ENHANCING TRUST IN MACHINE LEARNING INTERPRETABLE MODELS THROUGH EXPLAINABLE AI TECHNIQUES

Authors

  • Dr. KISHOR KUMAR GAJULA

Keywords:

Black Box Models, Explainable Artificial Intelligence (XAI), Model Transparency, Interpretability, Model-Agnostic Methods, Feature Importance, Ethical AI, Trustworthy AI

Abstract

Trust and transparency are of the utmost importance in light of the evergrowing influence of machine learning (ML) systems on autonomous systems, healthcare,and financial decisions. Classic black-box models are frequently precise; however, they fail to provide an explanation for the methodology

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References

Linardatos, P., Papadimitriou, K., & Kotsiantis, S. (2020). A review of machine learning

interpretability methods. Artificial Intelligence Review, 53(5), 1–44.

Akula, A. R., Wang, K., & Liu, C. (2021). CX-ToM: Enhancing human understanding

and trust in image recognition models with counterfactual explanations. IEEE

Transactions on Neural Networks and Learning Systems, 32(11), 4780–4793.

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Published

2025-09-15

How to Cite

Dr. KISHOR KUMAR GAJULA. (2025). ENHANCING TRUST IN MACHINE LEARNING INTERPRETABLE MODELS THROUGH EXPLAINABLE AI TECHNIQUES. Pegem Journal of Education and Instruction, 13(4), 909–915. Retrieved from https://www.pegegog.net/index.php/pegegog/article/view/4305

Issue

Vol. 13 No. 4 (2023): PEGEGOG

Section

Article

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