WebSHAP: Towards Explaining Any Machine Learning Models Anywhere
Abstract
As machine learning (ML) is increasingly integrated into our everyday Web experience, there is a call for transparent and explainable web-based ML. However, existing explainability techniques often require dedicated backend servers, which limit their usefulness as the Web community moves toward in-browser ML for lower latency and greater privacy. To address the pressing need for a client-side explainability solution, we present WebSHAP, the first in-browser tool that adapts the state-of-the-art model-agnostic explainability technique SHAP to the Web environment. Our open-source tool is developed with modern Web technologies such as WebGL that leverage client-side hardware capabilities and make it easy to integrate into existing Web ML applications. We demonstrate WebSHAP in a usage scenario of explaining ML-based loan approval decisions to loan applicants. Reflecting on our work, we discuss the opportunities and challenges for future research on transparent Web ML. WebSHAP is available at https://github.com/poloclub/webshap.
Cite
@article{arxiv.2303.09545,
title = {WebSHAP: Towards Explaining Any Machine Learning Models Anywhere},
author = {Zijie J. Wang and Duen Horng Chau},
journal= {arXiv preprint arXiv:2303.09545},
year = {2023}
}
Comments
5 pages, 4 figures. Accepted at the ACM Web Conference 2023 (WWW 2023). For a live demo, visit https://poloclub.github.io/webshap/. Code is open-source at https://github.com/poloclub/webshap