Enhancing Transparency and Interpretability in Deep Learning Models: A Comprehensive Study on Explainable AI Techniques

Enhancing Transparency and Interpretability in Deep Learning Models: A Comprehensive Study on Explainable AI Techniques

Dr.Shashank Singh1, Dr. Dhirendra Pratap Singh2, Mr.Kaushal Chandra3

1Professor and Proctor, Department of Computer Science and Engineering, S R Institute of Management and Technology, BakshiKaTalab, Affiliated to AKTU, Lucknow, Uttar Pradesh. 226201.  shashankjssit@gmail.com

2 Professors and Director, S R Institute of Management and Technology, BakshiKaTalab, Affiliated to AKTU, Lucknow, Uttar Pradesh. 226201.  dirsrgi485@gmail.com

3Director Corporate Relations, S R Institute of Management and Technology, BakshiKaTalab, Affiliated to AKTU, Lucknow, Uttar Pradesh.226201.   kaushalkamann@gmail.com

Abstract: Deep learning models have demonstrated remarkable capabilities across various domains, but their inherent complexity often leads to challenges in understanding and interpreting their decisions. The demand for transparent and interpretable artificial intelligence (AI) systems is particularly crucial in fields such as healthcare, finance, and autonomous systems. This research paper presents a comprehensive study on the application of Explainable AI (XAI) techniques to enhance transparency and interpretability in deep learning models.

Keywords: Explainable AI (XAI), artificial intelligence (AI).