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Original Article
Improving Transparency in Deep Learning Models using Explainable AI Techniques
Aquela Nawaz Qureshi1
Dr.P.Vishvapathi2
1 Assistant Professor, Department of Computer Science and Engineering, Deccan college of Engineering and Technology, Nampally, Hyderabad, Telangana, India. 2 Professor Department of Computer Science and Engineering, Deccan college of Engineering and Technology, Nampally, Hyderabad, Telangana, India.
Published Online: May-June 2026
Pages: 29-35
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260703003
References
1. S. M. Lundberg and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions,” IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 44, no. 6, pp. 3121–3133, 2022.
2. M. T. Ribeiro, S. Singh, and C. Guestrin, “Why Should I Trust You? Explaining the Predictions of Any Classifier,” IEEE Intelligent
Systems, vol. 37, no. 2, pp. 43–50, 2022.
3. Adadi and M. Berrada, “Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI),” IEEE Access, vol. 10,
pp. 123456–123478, 2022.
4. R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, and D. Pedreschi, “A Survey of Methods for Explaining Black Box Models,” IEEE
Transactions on Knowledge and Data Engineering, vol. 35, no. 4, pp. 3677–3695, 2023.
5. J. Chen, L. Song, M. Wainwright, and M. Jordan, “Learning to Explain: An Information-Theoretic Perspective on Model
Interpretation,” IEEE Transactions on Machine Learning, vol. 2, no. 1, pp. 1–15, 2023.6. F. Doshi-Velez and B. Kim, “Towards A Rigorous Science of Interpretable Machine Learning,” IEEE Transactions on Artificial
Intelligence, vol. 4, no. 2, pp. 145–159, 2023.
7. Z. C. Lipton, “The Mythos of Model Interpretability,” IEEE Computer, vol. 56, no. 3, pp. 36–43, 2023.
8. D. Molnar, G. Casalicchio, and B. Bischl, “Interpretable Machine Learning: A Guide for Making Black Box Models Explainable,”
IEEE Access, vol. 11, pp. 55678–55702, 2023.
9. K. G. Tjoa and C. Guan, “A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI,” IEEE Transactions on Neural
Networks and Learning Systems, vol. 35, no. 5, pp. 1–18, 2024.
10. Y. Zhang and Q. Yang, “Explainable Recommendation Systems: A Survey and New Perspectives,” IEEE Transactions on Knowledge
and Data Engineering, vol. 36, no. 2, pp. 789–804, 2024.
11. H. Samek, T. Wiegand, and K. Müller, “Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning
Models,” IEEE Signal Processing Magazine, vol. 41, no. 1, pp. 56–67, 2024.
12. S. Bhatt, A. Weller, and J. M. Moura, “Explainable Machine Learning in Deployment,” IEEE Transactions on Artificial Intelligence,
vol. 5, no. 1, pp. 22–35, 2024.
13. P. Linardatos, V. Papastefanopoulos, and S. Kotsiantis, “Explainable AI: A Review of Machine Learning Interpretability Methods,”
IEEE Access, vol. 12, pp. 34567–34589, 2025.
14. M. Arrieta et al., “Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges Toward Responsible
AI,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 9, no. 1, pp. 101–120, 2025.
15. R. Vilone and L. Longo, “Notions of Explainability and Evaluation Approaches for Explainable Artificial Intelligence,” IEEE
Transactions on Artificial Intelligence, vol. 6, no. 2, pp. 210–225, 2025.
Machine Intelligence, vol. 44, no. 6, pp. 3121–3133, 2022.
2. M. T. Ribeiro, S. Singh, and C. Guestrin, “Why Should I Trust You? Explaining the Predictions of Any Classifier,” IEEE Intelligent
Systems, vol. 37, no. 2, pp. 43–50, 2022.
3. Adadi and M. Berrada, “Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI),” IEEE Access, vol. 10,
pp. 123456–123478, 2022.
4. R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, and D. Pedreschi, “A Survey of Methods for Explaining Black Box Models,” IEEE
Transactions on Knowledge and Data Engineering, vol. 35, no. 4, pp. 3677–3695, 2023.
5. J. Chen, L. Song, M. Wainwright, and M. Jordan, “Learning to Explain: An Information-Theoretic Perspective on Model
Interpretation,” IEEE Transactions on Machine Learning, vol. 2, no. 1, pp. 1–15, 2023.6. F. Doshi-Velez and B. Kim, “Towards A Rigorous Science of Interpretable Machine Learning,” IEEE Transactions on Artificial
Intelligence, vol. 4, no. 2, pp. 145–159, 2023.
7. Z. C. Lipton, “The Mythos of Model Interpretability,” IEEE Computer, vol. 56, no. 3, pp. 36–43, 2023.
8. D. Molnar, G. Casalicchio, and B. Bischl, “Interpretable Machine Learning: A Guide for Making Black Box Models Explainable,”
IEEE Access, vol. 11, pp. 55678–55702, 2023.
9. K. G. Tjoa and C. Guan, “A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI,” IEEE Transactions on Neural
Networks and Learning Systems, vol. 35, no. 5, pp. 1–18, 2024.
10. Y. Zhang and Q. Yang, “Explainable Recommendation Systems: A Survey and New Perspectives,” IEEE Transactions on Knowledge
and Data Engineering, vol. 36, no. 2, pp. 789–804, 2024.
11. H. Samek, T. Wiegand, and K. Müller, “Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning
Models,” IEEE Signal Processing Magazine, vol. 41, no. 1, pp. 56–67, 2024.
12. S. Bhatt, A. Weller, and J. M. Moura, “Explainable Machine Learning in Deployment,” IEEE Transactions on Artificial Intelligence,
vol. 5, no. 1, pp. 22–35, 2024.
13. P. Linardatos, V. Papastefanopoulos, and S. Kotsiantis, “Explainable AI: A Review of Machine Learning Interpretability Methods,”
IEEE Access, vol. 12, pp. 34567–34589, 2025.
14. M. Arrieta et al., “Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges Toward Responsible
AI,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 9, no. 1, pp. 101–120, 2025.
15. R. Vilone and L. Longo, “Notions of Explainability and Evaluation Approaches for Explainable Artificial Intelligence,” IEEE
Transactions on Artificial Intelligence, vol. 6, no. 2, pp. 210–225, 2025.
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