Enhancing Transparency in Data Analytics Through Explainable Artificial Intelligence

Authors

  • Guruprasad Selvarajan, Sivakumar Ramakrishnan,Paras Doshi, Rasik Borkar

Keywords:

Explainable AI, Data Analytics, Machine Learning Interpretability, Transparency in AI, Ethical Decision-Making, Feature Importance Analysis

Abstract

Explainable Artificial Intelligence (XAI) is transforming data analytics by meeting the increasing need for openness, trust, and accountability in AI-generated insights. This research analyzes the function of XAI in rendering machine learning models comprehensible and accessible to decision-makers in various sectors.

References

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?" Explaining the predictions of any classifier.

Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions.

Caruana, R., Lou, Y., Gehrke, J., et al. (2015). Intelligible models for healthcare: Predicting pneumonia risk.

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Published

2025-03-10

How to Cite

Guruprasad Selvarajan, Sivakumar Ramakrishnan,Paras Doshi, Rasik Borkar. (2025). Enhancing Transparency in Data Analytics Through Explainable Artificial Intelligence . Journal of Computational Analysis and Applications (JoCAAA), 34(3), 8–21. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2073

Issue

Vol. 34 No. 3 (2025): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.