Enhancing The Explainability of Neural Network: In the aspects of feature importance, domain rules and algorithmic transparency

Arulprakash, Enoch; Aruldoss, Martin

ISBN 10: 6208064341 / ISBN 13: 9786208064341

Published by LAP LAMBERT Academic Publishing, 2024

About this Item

PRINT ON DEMAND.

Seller Inventory # 18403967165

Bibliographic Details

• Title: Enhancing The Explainability of Neural Network: In the aspects of feature importance, domain rules and algorithmic transparency
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2024
• Language: English
• ISBN 10: 6208064341
• ISBN 13: 9786208064341
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Artificial Intelligence (AI) driven by neural networks is crucial in many applications like recommendation systems, language translation, social media, chatbots, and spell-checking etc. However, these networks are often criticized for being "black boxes," raising concerns about their explainability, especially in sensitive domains like healthcare, autonomous driving etc. Existing methods to enhance explainability, such as feature importance, often lack clarity and interpretability.To address this, the Object-Oriented Neural Network for Improved Explainability (OONNIE) was developed. OONNIE uses object-oriented modeling to combine loss and connection weight for computing feature importance and integrates domain-specific rules through OOP's extendability. The model emphasizes algorithmic transparency by detailing every training step. Evaluated on XOR and XNOR functions, OONNIE shows promising results in feature importance, faster loss reduction, and improved predictions after integrating domain rules. This marks a significant contribution to explainable AI, making OONNIE a valuable tool for developing trustworthy AI systems.