Interpreting deep learning models: Exploring advanced deep learning techniques with a focus on interpretability

Noğay H. S., Akinci T. C.

in: Understanding Models Developed with AI Including Applications with Python and MATLAB Code, Ömer Faruk Ertuğrul,Tahir Çetin Akinci and Musa Yilmaz, Editor, Morgan Kaufmann, Massachusetts, pp.267-286, 2026

  • Publication Type: Book Chapter / Chapter Vocational Book
  • Publication Date: 2026
  • Doi Number: 10.1016/b978-0-443-44163-9.00004-2
  • Publisher: Morgan Kaufmann
  • City: Massachusetts
  • Page Numbers: pp.267-286
  • Editors: Ömer Faruk Ertuğrul,Tahir Çetin Akinci and Musa Yilmaz, Editor
  • Bursa Uludag University Affiliated: Yes

Abstract

Abstract

This chapter examines the increasing importance of interpretability in deep learning, particularly in areas where decisions have critical ethical, safety, or human consequences. Although neural networks have achieved remarkable success in tasks like image classification, language modeling, and time-series prediction, the reasoning behind their outputs is often difficult to understand. This lack of transparency creates challenges in building trust and accountability, especially in sensitive domains such as healthcare and engineering.
The authors address how interpretability tools can make model predictions more accessible, allowing experts to validate results, uncover biases, and make informed decisions. Throughout the chapter, foundational deep learning models such as convolutional, recurrent, and attention-based architectures are introduced, along with hybrid approaches that combine their strengths. The text also provides a detailed look at interpretability techniques, including LIME, SHAP, Grad-CAM, and Integrated Gradients, discussing when and how they are most useful.
Beyond technical applications, the chapter emphasizes the broader value of explainable AI in supporting clinical reasoning, enhancing industrial safety, and improving material quality control. Interpretability is presented not merely as an academic challenge, but as an essential requirement for the responsible use of artificial intelligence in real-world environments.

Keywords

Attribution methods
;
Deep learning
;
Explainable AI (XAI)
;
Interpretability
;
Neural network architectures
;
Transparency in AI