WebSHAP: Towards Explaining Any Machine Learning Models Anywhere
This addresses the need for transparent and explainable ML in web applications, particularly for users like loan applicants, though it is incremental as it adapts an existing method to a new environment.
The authors tackled the lack of client-side explainability for web-based machine learning by developing WebSHAP, an in-browser tool that adapts SHAP to enable transparent ML explanations without backend servers, demonstrated in a loan approval scenario.
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.