Home // EXPLAINABILITY 2024, The First International Conference on Systems Explainability // View article
Authors:
Holger Ziekow
Peter Schanbacher
Valentin Göttisheim
Keywords: Explainable AI; Machine Learning; Neural Networks; Shapley value approximation.
Abstract:
This paper addresses the problem of providing fast and accurate approximations of Shapley values for neural networks by embedding the approximation directly into the network architecture. The approach is tested on a synthetic and a real world dataset. The results demonstrate that integrating Shapley value approximations into the loss function enables making a trade-off between explainability and prediction accuracy, optimizing both aspects. This method yields accurate approximations while improving the model's explainability, making it more stable and easier to explain in practical applications.
Pages: 5 to 10
Copyright: Copyright (c) IARIA, 2024
Publication date: November 17, 2024
Published in: conference
ISBN: 978-1-68558-215-9
Location: Valencia, Spain
Dates: from November 17, 2024 to November 21, 2024