAI involves metrics like faithfulness, stability, comprehensibility, user satisfaction, and computational efficiency. Ethical considerations include fairness, privacy, accountability, and risks like overtrust or adversarial attacks. Standardization efforts (e.g., ISO/IEC 24029-2) and challenges such as integration, accuracy-interpretability trade-offs, and user overload are also discussed.

Future directions highlight the evolution of hybrid XAI models combining intrinsic and post-hoc explanations and the development of standardized evaluation benchmarks to advance the field.

Conclusion

XAI is essential for transparent, trustworthy AI.This survey reviews techniques, tools, applications, case studies, evaluation frameworks, ethical consid- erations,standardization,implementationchallenges, andfuturedirections.Byaddressingapplieddomains like agriculture and innovative areas like IoT, XAI enhances trust. Future research should focus on hy- brid models, standardized evaluation, and scalable solutions,aligningwithIJRASETandJETIRsscope.

References

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Copyright

Copyright © 2025 Kadirisani Neha. 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.